- Research article
- Open Access

# Impact of residue accessible surface area on the prediction of protein secondary structures

- Amir Momen-Roknabadi
^{1, 3}, - Mehdi Sadeghi
^{2, 3}, - Hamid Pezeshk
^{4}Email author and - Sayed-Amir Marashi
^{1, 5, 6}

**9**:357

https://doi.org/10.1186/1471-2105-9-357

© Momen-Roknabadi et al; licensee BioMed Central Ltd. 2008

**Received:**09 December 2007**Accepted:**31 August 2008**Published:**31 August 2008

## Abstract

### Background

The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method.

### Results

We studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained.

### Conclusion

The success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.

## Keywords

- Prediction Accuracy
- Secondary Structure Prediction
- Protein Secondary Structure
- Good Prediction Accuracy
- Relative Solvent Accessibility

## Background

The problem of accurate prediction of protein three-dimensional structure continues to be one of the challenging problems in Bioinformatics. The large-scale genome sequencing efforts have made this problem even more significant. Roughly 50% of the proteins in a genome have at least one homolog in protein structure databases and their structure can be predicted efficiently by homology modeling [1, 2]. However, for the other half of the sequences no structural template is currently known. To date, the performance of *ab initio* three dimensional prediction methods are still far from being perfect [3–5]. Therefore, in order to obtain information about the structure of a novel protein, one may consider simpler tasks, like one dimensional prediction of protein characteristics [6]. Acquiring such information is a key step in understanding the relationship between the protein folding and protein primary structure. The goal of protein secondary structure (SS) prediction methods is to predict whether each residue is in a helical structure (H), a strand (E), or in other structures (traditionally referred to as coil, C).

In the past decades, many prediction methods based on the database of known protein structures have been developed. Historically, the first generation of the SS prediction algorithms was developed by Chou and Fasman. [7, 8] This algorithm, which is usually referred to as the Chou-Fasman method, tries to find structures based on the difference in the probability of observing each of the twenty residues in helices, sheets and other structures. This method has an accuracy of about 50–60% [7, 8], although it has been shown that this method can be improved greatly with the application of several amendments [9]. It should be noted that other statistical methods (mainly based on hidden Markov models) have been also applied for protein SS prediction [10, 11] and it seems that their prediction accuracies are comparable to current methods.

The second generation of SS prediction methods started by the method of Garnier, Osguthorpe and Robson (GOR method) [12] and improved in several steps [13]. This method, with an information theory approach, relates sequence to SS type and evaluates the state of each residue with a sliding window approach. Using this approach, better prediction accuracies, up to 64%, can be obtained [14].

The third generation methods use multiple sequence alignment and machine learning techniques like nearest neighbors and neural networks to predict the secondary structure. APSSP [15], JPred [16], SSpro [17], PHD [18], PSIpred [19], PMSVM [20], and other methods based on support vector machines [21–23] can be considered as the representatives of this generation. These methods generally achieve very good prediction accuracy, of up to 76%. It should be noted that recently, achievement of 80% accuracy is reported using a large-scale training [24].

Some years ago, it was thought that improvement of the methods will steadily result in the improvement of the SS prediction accuracy in the future [25], but now it seems that there is some kind of "barrier" that prevents all the above mentioned approaches to leave the 80% accuracy behind, and approach the theoretical prediction limit, which is estimated to be about 88% [26] or maybe up to 90–95% [27]. One possible barrier for SS prediction might lie in the neglect of other factors that may influence the tendencies of amino acids for being in different secondary structures. For example, it has been reported that amino acid propensities for secondary structures are influenced by the protein structural class [28, 29], and by the organism from which the proteins are obtained [30].

It has been previously suggested that more accurate SS predictions can be achieved by taking relative solvent accessibility (RSA) into account [31–33]. The logic for the usefulness of such information lies in the fact that the environments around the protein residues can affect their propensities for different structures [34], and therefore, amino acids may behave differently when they are in the protein interior vs. surface of protein [35–39]. This effect is extensively studied in case of internal and surface beta-strands [40].

Based on these observations, one may ask why RSA is not routinely used today in the prediction of protein secondary structures. The answer lies in the fact that RSA prediction is not an easy task itself. The two original reports simply used DSSP [41] assignments to extract RSA information [32, 33]. However, in the real-world version of the problem, protein sequence is almost always the only available information. For that reason, it was later tried to predict real-value RSAs [42, 43] and to apply it for the improvement of protein SS prediction, in a method called SABLE [31]. While the performance of SABLE seems to be very good (i.e. 79.6% accuracy in CASP 6; see http://sable.cchmc.org/sable_doc.html), there seems to be much room for improvement of the method, as SABLE relies on an RSA prediction method with a correlation coefficient of 0.66 [31].

In the present work, we investigate the effect of the alteration of the RSA threshold on prediction accuracy. Our results imply that significant improvements in the prediction of SS can be obtained if the RSA cutoffs are selected according to the residues. We also discuss why predicted real-value RSAs might not be suitable for the improvement of SS prediction at this moment. Finally, we suggest that RSA prediction should be combined with the present SS prediction techniques, since the addition of RSA information improves the prediction, independent of the prediction approach.

## Results and discussion

### The effect of application of different RSA thresholds on the prediction of secondary structures

It was previously reported that when a 25% threshold for predicted RSA values is used to classify residues into {*B*, *Ex*} classes (i.e. Buried vs. Exposed; see Materials and Methods), this additional information increases the accuracy of SS prediction [31]. We decided to try other thresholds to see how they affect the predictions.

As an additional test, we also divided amino acids into three discrete groups, i.e. we classified the residues to buried, intermediate and exposed, [35]. For each classification, therefore, a fixed threshold pair is used. The results for these methods are presented in the Additional file 5. The results generally show that classification into three groups yields a better result compared to a two-group classification. Among the tested classifications, namely [4%,16%], [9%,16%], [9%,36%] and [16%,36%], the first pair was the best choice for all methods.

While these results prove that the addition of RSA information with a fixed cutoff is not a good recipe for improvement of SS prediction, it clearly shows that one should choose different thresholds for different amino acids (see below).

### Application of residue-specific RSA thresholds for the improvement of secondary structure prediction

In the previous section, we have shown that with the application of a fixed threshold one cannot obtain improvement for all residues. This is something previously observed by Macdonald and Johnson [32], who reported that proline (P) is always considered "buried" in their analysis (they used a fixed threshold of 50% for RSA). Since with the selection of a fixed RSA threshold the predictions of all residues are not improved, we decided to consider "residue-specific" RSA thresholds.

We tested the usefulness of "mean RSA" and "median RSA", i.e. to assume them as the thresholds for each residue *X*. We first obtained the actual distribution of RSA values for each of the twenty amino acids, and then calculated the mean and the median of each of these distributions (see Additional file 6). Then, in two separate tests, the mean and the median were used as residue-specific RSA thresholds.

Improvement of protein secondary structure prediction with the addition of a "residue-specific" RSA threshold using leave-one-out cross-validation, compared with this improvement using a fixed 16% RSA threshold.

Applied Threshold | |||
---|---|---|---|

Fixed (16%) | Mean | Median | |

A | 0.67 | 3.93 | 2.25 |

C | -5.02 | -0.05 | -0.42 |

D | -1.5 | -1.26 | -0.94 |

E | -2.77 | 1.33 | -3.71 |

F | 0.18 | 5.99 | 7.15 |

G | 0.90 | 5.98 | 5.53 |

H | 0.04 | -4.23 | -4.80 |

I | -19.00 | -16.19 | -16.17 |

K | 9.53 | 9.91 | 11.63 |

L | 0.54 | 4.20 | 2.24 |

M | -6.82 | -7.71 | -8.29 |

N | 1.21 | 1.71 | 1.63 |

P | 1.15 | 1.70 | 1.47 |

Q | -0.61 | -1.49 | -3.38 |

R | 1.84 | 0.87 | 1.15 |

S | 1.29 | 6.44 | 4.85 |

T | 0.20 | 3.12 | 2.53 |

V | -2.57 | -5.80 | -8.33 |

W | -6.18 | -1.66 | -2.22 |

Y | -0.19 | 10.04 | 9.78 |

Total Improvement | 3.46 | 5.79 | 5.13 |

We then studied the effect of consideration of three-state residue specific RSA information in SS prediction problem. We tested two types of thresholds again. For the first analysis we chose (mean + SD) and (mean - SD) of the RSA distributions as the selected pair of thresholds. For the second analysis, in case of each amino acid RSA distribution, two RSA values, *t*_{1} and *t*_{2} were selected so that one-third and two-third of the observations were smaller than *t*_{1} and *t*_{2}, respectively. We will refer to *t*_{1} and *t*_{2} as the first tertile and the second tertile, respectively. These values are summarized in Additional file 6.

Improvement of protein secondary structure prediction with the addition of two "residue-specific" RSA thresholds, compared with this improvement using a fixed [4%, 16%] RSA threshold.

Applied Threshold | |||
---|---|---|---|

Fixed([4%,16%]) | Mean ± standard deviation | Tertiles | |

A | -3.03 | -0.83 | -0.93 |

C | 2.63 | 1.92 | 0.74 |

D | -0.95 | 1.70 | 1.50 |

E | -2.54 | -1.02 | 3.98 |

F | 0.71 | 10.14 | 9.44 |

G | 0.90 | 9.28 | 7.94 |

H | -4.30 | -2.14 | -3.30 |

I | -7.49 | -14.49 | -15.16 |

K | 10.37 | 26.73 | 13.31 |

L | 3.04 | 4.14 | 2.87 |

M | -3.58 | -5.45 | -5.57 |

N | -1.21 | 2.46 | -0.10 |

P | 1.14 | 2.47 | 1.84 |

Q | 0.53 | 0.07 | -0.76 |

R | 2.80 | 5.10 | 3.04 |

S | 4.36 | 13.13 | 12.32 |

T | 3.27 | 8.84 | 5.81 |

V | 1.57 | 0.13 | -5.72 |

W | -2.30 | 0.40 | 0.25 |

Y | 4.17 | 9.73 | 10.30 |

Total Improvement | 5.44 | 8.24 | 7.17 |

Improvement of protein secondary structure prediction with the addition of a "residue-specific" RSA threshold for Chou-Fasman and HMM method.

Applied Threshold | ||||
---|---|---|---|---|

Chou-Fasman | HMM | |||

Mean | Median | Mean | Median | |

A | 11.89 | 10.43 | -5.29 | -5.41 |

C | 3.33 | 1.27 | 4.26 | 4.93 |

D | 11.73 | 10.77 | 5.81 | 6.66 |

E | 9.16 | 8.56 | -3.55 | -3.76 |

F | -0.32 | -0.39 | 1.12 | 1.55 |

G | 9.11 | 6.72 | 12.79 | 14.25 |

H | 10.92 | 12.61 | 2.83 | 3.28 |

I | -0.01 | -1.45 | 0.08 | 0.41 |

K | 8.31 | 5.76 | 0.25 | 0.35 |

L | 1.08 | 1.21 | -3.53 | -3.49 |

M | 0.17 | -0.40 | -3.60 | -3.62 |

N | 8.38 | 8.71 | 7.20 | 8.12 |

P | 12.32 | 10.08 | 11.97 | 13.56 |

Q | 10.35 | 9.07 | -2.55 | -2.53 |

R | 9.67 | 8.32 | -1.21 | -1.10 |

S | 11.61 | 7.89 | 5.07 | 5.79 |

T | 1.68 | 0.16 | 5.22 | 6.10 |

V | -0.23 | -0.61 | 2.20 | 2.50 |

W | -0.57 | -0.71 | -0.78 | -0.84 |

Y | 0.74 | 0.68 | 0.87 | 1.04 |

Total Improvement | 9.99 | 8.69 | 3.37 | 3.92 |

Applied Threshold | ||||

Chou-Fasman | HMM | |||

Tertile | Mean ± standard deviation | Tertile | Mean ± standard deviation | |

A | 12.15 | 12.50 | -4.31 | -2.69 |

C | 2.64 | 1.76 | 3.85 | 2.93 |

D | 13.61 | 13.17 | 8.94 | 6.01 |

E | 10.35 | 9.48 | -1.97 | -1.04 |

F | -0.23 | -1.88 | -0.17 | 1.37 |

G | 9.29 | 8.80 | 18.48 | 13.14 |

H | 12.23 | 11.60 | 4.48 | 3.76 |

I | -0.51 | 0.09 | -0.41 | 0.21 |

K | 8.20 | 8.57 | 1.17 | 1.34 |

L | 0.72 | 0.49 | -3.35 | -1.79 |

M | 1.76 | -0.81 | -1.42 | -1.64 |

N | 8.40 | 8.46 | 10.72 | 7.05 |

P | 12.72 | 14.91 | 17.33 | 11.05 |

Q | 10.40 | 10.65 | -0.73 | -0.66 |

R | 9.57 | 9.99 | 0.28 | 0.37 |

S | 10.10 | 13.09 | 7.41 | 5.28 |

T | 0.52 | 0.60 | 6.44 | 5.30 |

V | -0.28 | -0.20 | 0.97 | 2.02 |

W | -0.87 | -0.63 | -0.50 | 0.44 |

Y | 0.75 | 0.92 | 1.15 | 1.25 |

Total Improvement | 10.23 | 10.34 | 4.32 | 3.62 |

Our results clearly suggest that considerable improvements are obtained in SS prediction independent of the applied method. It is also important to test the validity of this observation for more popular methods like PSIpred[19] and PHD[18], which work based on finding conserved sequences that form regular structures. However, this is not an easy task. Our approach works by changing the twenty-letter alphabet of amino acids; therefore it is not possible to do the BLAST search with BLOSUM, PAM, or any other classical 20 × 20 matrix, as we need mutation matrices in which RSA information is also considered.

Finally, to assess the usefulness of our suggested residue-specific thresholds, we tried to test the effect of considering random thresholds for classification of RSA data. In each simulation, we randomly assigned one or two thresholds to each amino acid and classified the residues into two or three classes respectively. Then, with the addition of RSA information we computed the prediction accuracy. This procedure was repeated 100 times. The results of the simulation are summarized in Additional file 7. It can be observed that in almost all cases the improvement of the accuracy of prediction is not as high as the suggested residue specific thresholds.

### Application of predicted RSA values for the improvement of secondary structure prediction: can we use real-value RSAs?

We demonstrated that RSA information can positively influence the protein SS prediction. However, in practice, we only know the sequence of the protein, and we may only rely on the predicted RSA values for the improvement, not on the actual values.

Adamczak et al. have previously shown that the predicted real-value RSA information can be used to enhance SS prediction [31]. We used predicted values to test the validity of our approach for this case.

For obtaining predicted RSAs we used RVP-net program [44] to predict RSAs for a given protein sequence in our dataset, and then implemented these predicted RSAs into our method.

## Conclusion

In this study we have shown that, combination of actual and predicted RSA greatly improves the prediction of protein secondary structure. In practice, one cannot take advantage of the actual RSA information and it is necessary to use predicted RSA values for this purpose. However, one should notice that RSA prediction methods are still far from being faultless. Therefore, it is critically important to consider the weak points of RSA prediction methods when incorporating their results into SS prediction methods.

## Methods

### Dataset

We used WHATIF [45] PDB selection list, released in January 13, 2007. This dataset contained 6970 chains that have R-factor < 0.25 and resolution < 2.5 Å. The procedure used to generate this dataset was comparable to the PDBselect [46] algorithm, but instead of focusing on maximization of size of the subsets, WHATIF focuses on getting representative structures of the highest available quality. For the WHATIF selection an empirical quality value is defined. This is a composite score depending on the Resolution and the R-factor.

The above dataset was used for training and testing tasks in both the leave-one-out cross-validation and five-fold cross-validation procedure (see below).

### Chou-Fasman method

This method uses a conformational propensity table to predict SS from an input sequence. For each amino acid, this table gives a value describing the given amino acid's propensity to be found in helical structure (H), a strand (E), or in other structures (coil, C). These propensities are calculated by measuring the frequencies of each amino acid associated with a given structure. Then the frequencies were normalized by the prevalence of the amino acid in the dataset.

Using these values, the algorithm looks for "nucleation sites" where either 4 of 6 residues are helix formers or 3 of 5 residues are strand formers. These nucleation sites were then extended as long as the propensity for the given structure remained.

The algorithm also contained additional heuristics for strands, exceptional cases, and others. In this work, these small heuristic amendments are neglected.

In order to add RSA information in this method we classified amino acids into either two or three (i.e. {B(uried), Ex(posed)} or {B(uried), I(ntermediate), Ex(posed)}) discrete groups according to their RSAs. Then, we calculated the propensities of the twenty amino acids, each classified in one of the two or three groups defined based on RSA, and predicted the SS of a given sequence according to this newly built table.

### GOR method

The GOR algorithm [3] and later its newer versions [47], have always been of the most popular methods for SS prediction. The earliest version of GOR had been based on information theory [48], that was introduced by Shannon [49, 50] and Fano [51].

In GOR method, for each residue to be predicted, sum of directional information of eight flanking residues on each side is calculated. To obtain the information values from the dataset, the frequency of each of the twenty amino acids at different positions, up to eight residues on the N-terminal and C-terminal sides, should be calculated.

We used GOR IV [13] algorithm, which takes into account another approximation. In this version of GOR, the assumption is made that certain pair-wise combinations of amino acids in the flanking region, influence the conformation of the central amino acid. Hence the information contents calculation formula somewhat changes.

In order to add RSA in these quantities one must further classify residues. This means that instead of 20 residues in three SS conformation, we have 20 residues in 6 combination of SS conformation and RSA states (for two-state classification i.e. {*H*, *E*, *C*} × {*B*(*uried*), *Ex*(*posed*)}). For three-state classification we have 9 combinations of SS conformation and RSA states, i.e. {*H*, *E*, *C*} × {*B*(*uried*), *I*(*ntermediate*), *Ex*(*posed*)}.

### HMM method

In Hidden Markov Models a stochastic model is trained by several sequences, to estimate the probabilities of emissions and transitions. If stochastic models are trained by sequences that have known structures or known functions, the structures and functions for a new sequence can be determined in a stochastic manner, by calculating the probability of the sequence being generated by the model.

Here we first trained three HMMs of Helix, Strand and Coil by training dataset. In order to train the HMMs we calculated the emission probabilities, the transition probabilities and the initial probabilities by measuring the frequencies of amino acids in each structure and each transition. Then we determined the most probable path of a given sequence using Viterbi algorithm[52]. We tested this system by considering the 20 amino acids as the discrete output symbol of HMMs.

In order to implement RSA in this algorithm we divided amino acids into either two or three discrete groups according to their RSAs and trained our models with the resulting either 40 or 60 states.

### RSA and secondary structure assignment

The secondary structure was assigned using DSSP software [41]. In addition, we used the ASA (Accessible Surface Area) from DSSP to determine RSA of each residue by dividing the corresponding ASA value by the maximum possible ASA for each amino acid.

### RSA prediction

We used RVP-net [44] for predicting RSA values. The output of this program is an RSA value between 0% and 100%. We used this value for classifying residues into either two (Buried, Exposed), or three (Buried, Intermediate, Exposed) classes.

### Cross-validation

#### Leave-one-out cross-validation (LOOCV)

This procedure involves removing one chain from the original training set (which contain 6970 chains), using the remaining chains as the training set and then predicting the SS of the removed chain. This process was repeated until all chains have been left out. The final reported values in this work are actually average values over these 6970 experiments.

#### Five-fold cross-validation

We divide randomly the training set into 5 parts, four of which are used for training and the rest for testing. This process is repeated 10 times to ensure that the order of the chains that are used, do not affect the prediction.

### Accuracy measures for evaluation of prediction

**Q**_{
3
}: Prediction accuracy has been assessed by the percentage of correctly predicted residues (Q_{3}) for a three-state description of secondary structure (Helix, Strand and Coil), where Q_{3} is the percentage of amino acids correctly predicted as helix, sheet, or coil if all amino acids are classified in one of the three groups.

_{3}is calculated using the following formula:

#### Standard deviation

where *X*_{
i
}is our variable, $\overline{X}$ is the mean and n is the total number of observations. In this study we calculate two different standard deviations. The first one that is used in LOOCV is the standard deviation of Q_{3} of 6961 chains and the second one which is used in Five-fold cross-validation is the standard deviation of Q_{3} in 10-time repeated cross-validation.

## Declarations

### Acknowledgements

We would like to thank two anonymous referees for valuable comments and suggestions. We also thank S. Arab and A. Katanforoush (Institute of Biochemistry and Biophysics, University of Tehran) and A. Malekpour, Dr. A. Nowzari-Dalini and Mrs. M. Zare' (School of Mathematics, Statistics and Computer Sciences, University of Tehran) for their assistance and useful comments.

Hamid Pezeshk would like to thank the department of Research Affairs of University of Tehran.

This work was supported in part by a grant from IPM (No. CS 1385-1-02).

## Authors’ Affiliations

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