Efficacy of different protein descriptors in predicting protein functional families

  • Serene AK Ong1,

    Affiliated with

    • Hong Huang Lin1,

      Affiliated with

      • Yu Zong Chen1,

        Affiliated with

        • Ze Rong Li2 and

          Affiliated with

          • Zhiwei Cao3Email author

            Affiliated with

            BMC Bioinformatics20078:300

            DOI: 10.1186/1471-2105-8-300

            Received: 01 November 2006

            Accepted: 17 August 2007

            Published: 17 August 2007

            Abstract

            Background

            Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families.

            Results

            The performance of these descriptor-sets were ranked by Matthews correlation coefficient (MCC), and categorized into two groups based on their performance. While there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. The combination-sets tend to give slightly but consistently higher MCC values and thus overall best performance such that three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets.

            Conclusion

            Our study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors.

            Background

            Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein structural and functional classes [15], protein-protein interactions [69], subcellular locations [1016], peptides containing specific properties[17, 18], microarray data [19] and protein secondary structure prediction [20]. These descriptors serve to represent and distinguish proteins or peptides of different structural, functional and interaction profiles by exploring their distinguished features in compositions, correlations, and distributions of the constituent amino acids and their structural and physicochemical properties [2, 8, 21, 22]. There is thus a need to comparatively evaluate the effectiveness of these descriptor-sets for predicting different functional problems by using the same machine learning method and parameter optimization algorithm. Moreover, it is of interest to examine whether combined use of these descriptor-sets help to improve predictive performance.

            This work is intended to evaluate the effectiveness of a total of six individual descriptor-sets and four combination-sets (Table 1) in the prediction of several protein functional families by using support vector machine (SVM). Six sets of individual descriptors and three combination-sets have been separately utilized in machine learning prediction of different protein functional and structural properties, all of which have shown impressive predictive performances [2224]. The six individual sets are amino acid compositions [23] (Set D1), dipeptide compositions [24] (Set D2), normalized Moreau-Broto autocorrelation [25, 26] (Set D3), Moran autocorrelation [27] (Set D4), Geary autocorrelation [28] (Set D5), and the composition, transition and distribution of structural and physicochemical properties [26, 8, 17, 29, 30] (Set D6). The three combination-sets are quasi sequence order formed by weighted sums of amino acid compositions and physicochemical coupling correlations [10, 11, 18, 31] (Set D7), pseudo amino acid composition (PseAA) formed by weighted sums of amino acid compositions and physicochemical square correlations [23, 32] (Set D8), and combination of amino acid compositions and dipeptide compositions (Set D9) [24, 33]. In this work, we also considered a fourth combination-set that combines descriptor-sets D1 through D8 (Set D10).
            Table 1:

            Protein descriptors commonly used for predicting protein functional families.

            Sets

            Descriptor-sets

            No. of descriptors (properties)

            No. of components

            Type

            Physicochemical properties

            Refs

            D1

            Amino acid composition

            1

            20

            Sequence composition

             

            [23]

            D2

            Dipeptide composition

            1

            400

            Sequence composition

             

            [24]

            D3

            Normalized Moreau – Broto autocorrelation

            8

            240

            Correlation of physicochemical properties

            Hydrophobicity scale, average flexibility index, polarizability parameter, free energy of amino acid solution in water, residue accessible surface area, amino acid residue volume, steric parameters, relative mutability

            [25, 26]

            D4

            Moran autocorrelation

            8

            240

            Correlation of physicochemical properties

            Hydrophobicity scale, average flexibility index, polarizability parameter, free energy of amino acid solution in water, residue accessible surface area, amino acid residue volume, steric parameters, relative mutability

            [27]

            D5

            Geary autocorrelation

            8

            240

            Square correlation of physicochemical properties

            Hydrophobicity scale, average flexibility index, polarizability parameter, free energy of amino acid solution in water, residue accessible surface area, amino acid residue volume, steric parameters, relative mutability

            [28]

            D6

            Descriptors of composition, transition and distribution

            21

            147

            Distribution and variation of physicochemical properties

            Hydrophobicity, Van der Waals volume, polarity, polarizability, charge, secondary structures, solvent accessibility

            [2-6, 8, 17, 29, 30]

            D7

            Quasi sequence order

            4

            160

            Combination of sequence composition and correlation of physicochemical

            Hydrophobicity, hydrophilicity, polarity, side-chain volume

            [10, 11, 18, 31]

            D8

            Pseudo amino acid composition

            3

            298

            Combination of sequence composition and square correlation of physicochemical

            Hydrophobicity, hydrophilicity, side chain mass

            [23, 32]

            D9

            Combination of amino acid and dipeptide composition

            2

            420

            Combination of sequence compositions

              

            D10

            Combination of all eight sets of descriptors

            54

            1745

            Combination of all sets

              

            The protein functional families studied here include enzyme EC2.4 [3437], G protein-coupled receptors [3840], transporter TC8.A [41], chlorophyll [42], lipid synthesis proteins involved in lipid synthesis [43], and rRNA-binding proteins. These six protein families were selected for testing the descriptor-sets based on their functional diversity, sample size and the range of reported family member prediction accuracies [2]. The reported prediction accuracies for these families are generally lower than those of other families [3], which are ideal for critically evaluating the effectiveness of these descriptor-sets; having a lower accuracy should enable a better differentiation of the performance of the various classes. SVM was used as the machine learning method for predicting these functional families because it is a popular method that has consistently been shown better performances than other machine learning methods [44, 45]. As this work is intended as a benchmarking study of the performance of various classes of descriptors, other than automatic optimization of results that is an integral part of the SVM programs, such as sigma value scanning, no further attempt was made to optimize the prediction performance of any descriptor class or of any dataset by manually tuning the parameters. Hence, prediction results reported in this paper might differ from those of reported studies.

            EC2.4 includes glycosyltransferases that catalyze the synthesis of glycoconjugates and are involved in post-translational modification of proteins (glycosylation). Increased levels of glycosyltransferases have been found in disease states and inflammation [46, 47]. TC8.A consists of auxiliary transport proteins that facilitate transport across membranes, which play regulatory and structural roles [48]. GPCR represents G-protein coupled receptors that transduct signals for inducing cellular responses, and members of GPCR are of great pharmacological importance, as 50–60% of approved drugs elicit their therapeutic effect by selectively addressing members of the GPCR family [4952]. Chlorophyll proteins are essential for harvesting solar energy in photosynthetic antenna systems [53]. Lipid synthesis proteins play central roles in such processes as metabolism, and deficiencies or altered functioning of lipid binding proteins are associated with disease states such as obesity, diabetes, atherosclerosis, hyperlipidemia and insulin resistance [54]. rRNA-binding proteins play central roles in the post-transcriptional regulation of gene expression [55, 56], and their binding capabilities are mediated by certain RNA binding domains and motifs [5760].

            Results and Discussion

            The statistics of the six datasets are given in Table 2. Training and prediction statistics for each of the studied descriptor-sets are given in Table 3. Independent validation datasets were used to test the prediction accuracies. Among the 5-fold cross-validation test, independent dataset test and jackknife test, the jackknife is deemed the most rigorous [61]; however, it would have taken a lot of time to use SVM to conduct the jackknife test, thus as a compromise, here we adopted the independent dataset test. The program CDHIT [6264] was used to remove redundancy at both 90% and 70% sequence identity so to avoid bias, subsequently, the datasets are tested again with the independent evaluation sets and the statistics are given in Table 4. It should be emphasized that the performance evaluation for the studied descriptor-sets are based only on the datasets studied in this work and the conclusions from this study might not be readily extended to other datasets.
            Table 2:

            Summary of datasets statistics, including size of training, testing and independent evaluation sets, and average sequence length.

             

            Total

            Training

            Testing

            Independent testing

            Average sequence size

             

            P

            N

            P

            N

            P

            N

            P

            N

             

            EC2.4

            3304

            14373

            1382

            5068

            1022

            5859

            900

            3446

            460

            GPCR

            2819

            21515

            1580

            7389

            717

            7333

            522

            6793

            498

            TC8.A

            229

            23096

            94

            7962

            72

            7962

            63

            7172

            483

            Chlorophyll

            999

            22997

            356

            7928

            333

            7928

            310

            7141

            480

            Lipid

            2192

            11537

            850

            5779

            707

            4483

            635

            1275

            312

            rRNA

            5855

            13770

            2004

            5246

            1940

            4953

            1911

            3571

            376

            Table 3:

            Dataset training statistics and prediction accuracies of six protein functional families. DS refers to descriptor set, where D1 = amino acid composition; D2 = dipeptide composition; D3 = Moreau-Broto autocorrelation; D4 = Moran autocorrelation; D5 = Geary autocorrelation; D6 = composition, transition and distribution descriptors; D7 = quasi sequence order; D8 = pseudo amino acid composition; D9 = combination of D1+D2; and D10 = combination of D1-D8. Predicted results given as TP (true positive), FN (false negative), TN (true negative), FP (false positive), Sen (sensitivity), Spec (specificity), Q (overall accuracy) and MCC (Matthews correlation coefficient).

            Protein family

            Des-criptor set

            Training set

            Testing set

            Independent evaluation set

              
              

            P

            N

            P

            N

            P

            N

            Q(%)

            MCC

                

            TP

            FN

            TN

            FP

            TP

            FN

            Sen(%)

            TN

            FP

            Spec(%)

              

            EC2.4

            D1

            1249

            2120

            1154

            1

            9065

            12

            724

            176

            80.4

            3244

            202

            94.1

            91.3

            0.74

             

            D2

            1319

            2120

            1080

            5

            8806

            1

            646

            154

            82.9

            3349

            97

            97.2

            94.1

            0.80

             

            D3

            1105

            1756

            1295

            4

            9166

            5

            768

            132

            85.3

            3394

            52

            98.5

            95.8

            0.87

             

            D4

            1239

            2221

            1161

            4

            8701

            5

            756

            144

            84.0

            3365

            81

            97.7

            94.8

            0.84

             

            D5

            1242

            2223

            1160

            2

            8690

            14

            753

            147

            83.6

            3391

            55

            98.4

            95.4

            0.85

             

            D6

            1214

            2077

            1145

            45

            8846

            4

            741

            159

            82.3

            3383

            63

            98.2

            94.9

            0.84

             

            D7

            1293

            2624

            1072

            39

            8295

            8

            696

            204

            77.3

            3270

            176

            94.9

            91.3

            0.73

             

            D8

            1226

            3008

            1177

            1

            7918

            1

            794

            106

            88.2

            3387

            59

            98.3

            96.2

            0.88

             

            D9

            1275

            2747

            1129

            0

            8177

            3

            782

            118

            86.9

            3367

            79

            97.7

            95.5

            0.86

             

            D10

            1228

            3254

            1176

            0

            7672

            1

            798

            102

            88.7

            3397

            49

            98.6

            96.5

            0.89

            GPCR

            D1

            1590

            7458

            1847

            1

            14166

            3

            505

            17

            96.7

            6735

            58

            99.1

            99.0

            0.93

             

            D2

            564

            711

            1728

            3

            14121

            5

            510

            12

            97.7

            6737

            56

            99.2

            99.1

            0.93

             

            D3

            1169

            4628

            1122

            4

            10208

            1

            507

            15

            97.1

            6737

            56

            99.2

            99.0

            0.93

             

            D4

            1257

            4474

            1037

            1

            10363

            0

            499

            23

            95.6

            6745

            48

            99.3

            99.0

            0.93

             

            D5

            1290

            4724

            997

            8

            10113

            0

            494

            28

            94.6

            6734

            59

            99.1

            98.8

            0.91

             

            D6

            757

            2060

            1536

            2

            12777

            0

            503

            19

            96.3

            6742

            51

            99.2

            99.0

            0.93

             

            D7

            812

            2950

            1482

            1

            11887

            0

            495

            27

            94.8

            6696

            97

            98.6

            98.3

            0.88

             

            D8

            653

            2171

            1644

            0

            12550

            1

            501

            21

            96.0

            6769

            24

            99.7

            99.4

            0.95

             

            D9

            1590

            7458

            693

            12

            7322

            57

            512

            10

            98.1

            6735

            58

            99.1

            99.1

            0.93

             

            D10

            672

            2454

            1625

            0

            12268

            0

            502

            20

            96.2

            6757

            36

            99.5

            99.2

            0.94

            TC8.A

            D1

            118

            2858

            49

            0

            13121

            0

            36

            27

            57.1

            1843

            2

            99.9

            98.5

            0.73

             

            D2

            116

            1100

            50

            0

            14824

            0

            41

            22

            65.1

            1843

            2

            99.9

            98.7

            0.78

             

            D3

            94

            7962

            53

            0

            14501

            0

            42

            21

            66.7

            1842

            3

            98.6

            98.7

            0.78

             

            D4

            94

            7962

            47

            0

            11250

            0

            37

            26

            58.7

            1843

            2

            99.9

            98.5

            0.74

             

            D5

            94

            7962

            47

            0

            11137

            0

            37

            26

            58.7

            1843

            2

            99.9

            98.5

            0.74

             

            D6

            94

            7962

            64

            0

            15283

            0

            44

            19

            69.8

            1843

            2

            99.9

            98.9

            0.81

             

            D7

            94

            7962

            59

            0

            15045

            0

            43

            20

            68.3

            1843

            2

            99.9

            98.9

            0.80

             

            D8

            103

            943

            63

            0

            14981

            0

            48

            15

            76.2

            1843

            2

            99.9

            99.1

            0.85

             

            D9

            114

            810

            52

            0

            15114

            0

            41

            22

            65.1

            1843

            2

            99.9

            98.7

            0.78

             

            D10

            102

            1068

            64

            0

            14856

            0

            48

            15

            76.2

            1843

            2

            99.9

            99.1

            0.85

            Chlorophyll

            D1

            356

            7928

            166

            0

            14297

            0

            182

            128

            58.7

            1587

            11

            99.3

            92.7

            0.71

             

            D2

            4S40

            934

            248

            1

            7927

            1

            228

            82

            73.6

            1595

            3

            99.8

            95.6

            0.83

             

            D3

            425

            603

            264

            0

            15253

            0

            246

            64

            79.4

            1594

            4

            99.8

            96.4

            0.86

             

            D4

            415

            574

            273

            1

            15282

            0

            247

            65

            79.7

            1597

            1

            99.9

            96.6

            0.87

             

            D5

            429

            615

            259

            1

            15240

            1

            233

            77

            75.2

            1597

            1

            99.9

            95.9

            0.84

             

            D6

            482

            946

            202

            5

            14910

            0

            205

            105

            66.1

            1597

            1

            99.9

            94.4

            0.79

             

            D7

            394

            3337

            210

            85

            12517

            2

            178

            132

            57.4

            1597

            1

            99.9

            93.0

            0.73

             

            D8

            371

            1421

            317

            1

            14435

            0

            255

            55

            82.3

            1593

            5

            99.7

            96.9

            0.88

             

            D9

            399

            1273

            289

            1

            14582

            1

            249

            61

            80.3

            1591

            7

            99.6

            96.4

            0.86

             

            D10

            381

            1753

            307

            1

            14102

            1

            251

            59

            81.0

            1594

            4

            99.8

            96.7

            0.88

            Lipid synthesis

            D1

            849

            2026

            705

            3

            8229

            7

            470

            165

            74.0

            1218

            57

            95.5

            88.4

            0.73

             

            D2

            927

            2037

            629

            1

            8225

            0

            512

            123

            80.6

            1259

            16

            98.6

            92.7

            0.84

             

            D3

            898

            2968

            659

            0

            7294

            0

            509

            126

            80.2

            1271

            4

            99.7

            93.2

            0.84

             

            D4

            968

            3227

            588

            1

            7035

            0

            493

            142

            77.6

            1273

            2

            99.8

            92.5

            0.83

             

            D5

            970

            3280

            586

            1

            6982

            0

            491

            144

            77.3

            1260

            15

            98.8

            91.7

            0.81

             

            D6

            874

            2112

            681

            2

            8149

            1

            525

            110

            82.7

            1268

            7

            99.5

            93.9

            0.86

             

            D7

            863

            2415

            692

            2

            7845

            2

            512

            123

            80.6

            1271

            4

            99.7

            93.4

            0.85

             

            D8

            907

            1608

            615

            0

            4488

            0

            498

            137

            78.4

            1268

            7

            99.5

            92.5

            0.83

             

            D9

            815

            1613

            740

            2

            8638

            11

            525

            110

            82.7

            1248

            27

            97.9

            92.8

            0.84

             

            D10

            865

            1640

            657

            0

            4456

            0

            531

            104

            83.6

            1268

            7

            99.5

            94.2

            0.87

            rRNA binding

            D1

            548

            579

            3390

            6

            9598

            22

            1824

            87

            95.5

            3511

            60

            98.3

            97.3

            0.94

             

            D2

            1133

            1225

            2811

            0

            8974

            0

            1844

            67

            96.5

            3519

            52

            98.5

            97.8

            0.95

             

            D3

            1126

            1638

            2816

            2

            8560

            1

            1812

            99

            94.8

            3535

            36

            99.0

            97.5

            0.95

             

            D4

            1337

            1958

            2697

            0

            8241

            0

            1783

            128

            93.3

            3484

            87

            97.6

            96.1

            0.91

             

            D5

            1372

            1976

            2572

            0

            8223

            0

            1784

            127

            93.4

            3479

            92

            97.4

            96.0

            0.91

             

            D6

            921

            1208

            2971

            52

            8991

            0

            1824

            87

            95.5

            3541

            30

            99.2

            97.9

            0.95

             

            D7

            878

            2743

            3040

            26

            7442

            14

            1808

            103

            97.9

            3481

            90

            97.5

            96.5

            0.92

             

            D8

            810

            2245

            3143

            0

            7954

            0

            1849

            62

            96.8

            3541

            30

            99.2

            98.3

            0.96

             

            D9

            810

            972

            3075

            3

            9182

            2

            1848

            63

            96.7

            3526

            45

            98.7

            98.0

            0.96

             

            D10

            900

            2600

            3044

            0

            7599

            0

            1858

            53

            97.2

            3547

            24

            99.3

            98.6

            0.97

            Table 4:

            Dataset statistics and prediction accuracies after homologous sequences removal (HSR) at 90% and 70% identity. DS refers to descriptor set, where D1 = amino acid composition; D2 = dipeptide composition; D3 = Moreau-Broto autocorrelation; D4 = Moran autocorrelation; D5 = Geary autocorrelation; D6 = composition, transition and distribution descriptors; D7 = quasi sequence order; D8 = pseudo amino acid composition; D9 = combination of D1+D2; and D10 = combination of D1-D8. Predicted results given as TP (true positive), FN (false negative), TN (true negative), FP (false positive), Sen (sensitivity), Spec (specificity), Q (overall accuracy) and MCC (Matthews correlation coefficient).

               

            Independent evaluation set

            Protein family

            % HSR

            DS

            P

            N

            Q (%)

            MCC

               

            TP

            FN

            Sen(%)

            TN

            FP

            Spec(%)

              

            EC2.4

            90

            D1

            552

            250

            68.8

            3235

            201

            94.2

            89.4

            0.65

              

            D2

            626

            176

            78.1

            3339

            97

            97.2

            93.6

            0.78

              

            D3

            609

            193

            75.9

            3384

            52

            98.5

            94.2

            0.80

              

            D4

            603

            199

            75.2

            3355

            81

            97.6

            93.4

            0.78

              

            D5

            591

            211

            73.7

            3381

            55

            98.4

            93.7

            0.79

              

            D6

            501

            301

            62.5

            3374

            62

            98.2

            91.4

            0.70

              

            D7

            545

            257

            68.0

            3261

            175

            94.9

            89.8

            0.66

              

            D8

            666

            136

            83.0

            3375

            61

            98.2

            95.4

            0.84

              

            D9

            630

            172

            78.6

            3357

            79

            97.7

            94.1

            0.80

              

            D10

            670

            132

            83.5

            3388

            48

            98.6

            95.8

            0.86

             

            70

            D1

            459

            223

            67.3

            3193

            199

            94.1

            89.6

            0.62

              

            D2

            516

            166

            75.7

            3296

            96

            97.2

            93.6

            0.76

              

            D3

            503

            179

            73.8

            3341

            51

            98.5

            94.4

            0.78

              

            D4

            495

            187

            72.6

            3311

            81

            97.6

            93.4

            0.75

              

            D5

            484

            198

            71.0

            3339

            53

            98.4

            93.8

            0.77

              

            D6

            399

            283

            58.5

            3330

            62

            98.2

            91.5

            0.67

              

            D7

            452

            230

            66.3

            3218

            174

            94.9

            90.1

            0.63

              

            D8

            551

            131

            80.8

            3331

            61

            98.2

            95.3

            0.83

              

            D9

            520

            162

            76.3

            3314

            78

            97.7

            94.1

            0.78

              

            D10

            554

            128

            81.2

            3344

            48

            98.6

            95.7

            0.84

            GPCR

            90

            D1

            391

            13

            96.8

            6724

            58

            99.1

            99.0

            0.91

              

            D2

            395

            9

            97.8

            6744

            38

            99.4

            99.4

            0.94

              

            D3

            393

            11

            97.3

            6726

            56

            99.2

            99.1

            0.92

              

            D4

            386

            18

            95.5

            6734

            48

            99.3

            99.1

            0.92

              

            D5

            381

            23

            94.3

            6723

            59

            99.1

            98.9

            0.90

              

            D6

            391

            13

            96.8

            6731

            51

            99.3

            99.1

            0.92

              

            D7

            382

            22

            94.6

            6685

            97

            98.6

            98.3

            0.86

              

            D8

            387

            17

            95.8

            6758

            24

            99.7

            99.4

            0.95

              

            D9

            391

            13

            96.8

            6752

            30

            99.6

            99.4

            0.94

              

            D10

            388

            16

            96.0

            6762

            20

            99.7

            99.5

            0.95

             

            70

            D1

            307

            8

            97.5

            6695

            58

            99.1

            99.1

            0.90

              

            D2

            309

            6

            98.1

            6715

            38

            99.4

            99.4

            0.93

              

            D3

            306

            9

            97.1

            6697

            56

            99.2

            99.1

            0.90

              

            D4

            301

            14

            95.6

            6705

            48

            99.3

            99.1

            0.90

              

            D5

            198

            17

            94.6

            6694

            59

            99.1

            98.9

            0.88

              

            D6

            307

            8

            97.5

            6702

            51

            99.2

            99.2

            0.91

              

            D7

            296

            19

            94.0

            6656

            97

            98.6

            98.4

            0.83

              

            D8

            301

            14

            95.6

            6729

            24

            99.6

            99.5

            0.94

              

            D9

            307

            8

            97.5

            6723

            30

            99.6

            99.5

            0.94

              

            D10

            302

            13

            95.9

            6733

            20

            99.7

            99.5

            0.95

            TC8.A

            90

            D1

            28

            27

            50.9

            1846

            2

            99.9

            98.5

            0.68

              

            D2

            33

            22

            60.0

            1846

            2

            99.9

            98.7

            0.75

              

            D3

            34

            21

            61.8

            1845

            3

            99.8

            98.7

            0.75

              

            D4

            29

            26

            52.7

            1845

            3

            99.8

            98.8

            0.75

              

            D5

            29

            26

            52.7

            1845

            3

            99.8

            98.8

            0.75

              

            D6

            36

            19

            65.5

            1846

            2

            99.9

            98.9

            0.78

              

            D7

            35

            20

            63.6

            1845

            3

            99.8

            98.8

            0.76

              

            D8

            40

            15

            72.7

            1845

            3

            99.8

            99.2

            0.82

              

            D9

            33

            22

            60.0

            1846

            2

            99.9

            98.7

            0.75

              

            D10

            40

            15

            72.7

            1845

            3

            99.8

            99.2

            0.82

             

            70

            D1

            25

            24

            51.0

            1828

            2

            99.9

            98.6

            0.68

              

            D2

            29

            20

            59.2

            1828

            2

            99.9

            98.8

            0.74

              

            D3

            29

            20

            59.2

            1827

            3

            99.8

            98.8

            0.73

              

            D4

            26

            23

            53.1

            1828

            2

            99.9

            98.7

            0.70

              

            D5

            26

            23

            53.1

            1828

            2

            99.9

            98.7

            0.70

              

            D6

            33

            16

            67.3

            1828

            2

            99.9

            99.0

            0.79

              

            D7

            30

            19

            61.2

            1827

            3

            99.8

            98.8

            0.74

              

            D8

            36

            13

            73.5

            1827

            3

            99.8

            99.2

            0.82

              

            D9

            29

            20

            59.2

            1828

            2

            99.9

            98.8

            0.74

              

            D10

            36

            13

            73.5

            1827

            3

            99.8

            99.2

            0.82

            Chlorophyll

            90

            D1

            159

            127

            55.6

            1594

            8

            99.5

            92.9

            0.70

              

            D2

            205

            81

            71.7

            1598

            4

            99.8

            95.5

            0.82

              

            D3

            224

            62

            78.3

            1599

            3

            99.8

            96.6

            0.86

              

            D4

            222

            64

            77.6

            1599

            3

            99.8

            96.5

            0.86

              

            D5

            211

            75

            73.8

            1598

            4

            99.8

            95.8

            0.83

              

            D6

            182

            104

            63.6

            1594

            8

            99.5

            94.1

            0.75

              

            D7

            159

            127

            55.6

            1595

            9

            99.4

            92.8

            0.69

              

            D8

            233

            53

            81.5

            1595

            7

            99.6

            96.8

            0.87

              

            D9

            224

            62

            78.3

            1594

            8

            99.5

            96.3

            0.85

              

            D10

            229

            57

            80.1

            1597

            5

            99.7

            96.7

            0.87

             

            70

            D1

            113

            118

            48.9

            1578

            8

            99.5

            93.1

            0.65

              

            D2

            155

            76

            67.1

            1582

            4

            99.8

            95.6

            0.79

              

            D3

            171

            60

            74.0

            1583

            3

            99.8

            96.5

            0.84

              

            D4

            171

            60

            74.0

            1583

            3

            99.8

            96.5

            0.84

              

            D5

            161

            70

            69.7

            1582

            4

            99.8

            95.9

            0.81

              

            D6

            137

            94

            59.3

            1578

            8

            99.5

            94.4

            0.72

              

            D7

            114

            117

            49.4

            1575

            11

            99.3

            93.0

            0.64

              

            D8

            182

            49

            78.8

            1579

            7

            99.6

            96.9

            0.85

              

            D9

            172

            59

            74.5

            1578

            8

            99.5

            96.3

            0.82

              

            D10

            178

            53

            77.1

            1581

            5

            99.7

            96.8

            0.85

            Lipid synthesis

            90

            D1

            403

            149

            73.0

            1213

            59

            95.4

            88.6

            0.72

              

            D2

            431

            121

            78.1

            1256

            16

            98.7

            92.5

            0.81

              

            D3

            436

            116

            79.0

            1268

            4

            99.7

            93.4

            0.84

              

            D4

            421

            131

            76.3

            1270

            2

            99.8

            92.7

            0.83

              

            D5

            416

            136

            75.4

            1270

            2

            99.8

            92.4

            0.82

              

            D6

            449

            103

            81.3

            1270

            2

            99.8

            94.2

            0.86

              

            D7

            435

            117

            78.8

            1269

            3

            99.8

            93.4

            0.84

              

            D8

            423

            129

            76.6

            1265

            7

            99.5

            92.5

            0.82

              

            D9

            449

            103

            81.3

            1245

            27

            97.9

            92.9

            0.83

              

            D10

            454

            98

            82.3

            1265

            7

            99.5

            94.2

            0.86

             

            70

            D1

            316

            138

            69.6

            1205

            59

            95.3

            88.5

            0.69

              

            D2

            343

            111

            75.6

            1248

            16

            98.7

            92.6

            0.81

              

            D3

            340

            114

            74.9

            1260

            4

            99.7

            93.1

            0.82

              

            D4

            330

            124

            72.7

            1262

            2

            99.8

            92.7

            0.81

              

            D5

            328

            126

            72.3

            1260

            4

            99.7

            92.4

            0.80

              

            D6

            358

            96

            78.9

            1244

            20

            98.4

            93.3

            0.82

              

            D7

            342

            112

            75.3

            1257

            7

            99.5

            93.1

            0.82

              

            D8

            331

            123

            72.9

            1257

            7

            99.4

            92.4

            0.80

              

            D9

            360

            94

            79.3

            1237

            27

            97.9

            93.0

            0.81

              

            D10

            360

            94

            79.3

            1257

            7

            99.5

            94.1

            0.85

            rRNA binding

            90

            D1

            1407

            91

            93.9

            3502

            59

            98.3

            97.0

            0.93

              

            D2

            1437

            61

            95.9

            3510

            51

            98.6

            97.8

            0.95

              

            D3

            1403

            95

            93.7

            3529

            32

            99.1

            97.5

            0.93

              

            D4

            1347

            151

            89.9

            3491

            70

            98.0

            95.6

            0.89

              

            D5

            1347

            151

            89.9

            3533

            28

            99.2

            96.5

            0.91

              

            D6

            1451

            47

            96.9

            3537

            24

            99.3

            98.6

            0.97

              

            D7

            1358

            140

            90.7

            3429

            132

            96.3

            94.6

            0.87

              

            D8

            1442

            56

            96.3

            3531

            30

            99.2

            98.3

            0.96

              

            D9

            1436

            62

            95.9

            3518

            43

            98.8

            97.9

            0.95

              

            D10

            1449

            49

            96.7

            3537

            24

            99.3

            98.6

            0.97

             

            70

            D1

            924

            83

            91.8

            3454

            59

            98.3

            96.9

            0.91

              

            D2

            952

            55

            94.5

            3463

            50

            98.6

            97.7

            0.93

              

            D3

            920

            87

            91.4

            3483

            30

            99.2

            97.4

            0.92

              

            D4

            907

            100

            90.1

            3444

            69

            98.0

            96.3

            0.89

              

            D5

            908

            99

            90.2

            3485

            28

            99.2

            97.2

            0.92

              

            D6

            963

            44

            95.6

            3493

            20

            99.4

            98.6

            0.96

              

            D7

            917

            90

            91.1

            3382

            131

            96.3

            95.1

            0.86

              

            D8

            654

            53

            94.7

            3484

            29

            99.2

            98.2

            0.95

              

            D9

            950

            57

            94.3

            3471

            42

            98.8

            97.8

            0.94

              

            D10

            960

            47

            95.3

            3490

            23

            99.4

            98.5

            0.96

            The performance of the ten descriptor-sets were ranked by the Matthews correlation coefficient (MCC) values of the respective SVM prediction of the six functional families, which are given in Table 5. The computed MCC scores for these descriptor-sets are in the range of 0.64~0.97 for all protein families studied. Accordingly, the performance of these descriptor-sets is categorized into two groups based on their MCC values: 'Exceptional' (>0.85) and 'Good' (≤0.85). Moreover, these descriptor-sets are aligned in the order of their MCC values with "=" being of equal values and ">" indicating that one is better than the other. It is noted that, as the differences of many of these MCC values are rather small, such alignment is likely superficial to some extent and may not best reflect the real ranking of performance. Overall, the performances of these descriptor-sets are not significantly different, there is no overwhelmingly preferred descriptor-set, and SVM prediction performance appears to be highly dependent on the dataset.
            Table 5:

            Descriptor sets ranked and grouped by MCC (Matthews correlation coefficient), before and after removal of homologous sequences at 90% and 70% identity, respectively.

            Protein family

            % HRS*

            Prediction performance

              

            Exceptional > 0.85

            Good = 0.85

            EC2.4

            NR

            D10 > D8> D9 > D3

            D5 > D4 = D6 > D2 > D1 > D7

             

            90%

            D10

            D8 > D3 = D9 > D5 > D2 = D4 > D6 > D7 > D1

             

            70%

             

            D10 > D8 > D3 = D9 > D5 > D2 > D4 > D6 > D7 > D1

            GPCR

            NR

            D8 > D10 > D1 = D2 = D3 = D4 = D6 = D9 > D5 > D7

             
             

            90%

            D8 = D10 > D2 = D9 > D3 = D4 = D6 > D1 > D5 > D7

             
             

            70%

            D10 > D8 = D9 > D2 > D6 > D1 = D3 = D4 > D5

            D7

            TC8.A

            NR

             

            D8 = D10 > D6 > D7 > D2 = D3 = D9 > D4 = D5 > D1

             

            90%

             

            D8 = D10 > D6 > D7 > D2 = D3 = D4 = D5 = D9 > D1

             

            70%

             

            D8 = D10 > D6 > D2 = D7 = D9 > D3 > D4 = D5 > D1

            Chlorophyll

            NR

            D8 = D10 > D4 > D3 = D9

            D5 > D2 > D6 > D7 > D1

             

            90%

            D8 = D10 > D3 = D4

            D9 > D5 > D2 > D6 > D1 > D7

             

            70%

             

            D8 = D10 > D3 = D4 > D9 > D5 > D2 > D6 > D1 > D7

            Lipid synthesis

            NR

            D10 > D6

            D7 > D2 = D3 = D9 > D4 = D8 > D5 > D1

             

            90%

            D6 = D10

            D3 = D7 > D4 = D9 > D5 = D8 > D2 > D1

             

            70%

             

            D10 > D3 = D6 = D7 > D2 = D4 = D9 > D5 = D8 > D1

            rRNA binding

            NR

            D10 > D8 = D9 > D2 = D3 = D6 > D1 > D7> D4 = D5

             
             

            90%

            D6 = D10 > D8 > D2 = D9 > D1 = D3 > D5 > D4> D7

             
             

            70%

            D6 = D10> D8 > D9 > D2 > D3 = D5 > D1 > D4 > D7

             

            *HSR: homologous sequence removed

            NR: (homologous sequences) Not Removed

            As shown in Table 3 and Table 4, for many of the studied datasets, the differences in prediction accuracies and MCC values between different descriptor-sets are small. In particular, for GPCR and rRNA binding proteins, the results of almost all descriptor-sets are in the 'Exceptional' category. Examining the range of MCC values of the descriptor-sets for each of the studied protein families (after removal of 70% homologous sequences), the differences between the largest and smallest MCC values are, in order of increasing magnitude: 0.10, 0.12, 0.14, 0.16, 0.21 and 0.21 for rRNA binding proteins, GPCR, TC8.A, lipid synthesis proteins, chlorophyll proteins and EC.2.4 families respectively. Given that a difference of 0.10 and 0.20 in MCC values translates to an approximate 4% and 7% difference in overall prediction accuracy, this separation is not large indeed.

            Though the dataset is a more important determinant of prediction performance than the choice of descriptor class, a few general trends could be observed. Three out of four of the combination-sets tend to exhibit slightly but consistently higher MCC values for the protein families studied in this work. These sets are Sets D8, D9 and D10. In contrast, only one out of six individual sets, Set D6, tend to exhibit slightly but consistently higher MCC values for the protein families studied in this work. Therefore, statistically speaking, it appears that the use of combination-sets tend to give slightly better prediction performance than the use of individual-sets.

            When each class was examined individually in this study, we find that the combination of amino acid composition and dipeptide composition (Set D9) tends to give consistently better results than that of the individual descriptor-sets (Set D1 and Set D2). It has been reported that one drawback of amino acid composition descriptors is that the same amino acid composition may correspond to diverse sequences as sequence order is lost [24, 33]. This sequence order information can be partially covered by considering dipeptide composition (Set D2). On the other hand, dipeptide composition lacks information concerning the fraction of the individual residue in the sequence, thus, a combination-set is expected to give better prediction results [24, 33, 65, 66].

            Using all descriptor-sets (Set D10) generally, but not always, gives the best result, which is consistent with the findings on the use of molecular descriptors for predicting compounds of specific properties. [67, 68] For instance, Xue et al. found that feature selection methods are capable of reducing the noise generated by the use of overlapping and redundant molecular descriptors, and in some cases, improving the accuracy of SVM classification of pharmacokinetic behaviour of chemical agents [69]. In our study, for example, the three autocorrelation descriptor-sets (Sets D3, D4 and D5) all utilize the same physicochemical properties, only differing in the correlation algorithm. The use of all available descriptors likely results in the inclusion of partially redundant information, some of which may to some extent become noise that interferes with the prediction results or obscures relevant information. Based on the results of previous studies [69], it is possible that feature selection methods may be applied for selecting the optimal set of descriptors to improve prediction accuracy as well as computing efficiency for predicting protein functional families.

            Conclusion

            The effectiveness of ten protein descriptor-sets in six protein functional family prediction using SVM was evaluated. Corroborating with previous work done on chemical descriptors [67, 68, 7076] and protein descriptors [4, 21, 30, 32, 35, 43, 77, 78], we found that the descriptor-sets evaluated in this paper, which comprise some of the commonly used descriptors, generally return good results and do not differ significantly. In particular, the use of combination descriptor-sets tends to give slightly better prediction performance than the use of individual descriptor-sets. While there seems to be no preferred descriptor-set that could be utilized for all datasets as prediction results is highly dependent on datasets, the performance of protein classification may be enhanced by selection of optimal combinations of descriptors using established feature-selection methods [79, 80]. Incorporation of appropriate sets of physicochemical properties not covered by some of the existing descriptor-sets may also help improving the prediction performance.

            Methods

            Datasets

            The datasets were obtained from SwissProt [81], except for TC8.A, which was downloaded from Transport Classification Database (TCDB) [41]. These datasets were chosen for their functional diversity, sample size and the range of reported family member prediction accuracies. As SVM is essentially a statistical method, the datasets cannot be too small; yet it would also be convenient for the purposes of this study if they were not too large as to be unwieldy computationally. These downloaded datasets were used to construct the positive dataset for the corresponding SVM classification system. A negative dataset, representing non-class members, was generated by a well-established procedure [2, 3, 21, 30] such that all proteins was grouped into domain families [82] in the PFAM database, and the representative proteins of these families unrelated to the protein family being studied were chosen as negative samples.

            These proteins, positive and negative, were further divided into separate training, testing and independent evaluation sets by the following procedure: First, proteins were converted into descriptor vectors and then clustered using hierarchical clustering into groups in the structural and physicochemical feature space [83], where more homologous sequences will have shorter distances between them, and the largest separation between clusters was set to a ceiling of 20. One representative protein was randomly selected from each group to form a training set that is sufficiently diverse and broadly distributed in the feature space. Another protein within the group was randomly selected to form the testing set. The selected proteins from each group were further checked to ensure that they are distinguished from the proteins in other groups. The remaining proteins were then designated as the independent evaluation set, also checked to be at a reasonable level of diversity. Fragments, defined as smaller than 60 residues, were discarded. This selection process ensures that the training, testing and evaluation sets constructed are sufficiently diverse and broadly distributed in the feature space. Though an analysis of the 'similar' proteins in each cluster showed that the majority of the proteins in a cluster are quite non-homologous, the program CDHIT (Cluster Database at High Identity with Tolerance) [6264] was further used after the SVM model was trained to remove redundancy at both 90% and 70% sequence identity, so as to avoid bias as far as possible. CDHIT removes homologous sequences by clustering the protein dataset at some user-defined sequence identity threshold, for example 90%, and then generating a database of only the cluster representatives, thus eliminating sequences with greater than 90% identity. The statistical details are given in Tables 2 and 3.

            Algorithms for generating protein descriptors

            Ten sets of commonly used composition and physicochemical descriptors were generated from the protein sequence (see Table 1). These descriptors can be computed via the PROFEAT server [22].

            Amino acid composition (Set D1) is defined as the fraction of each amino acid type in a sequence
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ1_HTML.gif
            (1)
            where r = 1, 2, ..., 20, N r is the number of amino acid of type r, and N is the length of the sequence. Dipeptide composition (Set D2) is defined as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ2_HTML.gif
            (2)

            where r, s = 1, 2, ..., 20, N ij is the number of dipeptides composed of amino acid types r and s.

            Autocorrelation descriptors are a class of topological descriptors, also known as molecular connectivity indices, describe the level of correlation between two objects (protein or peptide sequences) in terms of their specific structural or physicochemical property [84], which are defined based on the distribution of amino acid properties along the sequence [85]. Eight amino acid properties are used for deriving the autocorrelation descriptors: hydrophobicity scale [86]; average flexibility index [87]; polarizability parameter [88]; free energy of amino acid solution in water [88]; residue accessible surface areas [89]; amino acid residue volumes [90]; steric parameters [91]; and relative mutability [92].

            These autocorrelation properties are normalized and standardized such that
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ3_HTML.gif
            (3)
            where http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_IEq1_HTML.gif is the average value of a particular property of the 20 amino acids. http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_IEq1_HTML.gif and σ are given by
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ4_HTML.gif
            (4)
            and
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ5_HTML.gif
            (5)
            Moreau-Broto autocorrelation descriptors (Set D3) [84, 93] are defined as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ6_HTML.gif
            (6)
            where d = 1, 2, ..., 30 is the lag of the autocorrelation, and P i and P i+dare the properties of the amino acid at positions i and i+d respectively. After applying normalization, we get
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ7_HTML.gif
            (7)
            Moran autocorrelation descriptors (Set D4) [94] are calculated as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ8_HTML.gif
            (8)
            where d, P i and P i+dare defined in the same way as that for Moreau-Broto autocorrelation and http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_IEq1_HTML.gif is the average of the considered property P along the sequence:
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ9_HTML.gif
            (9)
            Geary autocorrelation descriptors (Set D5) [95] are written as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ10_HTML.gif
            (10)

            where d, http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_IEq1_HTML.gif , P i and P i+dare defined as above. Comparing the three autocorrelation descriptors: while Moreau-Broto autocorrelation uses the property values as the basis for measurement, Moran autocorrelation utilizes property deviations from the average values, and Geary utilizes the square-difference of property values instead of vector-products (of property values or deviations). The Moran and Geary autocorrelation descriptors measure spatial autocorrelation, which is the correlation of a variable with itself through space.

            The descriptors in Set D6 comprise of the composition (C), transition (T) and distribution (D) features of seven structural or physicochemical properties along a protein or peptide sequence [5, 29]. The seven physicochemical properties [2, 5, 29] are hydrophobicity; normalized Van der Waals volume; polarity; polarizibility; charge; secondary structures; and solvent accessibility. For each of these properties, the amino acids are divided into three groups such that those in a particular group are regarded to have approximately the same property. For instance, residues can be divided into hydrophobic (CVLIMFW), neutral (GASTPHY), and polar (RKEDQN) groups. C is defined as the number of residues with that particular property divided by the total number of residues in a protein sequence. T characterizes the percent frequency with which residues with a particular property is followed by residues of a different property. D measures the chain length within which the first, 25%, 50%, 75% and 100% of the amino acids with a particular property are located respectively. There are 21 elements representing these three descriptors: 3 for C, 3 for T and 15 for D, and the protein feature vector is constructed by sequentially combining the 21 elements for all of these properties and the 20 residues, resulting in a total of 188 dimensions.

            The quasi-sequence order descriptors (Set D7) [96] are derived from both the Schneider-Wrede physicochemical distance matrix [10, 18, 97] and the Grantham chemical distance matrix [31], between each pair of the 20 amino acids. The physicochemical properties computed include hydrophobicity, hydrophilicity, polarity, and side-chain volume. Similar to the descriptors in Set D6, sequence order descriptors can also be used for representing amino acid distribution patterns of a specific physicochemical property along a protein or peptide sequence [18, 31]. For a protein chain of N amino acid residues R1R2...R N , the sequence order effect can be approximately reflected through a set of sequence order coupling numbers
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ11_HTML.gif
            (11)
            where τ d is the dth rank sequence order coupling number (d = 1, 2, ..., 30) that reflects the coupling mode between all of the most contiguous residues along a protein sequence, and d i,i+dis the distance between the two amino acids at position i and i+d. For each amino acid type, the type 1 quasi sequence order descriptor can be defined as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ12_HTML.gif
            (12)
            where r = 1, 2, ..., 20, f r is the normalized occurrence of amino acid type i and w is a weighting factor (w = 0.1). The type 2 quasi sequence order is defined as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ13_HTML.gif
            (13)

            where d = 21, 22, ..., 50. The combination of these two equations gives us a vector that describes a protein: the first 20 components reflect the effect of the amino acid composition, while the components from 21 to 50 reflect the effect of sequence order.

            Similar to the quasi-sequence order descriptor, the pseudo amino acid descriptor (Set D8) is made up of a 50-dimensional vector in which the first 20 components reflect the effect of the amino acid composition and the remaining 30 components reflect the effect of sequence order, only now, the coupling number τ d is now replaced by the sequence order correlation factor θλ [32]. The set of sequence order correlated factors is defined as follows:
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ14_HTML.gif
            (14)
            where θλ is the first-tier correlation factor that reflects the sequence order correlation between all of the λ-most contiguous resides along a protein chain (λ = 1,...30) and N is the number of amino acid residues. Θ(R i , R j ) is the correlation factor and is given by
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ15_HTML.gif
            (15)
            where H 1(R i ), H 2(R i ) and M(R i ) are the hydrophobicity [98], hydrophilicity [99] and side-chain mass of amino acid R i , respectively. Before being substituted in the above equation, the various physicochemical properties P(i) are subjected to a standard conversion,
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ16_HTML.gif
            (16)
            This sequence order correlation definition [Eqs. (14), (15)] introduce more correlation factors of physicochemical effects as compared to the coupling number [Eq. (11)], and has shown to be an improvement on the way sequence order effect information is represented [32, 35, 100]. Thus, for each amino acid type, the first part of the vector is defined as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ17_HTML.gif
            (17)
            where r = 1, 2, ..., 20, f r is the normalized occurrence of amino acid type i and w is a weighting factor (w = 0.1), and the second part is defined as
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ18_HTML.gif
            (18)

            Support Vector Machines (SVM)

            As the SVM algorithms have been extensively described in the literature [2, 3, 101], only a brief description is given here. In the case of a linear SVM, a hyperplane that separates two different classes of feature vectors with a maximum margin is constructed. One class represents positive samples, for example EC2.4 proteins, and the other the negative samples. This hyperplane is constructed by finding a vector w and a parameter b that minimizes ||w||2 that satisfies the following conditions: w·x i + b ≥ 1, for y i = 1 (positive class) and w·x i + b ≤ -1, for y i = -1 (negative class). Here x i is a feature vector, y i is the group index, w is a vector normal to the hyperplane, http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_IEq2_HTML.gif is the perpendicular distance from the hyperplane to the origin, and ||w||2 is the Euclidean norm of w. In the case of a nonlinear SVM, feature vectors are projected into a high dimensional feature space by using a kernel function such as http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_IEq3_HTML.gif . The linear SVM procedure is then applied to the feature vectors in this feature space. After the determination of w and b, a given vector x can be classified by using sign [(w.x) + b], a positive or negative value indicating that the vector x belongs to the positive or negative class respectively.

            As a discriminative method, the performance of SVM classification can be accessed by measuring the true positive TP (correctly predicted positive samples), false negative FN (positive samples incorrectly predicted as negative), true negative TN (correctly predicted negative samples), and false positive FP (negative samples incorrectly predicted as positive) [4, 102, 103]. As the numbers of positive and negative samples are imbalanced, the positive prediction accuracy or sensitivity Q p = TP/(TP+FN) and negative prediction accuracy or specificity Q n = TN/(TN+FP) [101] are also introduced. The overall accuracy is defined as Q = (TP+TN)/(TP+FN+TN+FP). However, in some cases, Q, Q p , and Q n are insufficient to provide a complete assessment of the performance of a discriminative method [102, 104]. Thus the Matthews correlation coefficient (MCC) was used in this work to evaluate the randomness of the prediction:
            http://static-content.springer.com/image/art%3A10.1186%2F1471-2105-8-300/MediaObjects/12859_2006_Article_1672_Equ19_HTML.gif
            (19)

            where MCC ∈ [-1,1], with a negative value indicating disagreement of the prediction and a positive value indicating agreement. A zero value means the prediction is completely random. The MCC utilizes all four basic elements of the accuracy and it provides a better summary of the prediction performance than the overall accuracy.

            Declarations

            Authors’ Affiliations

            (1)
            Department of Pharmacy, National University of Singapore
            (2)
            College of Chemistry, Sichuan University
            (3)
            Shanghai Center for Bioinformatics Technology

            References

            1. Karchin R, Karplus K, Haussler D: Classifying G-protein coupled receptors with support vector machines. Bioinformatics 2002, 18:147–159.PubMedView Article
            2. Cai CZ, Han LY, Ji ZL, Chen X, Chen YZ: SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nuclei Acid Res 2003, 31:3692–3697.View Article
            3. Cai CZ, Han LY, Ji ZL, Chen YZ: Enzyme family classification by support vector machines. Proteins 2004, 55:66–76.PubMedView Article
            4. Han LY, Cai CZ, Lo SL, Chung MC, Chen YZ: Prediction of RNA-binding proteins from primary sequence by a support vector machine approach . RNA 2004, 10:355–368.PubMedView ArticlePubMed Central
            5. Dubchak I, Muchnick I, Mayor C, Dralyuk I, Kim SH: Recognition of a protein fold in the context of the Structural Classification of Proteins (SCOP) classification. Proteins 1999, 35:401–407.PubMedView Article
            6. Bock JR, Gough DA: Predicting protein--protein interactions from primary structure. Bioinformatics 2001, 17:455–460.PubMedView Article
            7. Bock JR, Gough DA: Whole-proteome interaction mining . Bioinformatics 2003, 19:125–134.PubMedView Article
            8. Lo SL, Cai CZ, Chen YZ, Chung MC: Effect of training datasets on support vector machine prediction of protein-protein interactions. Proteomics 2005, 5:876–884.PubMedView Article
            9. Chou KC, Cai YD: Predicting protein-protein interactions from sequences in a hybridization space. J Proteome Res 2006, 5:316–322.PubMedView Article
            10. Chou KC: Prediction of protein subcellular locations by incorporating quasi-sequence-order effect. Biochem Biophys Res Commun 2000, 278:477–483.PubMedView Article
            11. Chou KC, Cai YD: Prediction of protein subcellular locations by GO-FunD-PseAA predictor. Biochem Biophys Res Commun 2004, 320:1236–1239.PubMedView Article
            12. Chou KC, Shen HB: Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun 2006, 347:150–157.PubMedView Article
            13. Chou KC, Shen HB: Large-scale plant protein subcellular location prediction. J Cell Biochem 2006,100(3):665–678.View Article
            14. Bhasin M, Garg A, Raghava GP: PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005,21(10):2522–2524.PubMedView Article
            15. Guo J, Lin Y, Liu XJ: GNBSL: a new integrative system to predict the subcellular location for Gram-negative bacteria proteins. Proteomics 2006,6(19):5099–5105.PubMedView Article
            16. Guo J, Lin Y: TSSub: eukaryotic protein subcellular localization by extracting features from profiles. Bioinformatics 2006,22(14):1784–1785.PubMedView Article
            17. Cui J, Han LY, Lin HH, Zhang HL, Tang ZQ, Zheng CJ, Cao ZW, Chen YZ: Prediction of MHC-binding peptides of flexible lengths from sequence-derived structural and physicochemical properties. Mol Immunol 2007, 44:866–877.PubMedView Article
            18. Schneider G, Wrede P: The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site. Biophys J 1994, 66:355–344.View Article
            19. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares MJ Jr, Haussler D: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 2000,97(1):262–267.PubMedView ArticlePubMed Central
            20. Ward JJ, McGuffin LJ, Buxton BF, Jones DT: Secondary structure prediction with support vector machines . Bioinformatics 2003,19(13):1650–1655.PubMedView Article
            21. Han LY, Cai CZ, Ji ZL, Cao ZW, Cui J, Chen YZ: Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach. Nuclei Acid Res 2004, 32:6437–6444.View Article
            22. Li ZR, Lin HH, Han LY, Jiang L, Chen X, Chen YZ: PROFEAT: A web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nuclei Acid Res 2006,34(Web Server issue):W32–37.View Article
            23. Chou KC, Cai YD: Prediction of membrane protein types by incorporating amphipathic effects. J Chem Inf Model 2005,45(2 ):407–413.PubMedView Article
            24. Gao QB, Wang ZZ, Yan C, Du YH: Prediction of protein subcellular location using a combined feature of sequence. FEBS Lett 2005,579(16):3444–3448.PubMedView Article
            25. Feng ZP, Zhang CT: Prediction of membrane protein types based on the hydrophobic index of amino acids. J Protein Chem 2000, 19:262–275.View Article
            26. Lin Z, Pan XM: Accurate prediction of protein secondary structural content. J Protein Chem 2001, 20:217–220.PubMedView Article
            27. Horne DS: Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers 1988, 27:451–477.PubMedView Article
            28. Sokal RR, Thomson BA: Population structure inferred by local spatial autocorrelation: an example from an Amerindian tribal population. Am J Phys Anthropol 2006, 129:121–131.PubMedView Article
            29. Dubchak I, I M, Holbrook SR, Kim SH: Prediction of protein folding class using global description of amino acid sequence. Proc Natl Acad Sci USA 1995, 92:8700–8704.PubMedView ArticlePubMed Central
            30. Lin HH, Han LY, Cai CZ, Ji ZL, Chen YZ: Prediction of transporter family from protein sequence by support vector machine approach. Proteins 2006,62(1):218–231.PubMedView Article
            31. Grantham R: Amino acid difference formula to help explain protein evolution. Science 1974, 185:862–864.PubMedView Article
            32. Chou KC: Prediction of protein cellular attributes using pseudo amino acid composition. Proteins: Structure Function and Genetics 2001, 43:246–255.View Article
            33. Bhasin M, Raghava GP: Classification of nuclear receptors based on amino acid composition and dipeptide composition. J Biol Chem 2004, 279:23262–23266.PubMedView Article
            34. NC-IUBMB: Enzyme Nomenclature. San Diego, California , Academic Press 1992.
            35. Chou KC: Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005, 21:10–19.PubMedView Article
            36. Chou KC, Cai YD: Predicting enzyme family class in a hybridization space. Protein Sci 2004, 13:2857–2863.PubMedView ArticlePubMed Central
            37. Chou KC, Elrod DW: Prediction of enzyme family classes. J Proteome Res 2003, 2:183–190.PubMedView Article
            38. Chou KC: Prediction of G-protein-coupled receptor classes. J Proteome Res 2005, 4:1413–1418.PubMedView Article
            39. Chou KC, Elrod DW: Bioinformatical analysis of G-protein-coupled receptors. J Proteome Res 2002, 1:429–433.PubMedView Article
            40. Bhasin M, Raghava GP: GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nuclei Acid Res 2004,32(Web Server issue):W383–389.View Article
            41. Saier MHJ, Tran CV, Barabote RD: TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nuclei Acid Res Saier Lab Bioinformatics Group 2006,34(Database issue):D181-D186.
            42. Suzuki JY, Bollivar DW, Bauer CE: Genetic analysis of chlorophyll biosynthesis. Annu Rev Genet 1997, 31:61–89.PubMedView Article
            43. Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Chen YZ: Prediction of the functional class of lipid binding proteins from sequence-derived properties irrespective of sequence similarity. J Lipid Res 2006, 47:824–831.PubMedView Article
            44. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares MJ, Haussler D: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 2000,97(1):262–267.PubMedView ArticlePubMed Central
            45. Burbidge R, Trotter M, Buxton B, Holden S: Drug design by machine learning: support vector machines for pharmaceutical data analysis. Comput Chem 2001,26(1):5–14.PubMedView Article
            46. Baenzigner JU: Protein-specific glycosyltransferase: how and why they do it! FASEB J 1994,8(13):1019–1025.
            47. Kapitonov D, Yu RK: Conserved domains of glycosyltransferase. Glycobiology 1999, 9:961–978.PubMedView Article
            48. Busch W, Saier MHJ: The Transporter Classification (TC) system . Crit Rev Biochem Mol Biol 2002,37(5):287–337.PubMedView Article
            49. Drews J: Genomic sciences and the medicine of tomorrow. Nat Biotechnol 1996,14(11):1516–1518.PubMedView Article
            50. Gudermann TB, Nurnberg B, Schultz G: Receptors and G proteins as primary components of transmembrane signal transduction. Part 1. G-protein-coupled receptors: structure and function. J Mol Med 1995,73(2):51–63.PubMedView Article
            51. Muller G: Towards 3D structures of G protein-coupled receptors: a multidisciplinary approach. Curr Med Chem 2000,7(9):861–888.PubMed
            52. Paulson JC, Colley KJ: Glycosyltransferase. J Biol Chem 1989,264(30):17645–17618.
            53. Beale SI, Weinstein JD: Biochemistry and regulation of photosynthetic pigment formation in plants and algae. Biosynthesis of Tetrapyrroles (Edited by: Jordan PM). Amsterdam , Elsevier 1991, 155–235.View Article
            54. Glatz JF, Luiken JJ, van Bilsen M, van der Vusse GJ: Cellular lipid binding proteins as facilitators and regulators of lipid metabolism. Mol Cell Biochem 2002, 239:3–7.PubMedView Article
            55. Burd CG, Dreyfuss G: Conserved structures and diversity of functions of RNA-binding proteins . Science 1994, 265:615–621.PubMedView Article
            56. Kiledjian M, Burd CG, Portman DS, Gorlach M, Dreyfuss G: Structure and function of hnRNP proteins. RNA-Protein Interactions: Frontiers in Molecular Biology (Edited by: Nagai K, Mattaj IW). Oxford , IRL Press 1994, 127–149.
            57. Draper DE: Themes in RNA-protein recognition. J Mol Biol 1999, 293:255–270.PubMedView Article
            58. Fierro-Monti I, Mathews MB: Proteins binding to duplexed RNA: one motif, multiple functions. Trends Biochem Sci 2000, 25:241–246.PubMedView Article
            59. Perculis BA: RNA-binding proteins: If it looks like a sn(o)RNA. Curr Biol 2000, 10:R916-R918.View Article
            60. Perez-Canadillas JM, Varani G: Recent advances in RNA-protein recognition. Curr Opin Struct Biol 2001, 11:53–58.PubMedView Article
            61. Chou KC, Zhang CT: Prediction of protein structural classes. Crit Rev Biochem Mol Biol 1995,30(4):275–349.PubMedView Article
            62. Li WZ, Godzik A: Cd-hit: a fast program for clustering and comparing large sets of proteins or nucleotide sequences. Bioinformatics 2006, 22:1658–1659.PubMedView Article
            63. Li WZ, Jaroszewksi L, Godzik A: Clustering of highly homologous sequences to reduce the size of large protein database. Bioinformatics 2001, 17:282–283.PubMedView Article
            64. Li WZ, Jaroszewksi L, Godzik A: Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics 2002, 18:77–82.PubMedView Article
            65. Garg A, Bhasin M, Raghava GP: Support vector machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search. J Biol Chem 2005,280(15):14427014432.View Article
            66. Bhasin M, Raghava GP: ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nuclei Acid Res 2004,32(Web Server issue):414–419.View Article
            67. Xue L, Bajorath J: Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Comb Chem High Throughput Screen 2000,3(5):363–372.PubMed
            68. Xue L, Godden JW, Bajorath J: Identification of a preferred set of descriptors for compound classification based on principal component analysis. J Chem Inf Comput Sci 1999, 39:669–704.
            69. Xue Y, Li ZR, Yan CW, Sun LZ, Chen X, Chen YZ: Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J Chem Inf Comput Sci 2004,44(5):1630–1638.PubMed
            70. Brown RD, Martin YC: Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compound selection. J Chem Inf Comput Sci 1996,36(3):572–584.
            71. Cramer RD, Patterson DE, Bunce JD: Comparative molecular field analysis (CoMFA): effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 1988, 110:5959–5967.View ArticlePubMed
            72. Glen WG, Dunn WJ, Scott RD: Principal components analysis and partial least squares regression. Tetrahedron Comput Methodol 1989, 2:349–376.View Article
            73. Matter H: Selecting optimally diverse compounds from structure databases: a validation study of two-dimensional and three-dimensional molecular descriptors. J Med Chem 1997,40(8):1219–1229.PubMedView Article
            74. Matter H, Pötter T: Comparing 3D pharmacophore triplets and 2D fingerprints for selecting diverse compound subsets. J Chem Inf Comput Sci 1999, 39:1211–1225.
            75. Patterson DEP, Cramer RD, Ferguson AM, Clark RD, Weinberger LE: Neighborhood behavior: a useful concept for validation of "molecular diversity" descriptors. J Med Chem 1996,39(16):049 -3059.View Article
            76. Xue L, Godden JW, Bajorath J: Evaluation of descriptors and mini-fingerprints for the identification of molecules with similar activity. J Chem Inf Comput Sci 2000,40(5):1227–1234.PubMed
            77. Lin HH, Han LY, Zhang HL, Zheng CJ, Xie B, Chen YZ: Prediction of the functional class of DNA-binding proteins from sequence derived structural and physicochemical properties. 2006.
            78. Chen C, Zhou X, Tian Y, Zhou X, Cai P: Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. Anal Biochem 2006, 357:116–121.PubMedView Article
            79. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D: Support vector machines classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16:906–914.PubMedView Article
            80. Yu H, Yang J, Wang W, Han J: Discovering compact and highly discriminative features or feature combinations of drug activities using support vector machines. Proc IEEE Comput Soc Bioinform Conf 2003, (2):220–228.
            81. Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nuclei Acid Res 2003,31(1):365–370.View Article
            82. Bateman A, Birney E, Cerruti L, Durbin R, Etwiller L, Eddy SR, Griffiths–Jones S, Howe KL, Marshall M, Sonnhammer EL: The Pfam protein families database. Nuclei Acid Res 2002,31(1):276–280.View Article
            83. Heyer LJ, Kruglyak S, Yooseph S: Exploring expression data: Identification and analysis of coexpressed genes. Genome Res 1999,9(11):1106–1115.PubMedView ArticlePubMed Central
            84. Broto P, Moreau G, Vandicke C: Molecular structures: perception, autocorrelation descriptor and SAR studies. Eur J Med Chem 1984, 19:71–78.
            85. Kawashima S, Kanehisa M: AAindex: amino acid index database. Nuclei Acid Res 2000, 28:374.View Article
            86. Cid H, Bunster M, Canales M, Gazitua F: Hydrophobicity and structural classes in proteins. Protein Eng 1992, 5:373–375.PubMedView Article
            87. Bhaskaran R, Ponnuswammy PK: Positional flexibilities of amino acid residues in globular proteins. Int J Pept Protein Res 1988, 32:242–255.
            88. Charton M, Charton BI: The structural dependence of amino acid hydrophobicity parameters. J Theor Biol 1982, 99:629–644.PubMedView Article
            89. Chothia C: The nature of the accessible and buried surfaces in proteins. J Mol Biol 1976, 15:1–12.View Article
            90. Bigelow CC: On the average hydrophobicity of proteins and the relation between it and protein structure. J Theor Biol 1967, 16:187–211.PubMedView Article
            91. Charton M: Protein folding and the genetic code: an alternative quantitative model. J Theor Biol 1981, 91:115–373.PubMedView Article
            92. Dayhoff H, Calderone H: Composition of proteins. Atlas of Protein Sequence and Structure 1978, 5:363–373.
            93. Moreau G, Broto P: Autocorrelation of molecular structures, application to SAR studies. Nour J Chim 1980, 4:757–767.
            94. Moran PAP: Notes on continuous stochastic phenomena. Biometrika 1950, 37:17–23.PubMed
            95. Geary RC: The contiguity ratio and statistical mapping. Incorp Statist 1954, 5:115–145.View Article
            96. Cai YD, Liu XJ, Xu X, Chou KC: Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect. J Cell Biochem 2002,84(2):343–348.PubMedView Article
            97. Chou KC, Cai YD: Using functional domain composition and support vector machines for prediction of protein subcellular location. J Biol Chem 2002, 277:45765–45769.PubMedView Article
            98. Jones DD: Amino acid properties and side-chain orientation in proteins: a cross correlation approach. J Theor Biol 1975, 50:167–183.PubMedView Article
            99. Hopp TP, Woods KR: Prediction of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci USA 1981, 78:3824–3828.PubMedView ArticlePubMed Central
            100. Feng ZP: An overview on predicting the subcellular location of a protein. In Silico Biol 2002, 2:291–303.PubMed
            101. Burges CJC: A tutorial on support vector machines for pattern recognition. Data Min Knowl Dis 1998,2(2):121–167.View Article
            102. Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000,16(5):412–424.PubMedView Article
            103. Roulston JE: Screening with tumor markers: critical issues. Mol Biotechnol 2002,20(2):153–162.PubMedView Article
            104. Provost F, Fawcett T, Kohavi R: The case against accuracy estimation for comparing induction algorithms. Proc 15th International Conf on Machine Learning San Francisco, California , Morgan Kaufmann 1998, 445–453.

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            © Ong et al. 2007

            This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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