Volume 13 Supplement 10

Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)

Open Access

Computing the protein binding sites

BMC Bioinformatics201213(Suppl 10):S2

DOI: 10.1186/1471-2105-13-S10-S2

Published: 25 June 2012

Abstract

Background

Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification and comparison of functional sites. Structural genomic projects are beginning to produce protein structures with unknown functions. Therefore, efficient methods are required if all these structures are to be properly annotated. Lots of methods for finding binding sites involve 3D structure comparison. Here we design a method to find protein binding sites by direct comparison of protein 3D structures.

Results

We have developed an efficient heuristic approach for finding similar binding sites from the surface of given proteins. Our approach consists of three steps: local sequence alignment, protein surface detection, and 3D structures comparison. We implement the algorithm and produce a software package that works well in practice. When comparing a complete protein with all complete protein structures in the PDB database, experiments show that the average recall value of our approach is 82% and the average precision value of our approach is also significantly better than the existing approaches.

Conclusions

Our program has much higher recall values than those existing programs. Experiments show that all the existing approaches have recall values less than 50%. This implies that more than 50% of real binding sites cannot be reported by those existing approaches. The software package is available at http://sites.google.com/site/guofeics/bsfinder.

Background

Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification and comparison of functional sites. Structural genomic projects are beginning to produce protein structures with unknown functions. Therefore, efficient methods are required if all these structures are to be properly annotated.

Many methods have been proposed for identifying the location of binding sites on proteins. Laurie and Jackson give an energy-based method for the prediction of protein-ligand binding sites [1]. Bradford and Westhead combine a support vector machine (SVM) approach with surface patch analysis to predict protein-protein binding sites [2]. Chen et al. develop a tool, 3D-partner, for inferring interacting partners and binding models [3]. 3D-partner first utilizes IMPALA to identify homologous structures (templates) of a query protein sequence from heterodimer profile library. The sequence profiles of those templates are then used to search interacting candidates of the query from protein sequence databases by PSI-BLAST. Lo et al. develop a method for predicting helix-helix interaction from residue contacts in membrane proteins [4]. They first predict contact residues from sequences. Their relationships are further predicted in the second step via statistical analysis on contact propensities and sequence and structural information. Li et al. propose an approach for finding binding sites for groups of proteins [5]. It contains the following steps: finding protein groups as bicliques of protein-protein interaction networks (PPI), identifying conserved motifs, and searching domain-domain interaction databases. Liu et al. extend the method of Li et al. in [5] and consider comparing 3D local structures [6]. Guo and Wang identify the binding sites by finding two similar 3D substructures [7].

SiteEngine is a method that recognizes the regions on the surface of one protein that are similar to the binding sites of another. It uses geometric hashing triangles to transfer the input sites into the recognized region [8]. SuMo is a system for finding similarities in arbitrary 3D structures or substructures of proteins. It is based on a unique representation of macromolecules using selected triples of chemical groups [9]. The web server pdbFun analyzes the structure and function of proteins at the residue level [10]. When comparing a complete protein with all complete protein structures in the PDB database, experiments show that all the existing approaches have recall values less than 50% implying that more than 50% of real binding sites cannot be reported by those existing approaches.

In this paper, we design a method to recognize regions of binding sites on the proteins. It consists of three steps: local sequence alignment, protein surface detection, and 3D structures comparison. Experiments show that the average recall value of our approach is 82% and the average precision value of our approach is also significantly better than the existing approaches.

Methods

Given two complete protein structures, our task is to find the binding sites on the given proteins. Our method contains three steps. Step 1, we do local sequence alignment at the atom level to get the alignments of conserved regions. These alignments of conserved regions may contain some gaps. Step 2, among the conserved regions obtained in Step 1, we use the 3D structure information to identify the surface segments. Step 3, for any pair of the surface segments identified in Step 2, we compute a rigid transformation to compare the similarity of the substructures in 3D space and output the qualified pairs as binding sites.

Step 1: Local sequence alignment

In PDB format files, each residue (amino acid) is represented in the traditional order of atom records N, CA, C, O, followed by the side chain atoms (CB, CG1, CG2 ...) in order first of increasing remoteness, and then branch. The whole protein sequence of residues can be translated into a sequence of atoms based on this representation. The sequences of binding sites on the proteins are usually conserved at the atom level. When looking at the SitesBase [11], we can see that the pair of binding sites form a conserved region that are well aligned at the atom level, where atoms of the same types are matched and all the unmatched atoms correspond to gaps. Figure 1 is the result of SitesBase for proteins 1TU4D and 5P21A.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-S10-S2/MediaObjects/12859_2012_Article_5207_Fig1_HTML.jpg
Figure 1

The binding sites on 1TU4D and 5P21A.

We use the standard Smith-Waterman's local alignment algorithm [12] to find the conserved segments, where a matched pair of atoms of the same type has a score 1, a mismatched pair of atoms of different types has a score -∞, a mismatch between an atom and a space has a score -2. The local alignment algorithm can return a set of conserved segments in the alignment of the protein sequences of atoms.

We have done many experiments and found that the set of conserved segments output by the local sequence alignment algorithm always contains the pairs of binding sites in the SitesBase. The only problem is that the local sequence alignment algorithm outputs too many matched atoms. Next, we will further reduce the matched atoms. After obtaining the set of conserved segments from the local sequence alignment, we focus on the columns with identical pairs of atoms and ignore the rest of columns in the following steps.

Step 2: Identifying surface segments

Inspired by the work in [13], we propose the following method to find surface segment of proteins. First, the protein is projected onto 3D grid in the Euclidean space. For the grid, we use a step size of 1Å. Second, grid points are marked as interior, surface or empty. A grid point is marked as protein if the point is within 2Å distance of an atom in the protein. A grid point is marked as empty if it is not protein point. A grid point is marked as interior if all its six neighbor grid points are protein points. A grid point is marked as surface if at least one of its six neighbor grid points is not protein point. An atom in the protein is a surface atom if it is within distance 1.5Å of a surface point. Figure 2 gives an example, where the dark grid points are surface points.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-S10-S2/MediaObjects/12859_2012_Article_5207_Fig2_HTML.jpg
Figure 2

The surface grid points are indicated by the dark points.

For a conserved segment output by the local sequence alignment algorithm, we consider all its subsegments containing at least 15 matched pairs of atoms. For such a subsegment, if both sequences on this subsegment have at least 2/3 atoms as the surface atoms, we treat such a subsegment as a candidate binding site for further processing in the next step.

Step 3: Computing rigid transformations to match candidate binding sites

For any candidate binding sites obtained from Step 2, we will further test if the pair of 3D substructures can match well on such a site. Precisely, we can find the set of subsegments in a given segment with alignment  A using the following rule: there exists a rigid transformation such that the distance between each pair of atoms in the same column of the subsegment is at most d, where d is a parameter given by the user. A rigid transformation is a transformation for protein 3D structure in the 3D space that preserves distances between any pair of points in the structure of protein. This requires us to solve the following protein 3D structure matching problem:

Input: A segment with sequence alignment  A of two proteins, where each position in the alignment has two identical atoms, the 3D coordinate of each atom in the alignment, and a threshold d.

Goal: Find a set of subsegments with alignment  A such that for each output subsegment the Euclidean distance between each pair of atoms in the same column is at most d.

The protein 3D structure matching problem can be solved in several ways. Here we use the method in [14] which is a faster version of the method in [15] to solve the problem. The method in [14] can compute a rigid transformation such that the distance between each matched pair of atoms is at most (1+)d, where = 0.1 is a parameter to control the precision of the transformation. This is just an approximate rigid transformation, and it is good enough in practice.

Testing the overlap of the proteins in 3D space

When computing the rigid transformation, we also require that the proteins do not overlap under the transformation. For each rigid transformation that can match the substructures of the candidate subsegment, we test if the proteins have overlap in 3D space under such a transformation as follows:

1. Construct the grid in 3D space and mark each grid point as interior, surface or empty as in Step 2 with respect to each of the given proteins.

2. Let X be the number of grid points that are interior points for both proteins, X1 and X2 be the number of interior points of the first protein and the second protein, respectively. If X ≤ 0.05 × min{X1, X2}, then we say that there is no overlap between proteins under the current rigid transformation and we output the matched substructures as the predicted binding sites.

Results

Comparison with existing methods

In this section, we compare our program BsFinder with three existing programs SiteEngine, SuMo, and pdbFun. They use different methods to predict the binding sites of given proteins. SiteEngine [16] is a method that recognizes the regions on the surface of one protein that are similar to the binding sites of another, and geometric hashing triangles are used to transfer the input sites into the recognized region [8]. SuMo [17] is a system for finding binding sites onto query structures, by comparing the structure of triplets of chemical groups against the binding sites found in PDB database [9]. The web server pdbFun [18] locates binding sites in proteins at the residue level, and it analyzes structural similarity between any pair of residue selections [10].

To compare BsFinder with the three existing systems, we use the proteins in PDB database, and select 55 proteins to compare with the whole database. Note that the Structural Classification of Proteins (SCOP) database [19] in [20] aims to provide a detailed and comprehensive description of the structural and evolutionary relationships between all proteins whose structures are known. It provides 11 classes to separate all known protein folds. Each class contains several different families. We choose 5 proteins from each class in different families such that there is only one entry from each family. Since BsFinder allows users to give the value of d, we set the threshold d = 1.5Å and output the matched sites with at least 15 atoms.

Evaluation of prediction

To calculate the precision and recall value for each approach, we need to know which pair of binding sites output by the programs is real. Here we look at SitesBase [21] in [11], which holds the set of known binding sites found in PDB. The precision value is defined as the number of sites output by the program that are confirmed in SitesBase divided by the total number of sites output by the program, where a output site is confirmed in SitesBase if at least two residues of the output sites are the same as the binding sites in SitesBase. As the sites output by SuMo are very short, the sites output by SuMo are confirmed if each one has at least one residue which is identical to that in SitesBase. Ideally, all the sites output by the program are confirmed in SitesBase, in the case, the precision value is 100%. Apparently, the larger the precision value is, the better the program is. The recall value is defined as the number of sites output by the program that are confirmed in SitesBase divided by the total number of binding sites more than two complete residues for given proteins in SitesBase. If all the binding sites for given proteins in the SitesBase can be output by the program, then the recall value is 100%. Again, the larger the recall value is, the better the program is.

We use the 55 selected proteins to compare with the whole PDB database. The results are shown in Table 1. The average numbers of the sites output by BsFinder, SiteEngine, SuMo, and pdbFun are 6425, 6003, 6329, and 1936, respectively. On average, pdbFun reports the smallest number of sites and the other three systems output approximately the same number of sites. The average numbers of the confirmed sites output by BsFinder, SiteEngine, SuMo, and pdbFun are 2218, 1265, 674, and 281, respectively. See Figure 3(a).
Table 1

Comparison of BsFinder, SiteEngine, SuMo, and pdbFun on 55 proteins.

 

BsFinder

SiteEngine

SuMo

pdbFun

 

number

ratio(%)

number

ratio(%)

number

ratio(%)

number

ratio(%)

1C52

5937/1583

27/73

4798/1212

25/56

1014/159

16/8

2621/627

24/29

8GSS

6111/3243

53/95

4702/1918

41/57

4698/1603

34/48

2704/951

35/28

256B

7834/3102

40/89

3982/1410

35/41

664/100

15/3

798/221

28/7

8ICK

8984/3758

42/97

8165/1848

23/48

9398/1526

16/40

2208/372

17/10

4VHB

7122/2299

32/78

3750/855

23/30

1500/167

11/6

1014/251

25/9

2BPV

6210/1309

21/85

4689/717

15/47

842/66

8/5

1230/151

12/10

2RTO

5540/2463

44/69

3612/1350

37/38

2542/871

34/24

1744/173

10/5

2TRM

4528/1268

28/53

6984/1107

16/46

10126/919

9/38

2348/331

14/14

2XAT

6041/2783

46/72

4506/1046

23/27

4721/856

18/22

1963/230

12/6

1JJU

5074/936

18/79

5685/616

11/53

9785/381

4/33

5328/429

8/37

4FX2

5603/2667

48/68

3318/1064

32/28

1878/583

31/15

1833/439

24/12

5P21

7179/3401

47/87

6017/1998

33/52

7556/1702

23/44

2097/659

31/17

2DUB

8681/2764

32/89

7226/1734

24/57

2641/473

18/16

2673/531

20/18

3MAN

7469/3536

47/91

8974/2102

23/55

9487/1627

17/43

2371/983

42/26

6DFR

7541/3621

48/93

8054/2479

31/64

3682/877

24/23

2318/670

29/18

1J6W

8543/2361

28/88

4812/1324

28/54

2762/314

12/13

1133/276

25/11

3PYP

5733/3347

58/87

3043/932

31/25

1841/529

29/14

1713/358

21/9

1E1V

8719/2148

24/85

6704/1064

16/48

8505/882

11/39

1943/195

10/9

1OIY

8452/2981

35/92

8104/1884

23/59

8441/1121

13/35

1844/280

15/9

3BU4

1407/874

62/84

3916/948

24/38

3945/599

15/24

1089/48

4/2

1T9G

7092/2526

36/84

8927/1714

19/58

9590/1073

11/36

2243/318

14/11

7CAT

6813/1564

23/87

7241/1483

21/83

14407/875

6/49

2375/376

16/21

1JX4

5294/497

9/94

5576/314

6/60

5637/198

4/38

1843/65

4/12

1CY6

5791/477

8/95

8485/326

4/66

11855/220

2/44

2793/85

3/17

1SK6

3267/457

14/82

9713/368

4/75

17100/345

2/70

2094/79

4/16

1H2S

8437/2263

27/95

3567/967

27/41

3079/497

16/21

2912/211

8/9

1DDT

8071/1921

24/89

7446/1324

18/65

11301/904

8/45

3428/162

5/8

1U19

8186/3523

43/92

9795/2057

21/54

11298/1629

14/43

2619/508

19/13

1PPJ

6638/377

6/94

9657/332

4/83

10634/146

2/37

3509/59

2/15

1NTM

6263/916

15/87

6640/421

6/43

13164/362

3/37

3357/48

2/5

7INS

6327/3155

50/82

5827/1750

30/46

682/38

6/1

169/47

28/2

1KI0

8011/3356

42/91

7502/2014

27/55

8999/1601

18/43

876/240

27/7

1PTR

5386/1696

31/67

5503/1349

25/54

1849/296

16/12

743/94

13/4

1GMN

7970/3474

44/88

7706/2014

26/53

5955/1053

18/27

733/261

36/7

1F4L

8445/1964

23/94

8683/1459

17/71

13102/1078

8/52

3199/435

14/21

1G9B

8738/2779

32/89

5786/1959

34/68

3542/382

11/13

4354/837

19/29

1JSH

3183/1365

43/45

4251/726

17/24

8483/1246

15/41

1735/537

31/18

1MG1

8162/1220

15/91

9897/952

10/74

11225/573

5/44

2559/191

7/15

1S1C

7999/3827

48/97

7460/2382

32/62

6037/1299

22/34

1898/532

28/14

1KWX

7995/2236

28/95

6022/1765

30/75

5255/452

9/19

1831/201

11/9

1IZL

8155/3676

45/96

7196/1637

23/43

2632/477

18/15

928/244

26/7

1DWL

6558/3201

49/83

3695/1269

35/33

246/45

18/1

317/100

32/3

1FFX

6770/902

13/87

7392/422

6/41

16061/469

3/45

2699/76

3/7

3LDH

6086/2762

45/75

6013/1890

32/52

11994/1464

12/40

1924/321

17/9

2YHX

8213/2737

33/91

8279/1642

20/54

9127/1186

13/39

2250/169

8/6

1GO9

5165/2661

52/67

3571/1210

34/31

30/1

3/1

359/135

38/4

1HTH

3321/1636

49/42

2147/896

42/23

55/7

13/1

58/20

34/1

1LXF

6121/1034

17/78

4831/731

15/55

2945/145

5/11

733/25

4/2

2PRG

6147/832

14/86

6298/718

12/74

6151/190

3/20

1873/71

4/8

1H2K

964/43

4/94

2779/26

2/58

8038/108

1/40

2017/18

1/40

1G8X

6989/3605

52/91

8935/2029

23/54

16085/2318

14/62

5733/797

14/21

1JY4

4207/2171

52/56

2678/756

28/20

129/35

27/1

105/49

5/1

1K09

3123/638

20/82

1201/291

24/38

123/4

3/1

97/16

16/2

1ABZ

2825/2432

86/63

1354/717

53/19

44/1

2/1

255/93

36/3

1L6X

8142/3627

45/96

7036/2059

29/55

5128/1005

20/27

858/355

41/10

The first number is the number of output sites reported by the program, the second number is the number of confirmed sites reported by the program;

The first number is the precision value (%) for the program, the second number is the recall value (%) for the program;

https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-S10-S2/MediaObjects/12859_2012_Article_5207_Fig3_HTML.jpg
Figure 3

Comparison of BsFinder, SiteEngine, SuMo, and pdbFun on 55 proteins. (a)The average numbers of the output sites (black bar) and the confirmed sites (gray bar) for BsFinder, SiteEngine, SuMo, and pdbFun; (b)The average values of precision (black bar) and recall (gray bar) for BsFinder, SiteEngine, SuMo, and pdbFun.

The precision and recall values for 55 proteins output by four programs are shown in Table 1. Apparently, BsFinder has the largest precision and recall values for most of the cases. On average, the precision value of BsFinder is 34% while the precision values for SiteEngine, SuMo, and pdbFun are 21%, 11%, and 15%, respectively. The average recall value of BsFinder is 82% while the average recall values for SiteEngine, SuMo, and pdbFun are 47%, 25%, and 11%, respectively. See Figure 3(b). The value of recall is very important in practice. From the experiment results, we know that the existing programs have lower values of recall.

The possible reasons that our method can get better results might be (1) we use the surface information, (2) we look at the similarity of two local 3D substructures in terms of rigid transformation while the previous methods use triples of atoms or pairs of amino acids and (3) the volumes of the protein molecules are considered when the rigid transformation is computed.

Comparison of running time

To compare the running time of different programs, we use a Pentium(R) 4 (CPU of 2.40 GHz) to run all four programs. Based on 55 selected proteins, the average running times of BsFinder, SiteEngine, SuMo, and pdbFun for comparing each given protein with the whole PDB database are roughly 50 minutes, 70 minutes, 30 minutes, and 5 minutes, respectively. See Table 2. Thus, BsFinder is the second slowest program. However, it is still faster than SiteEngine which has the highest average values of precision and recall among the three existing programs.
Table 2

Comparison of four programs.

 

Running Time

Precision

Recall

BsFinder

50 minutes

34%

82%

SiteEngine

70 minutes

21%

47%

SuMo

30 minutes

11%

25%

pdbFun

5 minutes

15%

11%

Performance of programs for different families

To see the performance of programs for different protein families, we look at three different families (G proteins family in P-loop folds, PYP-like family in Profilin-like folds, and FAD-linked reductases family in FAD/NAD(P)-binding folds) and select five proteins from each family. The average numbers of matched sites output by BsFinder for three families are 7680, 5289, and 7892, respectively. The average numbers of confirmed sites for three families are 3487, 1132, and 4138, respectively. The average precision values for three families are 45%, 21% and 53%, respectively. The average recall values for three families are 94%, 60% and 96%, respectively. The results are shown in Figure 4.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-S10-S2/MediaObjects/12859_2012_Article_5207_Fig4_HTML.jpg
Figure 4

Results of BsFinder on three different families. (a)The average numbers of the output sites (black bar) and the confirmed sites (gray bar) for three different families; (b)The average values of precision (black bar) and recall (gray bar) for three different families.

G proteins family in P-loop folds

We select 5 proteins (1A2B, 1CXZ, 1DPF, 1FTN, 1S1C) from G proteins family in P-loop folds. The results are shown in Table 3. The precision values of BsFinder (48%, 46%, 43%, 42% and 47%) are larger than those of other three programs. The recall values of BsFinder (95%, 93%, 92%, 91% and 99%) are more than 90%, while the recall values of the other three programs are almost less than 40%.
Table 3

Comparison of the four programs on G proteins family in P-loop folds.

 

BsFinder

SiteEngine

SuMo

pdbFun

 

number

ratio(%)

number

ratio(%)

number

ratio(%)

number

ratio(%)

1A2B

7601/3647

48/95

6579/1717

26/40

6375/1209

19/29

1787/381

21/9

1CXZ

7832/3602

46/93

7425/1480

20/35

8388/1433

18/34

2696/403

15/10

1DPF

7537/3241

43/92

5975/1343

23/34

4702/1029

22/26

1993/365

18/10

1FTN

7435/3121

42/91

7147/1471

21/35

8599/1328

16/31

2232/414

19/11

1S1C

7995/3827

47/99

7460/1382

19/36

6037/1299

22/34

1898/532

28/14

The first number is the number of output sites reported by the program, the second number is the number of confirmed sites reported by the program;

The first number is the precision value (%) for the program, the second number is the recall value (%) for the program;

PYP-like family in Profilin-like folds

We select 5 proteins (1D7E, 1F9I, 1KOU, 1NWZ, 2PHY) from PYP-like family in Profilin-like folds. The results are shown in Table 4. The precision values of BsFinder (17%, 18%, 24%, 25% and 21%) are similar to those of the other three programs. The recall values of BsFinder (58%, 64%, 59%, 63% and 57%) are larger than that of the other three programs.
Table 4

Comparison of the four programs on PYP-like family in Profilin-like folds.

 

BsFinder

SiteEngine

SuMo

pdbFun

 

number

ratio(%)

number

ratio(%)

number

ratio(%)

number

ratio(%)

1D7E

4845/834

17/58

5017/698

14/48

2582/173

7/13

223/33

15/3

1F9I

5771/1068

18/64

5680/740

13/44

3405/224

7/14

203/13

7/1

1KOU

5352/1297

24/59

4521/896

20/41

2421/264

11/13

916/80

9/4

1NWZ

5027/1279

25/63

5497/914

17/45

2096/243

12/13

206/23

12/2

2PHY

5451/1189

21/57

4014/821

20/39

3178/285

9/14

208/15

8/1

The first number is the number of output sites reported by the program, the second number is the number of confirmed sites reported by the program;

The first number is the precision value (%) for the program, the second number is the recall value (%) for the program;

FAD-linked reductases family in FAD/NAD(P)-binding folds

We select 5 proteins (1B4V, 1B8S, 1COY, 1IJH, 3COX) from FAD-linked reductases family in FAD/NAD(P)-binding folds. The results are shown in Table 5. The precision values of BsFinder (54%, 52%, 53%, 53% and 54%) are all more than 50%. The recall values of BsFinder (97%, 96%, 96%, 96% and 98%) are very close to 100%.
Table 5

Comparison of the four programs on FAD-linked reductases family in FAD/NAD(P)-binding folds.

 

BsFinder

SiteEngine

SuMo

pdbFun

 

number

ratio(%)

number

ratio(%)

number

ratio(%)

number

ratio(%)

1B4V

7835/4138

54/97

7017/1998

29/47

11797/1925

17/46

6894/2381

34/56

1B8S

7996/4101

52/96

7680/1940

26/46

11929/1865

16/44

9408/2843

31/68

1COY

7892/4135

53/96

8521/1996

24/47

11989/1864

16/43

9407/2857

31/67

1IJH

7859/4119

53/96

8497/2014

24/47

11427/1894

17/45

9424/2662

29/63

3COX

7878/4199

54/98

8014/2021

26/48

11647/1775

16/42

9245/2850

31/67

The first number is the number of output sites reported by the program, the second number is the number of confirmed sites reported by the program;

The first number is the precision value (%) for the program, the second number is the recall value (%) for the program;

A Case: We compare two proteins 4VHBA and 1CQXA. The cartoon version of the protein 3D structures are shown in Figure 5, and the matched parts of structures are shown as the sticks fashion. BsFinder finds a rigid transformation that matches residues 84-86 from 4VHBA to residues 84-86 from 1CQXA, residues 95-100 from 4VHBA to residues 95-100 from 1CQXA, and residues 125-128 from 4VHBA to residues 125-128 from 1CQXA. See Figure 6. The three pairs of matched sites are confirmed in SitesBase. Note that these three pairs can be matched under one rigid transformation simultaneously.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-S10-S2/MediaObjects/12859_2012_Article_5207_Fig5_HTML.jpg
Figure 5

The 3D structures of proteins 4VHBA (a) and 1CQXA (b).

https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-S10-S2/MediaObjects/12859_2012_Article_5207_Fig6_HTML.jpg
Figure 6

The similar sites for 4VHBA and 1CQXA predicted by BsFinder. (a)residues 84-86 from 4VHBA and residues 84-86 from 1CQXA; (b) residues 95-100 from 4VHBA and residues 95-100 from 1CQXA; (c) residues 125-128 from 4VHBA and residues 125-128 from 1CQXA.

Searching similar binding sites

BsFinder can use a binding site to search the similar sites in the protein structures database. SiteEngine can search a given functional site on a large set of complete protein structures. SuMo can search for the given 3D site of interest among the structures of the PDB. PAST [22] is a web service based on an adaptation of the generalized suffix tree and relies on a linear representation of the protein backbone [23]. PAST can find the functional sites from the protein structures database similar to the given binding site.

We randomly select the 100 binding sites with different types from the SitesBase and search the whole PDB database. The average numbers of the sites output by BsFinder, SiteEngine, SuMo, and PAST are 274, 266, 399, and 281, respectively. The average numbers of the confirmed sites output by BsFinder, SiteEngine, SuMo, and PAST are 106, 73, 72, and 58, respectively. See Figure 7(a). BsFinder finds a relatively smallest number of output sites, and the number of confirmed sites output by BsFinder is the biggest. Apparently, BsFinder has the largest precision and recall values for most of the cases. On average, the precision value of BsFinder is 39% while the precision values for SiteEngine, SuMo, and PAST are 27%, 22%, and 24%, respectively. The average recall value of BsFinder is 86% while the average recall values for SiteEngine, SuMo, and PAST are 58%, 51%, and 45%, respectively. See Figure 7(b).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2105-13-S10-S2/MediaObjects/12859_2012_Article_5207_Fig7_HTML.jpg
Figure 7

Comparison of BsFinder, SiteEngine, SuMo, and PAST on 100 sites. (a)The average numbers of the output sites (black bar) and the confirmed sites (gray bar) for BsFinder, SiteEngine, SuMo, and PAST; (b)The average values of precision (black bar) and recall (gray bar) for BsFinder, SiteEngine, SuMo, and PAST.

Discussion

The gaps in binding sites

In the first step of our algorithm, we do sequence alignment where each letter is an atom. This allows the matched sites to have some missed atoms, and each missed atom represents one gap in the binding sites. Step 1 is very important for predicting binding sites on proteins. Among the output sites, 67127 of them do not contain any gap, 63593 contain one gap, 77725 contain two gaps, 81259 contain three gaps, 38863 contain four gaps, 21198 contain five gaps and 3533 contain more than five gaps. The gap distribution of the confirmed sites are 18285 (no gap), 19504 (one gap), 26809 (two gaps), 26809 (three gaps), 15847 (four gaps), 12197 (five gaps) and 2452 (more than five gaps). The confirmed sites have higher proportion of the four or more gaps among all output sites reported by BsFinder.

The power of surface detection

In Step 2 of our algorithm, we identify the surface atoms in the given proteins and rule out the substructures in which less than 2/3 of atoms are the surface atoms for further calculation of the rigid transformation. To demonstrate the effect of Step 2, we compare the final version of BsFinder with the version without Step 2. By adjusting the parameters, the final version of BsFinder has improved precision value while the recall value remains essentially unchanged. The average precision values for BsFinder without Step 2 and the final version of BsFinder are 29% and 34%, respectively. The average recall values for BsFinder without Step 2 and the final version of BsFinder are 83% and 82%, respectively. Therefore, by doing Step 2 the precision value can be improved by about 5%. This is a significant improvement.

Conclusions

We have developed a program for finding binding sites on the given proteins. Our method uses the 3D structure information to detect the similar surface regions. Experiments show that our program outperforms all existing programs.

Declarations

Acknowledgements

This article has been published as part of BMC Bioinformatics Volume 13 Supplement 10, 2012: "Selected articles from the 7th International Symposium on Bioinformatics Research and Applications (ISBRA'11)". The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/13/S10.

This work is supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 121608].

Authors’ Affiliations

(1)
School of Computer Science and Technology, Shandong University
(2)
Department of Computer Science, City University of Hong Kong

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Copyright

© Guo and Wang; licensee BioMed Central Ltd. 2012

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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