SVM-Cabins: prediction of solvent accessibility using accumulation cutoff set and support vector machine

A number of methods for predicting levels of solvent accessibility or accessible surface area (ASA) of amino acid residues in proteins have been developed. These methods either predict regularly spaced states of relative solvent accessibility or an analogue real value indicating relative solvent accessibility. While discrete states of exposure can be easily obtained by post prediction assignment of thresholds to the predicted or computed real values of ASA, the reverse, that is, obtaining a real value from quantized states of predicted ASA, is not straightforward as a two-state prediction in such cases would give a large real valued errors. However, prediction of ASA into larger number of ASA states and then finding a corresponding scheme for real value prediction may be helpful in integrating the two approaches of ASA prediction. We report a novel method of obtaining numerical real values of solvent accessibility, using accumulation cutoff set and support vector machine. This so-called SVM-Cabins method first predicts discrete states of ASA of amino acid residues from their evolutionary profile and then maps the predicted states onto a real valued linear space by simple algebraic methods. Resulting performance of such a rigorous approach using 13-state ASA prediction is at least comparable with the best methods of ASA prediction reported so far. The mean absolute error in this method reaches the best performance of 15.1% on the tested data set of 502 proteins with a coefficient of correlation equal to 0.66. Since, the method starts with the prediction of discrete states of ASA and leads to real value predictions, performance of prediction in binary states and real values are simultaneously optimized.     

RVP-net: online prediction of real valued accessible surface area of proteins from single sequences

SUMMARY: RVP-net is an online program for the prediction of real valued solvent accessibility. All previous methods of accessible surface area (ASA) predictions classify amino acid residues into exposure states and named them buried or exposed based on different thresholds. Real values in some cases were generated by taking the mid points of these state thresholds. This is the first method, which provides a direct prediction of ASA without making exposure categories and achieves results better than 19% mean absolute error. To facilitate batch processing of several sequences, a standalone version of this tool is also provided. AVAILABILITY: Online predictions are available at http://www.netasa.org/rvp-net/. Standalone version of the program can be obtained from the corresponding author by E-mail request.     

Prediction of protein accessible surface areas by support vector regression

A novel support vector regression (SVR) approach is proposed to predict protein accessible surface areas (ASAs) from their primary structures. In this work, we predict the real values of ASA in squared angstroms for residues instead of relative solvent accessibility. Based on protein residues, the mean and median absolute errors are 26.0 A(2) and 18.87 A(2), respectively. The correlation coefficient between the predicted and observed ASAs is 0.66. Cysteine is the best predicted amino acid (mean absolute error is 13.8 A(2) and median absolute error is 8.37 A(2)), while arginine is the least predicted amino acid (mean absolute error is 42.7 A(2) and median absolute error is 36.31 A(2)). Our work suggests that the SVR approach can be directly applied to the ASA prediction where data preclassification has been used.     

Prediction of protein surface accessibility with information theory

A new, simple method based on information theory is introduced to predict the solvent accessibility of amino acid residues in various states defined by their different thresholds. Prediction is achieved by the application of information obtained from a single amino acid position or pair-information for a window of seventeen amino acids around the desired residue. Results obtained by pairwise information values are better than results from single amino acids. This reinforces the effect of the local environment on the accessibility of amino acid residues. The prediction accuracy of this method in a jackknife test system for two and three states is better than 70 and 60 %, respectively. A comparison of the results with those reported by others involving the same data set also testifies to a better prediction accuracy in our case.     

Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression

A multiple linear regression method was applied to predict real values of solvent accessibility from the sequence and evolutionary information. This method allowed us to obtain coefficients of regression and correlation between the occurrence of an amino-acid residue at a specific target and its sequence neighbor positions on the one hand, and the solvent accessibility of that residue on the other. Our linear regression model based on sequence information and evolutionary models was found to predict residue accessibility with 18.9% and 16.2% mean absolute error respectively, which is better than or comparable to the best available methods. A correlation matrix for several neighbor positions to examine the role of evolutionary information at these positions has been developed and analyzed. As expected, the effective frequency of hydrophobic residues at target positions shows a strong negative correlation with solvent accessibility, whereas the reverse is true for charged and polar residues. The correlation of solvent accessibility with effective frequencies at neighboring positions falls abruptly with distance from target residues. Longer protein chains have been found to be more accurately predicted than their smaller counterparts.     

Context dependent reference states of solvent accessibility derived from native protein structures and assessed by predictability analysis

Background  

Solvent accessibility (ASA) of amino acid residues is often transformed from absolute values of exposed surface area to their normalized relative values. This normalization is typically attained by assuming a highest exposure conformation based on extended state of that residue when it is surrounded by Ala or Gly on both sides i.e. Ala-X-Ala or Gly-X-Gly solvent exposed area. Exact sequence context, the folding state of the residues, and the actual environment of a folded protein, which do impose additional constraints on the highest possible (or highest observed) values of ASA, are currently ignored. Here, we analyze the statistics of these constraints and examine how the normalization of absolute ASA values using context-dependent Highest Observed ASA (HOA) instead of context-free extended state ASA (ESA) of residues can influence the performance of sequence-based prediction of solvent accessibility. Characterization of burial and exposed states of residues based on this normalization has also been shown to provide better enrichment of DNA-binding sites in exposed residues.     

Two-stage support vector regression approach for predicting accessible surface areas of amino acids

We address the problem of predicting solvent accessible surface area (ASA) of amino acid residues in protein sequences, without classifying them into buried and exposed types. A two-stage support vector regression (SVR) approach is proposed to predict real values of ASA from the position-specific scoring matrices generated from PSI-BLAST profiles. By adding SVR as the second stage to capture the influences on the ASA value of a residue by those of its neighbors, the two-stage SVR approach achieves improvements of mean absolute errors up to 3.3%, and correlation coefficients of 0.66, 0.68, and 0.67 on the Manesh dataset of 215 proteins, the Barton dataset of 502 nonhomologous proteins, and the Carugo dataset of 338 proteins, respectively, which are better than the scores published earlier on these datasets. A Web server for protein ASA prediction by using a two-stage SVR method has been developed and is available (http://birc.ntu.edu.sg/~ pas0186457/asa.html).     

RSARF: prediction of residue solvent accessibility from protein sequence using random forest method

Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction. In this work, we propose a random forest method, RSARF, to predict residue accessible surface area from protein sequence information. The training and testing was performed using 120 proteins containing 22006 residues. For each residue, buried and exposed state was computed using five thresholds (0%, 5%, 10%, 25%, and 50%). The prediction accuracy for 0%, 5%, 10%, 25%, and 50% thresholds are 72.9%, 78.25%, 78.12%, 77.57% and 72.07% respectively. Further, comparison of RSARF with other methods using a benchmark dataset containing 20 proteins shows that our approach is useful for prediction of residue solvent accessibility from protein sequence without using structural information. The RSARF program, datasets and supplementary data are available at http://caps.ncbs.res.in/download/pugal/RSARF/.     

Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only

Residue depth (RD) is a solvent exposure measure that complements the information provided by conventional accessible surface area (ASA) and describes to what extent a residue is buried in the protein structure space. Previous studies have established that RD is correlated with several protein properties, such as protein stability, residue conservation and amino acid types. Accurate prediction of RD has many potentially important applications in the field of structural bioinformatics, for example, facilitating the identification of functionally important residues, or residues in the folding nucleus, or enzyme active sites from sequence information. In this work, we introduce an efficient approach that uses support vector regression to quantify the relationship between RD and protein sequence. We systematically investigated eight different sequence encoding schemes including both local and global sequence characteristics and examined their respective prediction performances. For the objective evaluation of our approach, we used 5-fold cross-validation to assess the prediction accuracies and showed that the overall best performance could be achieved with a correlation coefficient (CC) of 0.71 between the observed and predicted RD values and a root mean square error (RMSE) of 1.74, after incorporating the relevant multiple sequence features. The results suggest that residue depth could be reliably predicted solely from protein primary sequences: local sequence environments are the major determinants, while global sequence features could influence the prediction performance marginally. We highlight two examples as a comparison in order to illustrate the applicability of this approach. We also discuss the potential implications of this new structural parameter in the field of protein structure prediction and homology modeling. This method might prove to be a powerful tool for sequence analysis.     

Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure

The present study is an attempt to develop a neural network-based method for predicting the real value of solvent accessibility from the sequence using evolutionary information in the form of multiple sequence alignment. In this method, two feed-forward networks with a single hidden layer have been trained with standard back-propagation as a learning algorithm. The Pearson's correlation coefficient increases from 0.53 to 0.63, and mean absolute error decreases from 18.2 to 16% when multiple-sequence alignment obtained from PSI-BLAST is used as input instead of a single sequence. The performance of the method further improves from a correlation coefficient of 0.63 to 0.67 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields a mean absolute error value of 15.2% between the experimental and predicted values, when tested on two different nonhomologous and nonredundant datasets of varying sizes. The method consists of two steps: (1) in the first step, a sequence-to-structure network is trained with the multiple alignment profiles in the form of PSI-BLAST-generated position-specific scoring matrices, and (2) in the second step, the output obtained from the first network and PSIPRED-predicted secondary structure information is used as an input to the second structure-to-structure network. Based on the present study, a server SARpred (http://www.imtech.res.in/raghava/sarpred/) has been developed that predicts the real value of solvent accessibility of residues for a given protein sequence. We have also evaluated the performance of SARpred on 47 proteins used in CASP6 and achieved a correlation coefficient of 0.68 and a MAE of 15.9% between predicted and observed values.     

NETASA: neural network based prediction of solvent accessibility

MOTIVATION: Prediction of the tertiary structure of a protein from its amino acid sequence is one of the most important problems in molecular biology. The successful prediction of solvent accessibility will be very helpful to achieve this goal. In the present work, we have implemented a server, NETASA for predicting solvent accessibility of amino acids using our newly optimized neural network algorithm. Several new features in the neural network architecture and training method have been introduced, and the network learns faster to provide accuracy values, which are comparable or better than other methods of ASA prediction. RESULTS: Prediction in two and three state classification systems with several thresholds are provided. Our prediction method achieved the accuracy level upto 90% for training and 88% for test data sets. Three state prediction results provide a maximum 65% accuracy for training and 63% for the test data. Applicability of neural networks for ASA prediction has been confirmed with a larger data set and wider range of state thresholds. Salient differences between a linear and exponential network for ASA prediction have been analysed. AVAILABILITY: Online predictions are freely available at: http://www.netasa.org. Linux ix86 binaries of the program written for this work may be obtained by email from the corresponding author.     

Prediction of exposure degree diagram and sites of limited proteolysis in globular proteins as an approach to computer-aided design of protein bioregulators with prolonged action

In order to prolong the lifetime of protein bioregulators in blood it is possible to engineer analogs with protected sites of limited proteolysis. To determine the sites, primarily accessible to trypsin-like proteases, a computer procedure has been developed, including a prediction algorithm, to produce the residue diagram of a globular protein and a discriminant algorithm to determine the sites most liable to proteolysis. The accuracy of prediction of amino acid residue exposure is characterised by correlation coefficients between experimental and theoretical exposure values, the coefficients being about 0.7 as calculated for 10 globular proteins. The classification of Arg and Lys residues into two groups, susceptible or insusceptible to protease, has an error percentage of about 25.     

Accurate prediction of solvent accessibility using neural networks-based regression

Accurate prediction of relative solvent accessibilities (RSAs) of amino acid residues in proteins may be used to facilitate protein structure prediction and functional annotation. Toward that goal we developed a novel method for improved prediction of RSAs. Contrary to other machine learning-based methods from the literature, we do not impose a classification problem with arbitrary boundaries between the classes. Instead, we seek a continuous approximation of the real-value RSA using nonlinear regression, with several feed forward and recurrent neural networks, which are then combined into a consensus predictor. A set of 860 protein structures derived from the PFAM database was used for training, whereas validation of the results was carefully performed on several nonredundant control sets comprising a total of 603 structures derived from new Protein Data Bank structures and had no homology to proteins included in the training. Two classes of alternative predictors were developed for comparison with the regression-based approach: one based on the standard classification approach and the other based on a semicontinuous approximation with the so-called thermometer encoding. Furthermore, a weighted approximation, with errors being scaled by the observed levels of variability in RSA for equivalent residues in families of homologous structures, was applied in order to improve the results. The effects of including evolutionary profiles and the growth of sequence databases were assessed. In accord with the observed levels of variability in RSA for different ranges of RSA values, the regression accuracy is higher for buried than for exposed residues, with overall 15.3-15.8% mean absolute errors and correlation coefficients between the predicted and experimental values of 0.64-0.67 on different control sets. The new method outperforms classification-based algorithms when the real value predictions are projected onto two-class classification problems with several commonly used thresholds to separate exposed and buried residues. For example, classification accuracy of about 77% is consistently achieved on all control sets with a threshold of 25% RSA. A web server that enables RSA prediction using the new method and provides customizable graphical representation of the results is available at http://sable.cchmc.org.     

ZPRED: predicting the distance to the membrane center for residues in alpha-helical membrane proteins

MOTIVATION: Prediction methods are of great importance for membrane proteins as experimental information is harder to obtain than for globular proteins. As more membrane protein structures are solved it is clear that topology information only provides a simplified picture of a membrane protein. Here, we describe a novel challenge for the prediction of alpha-helical membrane proteins: to predict the distance between a residue and the center of the membrane, a measure we define as the Z-coordinate. Even though the traditional way of depicting membrane protein topology is useful, it is advantageous to have a measure that is based on a more "physical" property such as the Z-coordinate, since it implicitly contains information about re-entrant helices, interfacial helices, the tilt of a transmembrane helix and loop lengths. RESULTS: We show that the Z-coordinate can be predicted using either artificial neural networks, hidden Markov models or combinations of both. The best method, ZPRED, uses the output from a hidden Markov model together with a neural network. The average error of ZPRED is 2.55A and 68.6% of the residues are predicted within 3A of the target Z-coordinate in the 5-25A region. ZPRED is also able to predict the maximum protrusion of a loop to within 3A for 78% of the loops in the dataset. AVAILABILITY: Supplementary information and training data is available at http://www.sbc.su.se/~erikgr/.     

Predicting absolute contact numbers of native protein structure from amino acid sequence

The contact number of an amino acid residue in a protein structure is defined by the number of C(beta) atoms around the C(beta) atom of the given residue, a quantity similar to, but different from, solvent accessible surface area. We present a method to predict the contact numbers of a protein from its amino acid sequence. The method is based on a simple linear regression scheme and predicts the absolute values of contact numbers. When single sequences are used for both parameter estimation and cross-validation, the present method predicts the contact numbers with a correlation coefficient of 0.555 on average. When multiple sequence alignments are used, the correlation increases to 0.627, which is a significant improvement over previous methods. In terms of discrete states prediction, the accuracies for 2-, 3-, and 10-state predictions are, respectively, 71.4%, 54.1%, and 18.9% with residue type-dependent unbiased thresholds, and 76.3%, 59.2%, and 21.8% with residue type-independent unbiased thresholds. The difference between accessible surface area and contact number from a prediction viewpoint and the application of contact number prediction to three-dimensional structure prediction are discussed.     

How well does presence‐only‐based species distribution modelling predict assemblage diversity? A case study of the Tenerife flora

Prediction error is considered an important problem in species distribution models. To address this issue, we here examined the accuracy of overlays of presence‐only‐based models for many individual species in representing patterns of assemblage diversity. For this purpose, we used a database of 977 160 records of seed plant occurrences on an intensively surveyed, species‐rich island (Tenerife, Canary Islands) for modelling the distribution of all its 841 native plant species individually. The modelling was done using Maxent, one of the best‐performing presence‐only modelling techniques, using various thresholds to convert the estimated suitability values into predicted presence or absence. Distribution models for each individual species were overlaid to predict species richness and composition, which were then compared to the observed values for well‐surveyed grid cells. We found high levels of compositional error, when the best performing suitability threshold for predicting species richness was applied. Our best prediction had a mean species richness error of 24% and a mean compositional error of 60% relative to the observed values for the well‐surveyed cells; >50% of all species were included erroneously in >25% of the well‐surveyed cells. Hence, large quantities of data are not necessarily enough to obtain reliable predictions of assemblage diversity, limiting the usefulness of this methodology in conservation planning.     

Accurate Prediction of Protein Catalytic Residues by Side Chain Orientation and Residue Contact Density

Prediction of protein catalytic residues provides useful information for the studies of protein functions. Most of the existing methods combine both structure and sequence information but heavily rely on sequence conservation from multiple sequence alignments. The contribution of structure information is usually less than that of sequence conservation in existing methods. We found a novel structure feature, residue side chain orientation, which is the first structure-based feature that achieves prediction results comparable to that of evolutionary sequence conservation. We developed a structure-based method, Enzyme Catalytic residue SIde-chain Arrangement (EXIA), which is based on residue side chain orientations and backbone flexibility of protein structure. The prediction that uses EXIA outperforms existing structure-based features. The prediction quality of combing EXIA and sequence conservation exceeds that of the state-of-the-art prediction methods. EXIA is designed to predict catalytic residues from single protein structure without needing sequence or structure alignments. It provides invaluable information when there is no sufficient or reliable homology information for target protein. We found that catalytic residues have very special side chain orientation and designed the EXIA method based on the newly discovered feature. It was also found that EXIA performs well for a dataset of enzymes without any bounded ligand in their crystallographic structures.     

Atom-wise statistics and prediction of solvent accessibility in proteins

In this work, we explore a novel method to broaden the scope of sequence-based predictions of solvent accessibility or accessible surface area (ASA) to the atomic level. All 167 heavy atoms from the 20 types of amino acid residues in proteins have been studied. An analysis of ASA distribution of these atomic groups in different proteins has been performed and rotamer-style libraries have been developed. We observe that the ASA of some atomic groups (e.g., backbone C and N atoms) can be estimated from the sequence environment within a mean absolute error of 2-3 angstroms(2). However, some side chain atoms such as CG in Pro, NH1 in Arg and NE2 in Gln show a strong variability making it more difficult to estimate their ASA from sequence environment. In general, the prediction of ASA becomes more difficult for atomic positions at the side chain extremities of long amino acid residues (aromatic side chain terminals being the exception). Several atomic groups are frequently exposed to solvent. Some of them have a bimodal distribution, suggesting two stable conformations in terms of their solvent exposure. More detailed understanding and prediction of solvent accessibility, i.e., at an atomic level is expected to help in bioinformatics approaches to structure prediction, functional relevance of atomic solvent accessibilities and other interaction analyses.     

Decomposition and Mineralization of Organic Residues Predicted Using Near Infrared Spectroscopy

Characterization of decomposition characteristics is important for sound management of organic residues for both soils and livestock, but routine residue quality analysis is hindered by slow and costly laboratory methods. This study tested the accuracy and repeatability of near-infrared spectroscopy (NIR) for direct prediction of in vitro dry matter digestibility (IVDMD) and C and N mineralization for a diverse range of organic materials (mostly crop and tree residues) of varying quality (n = 32). The residue samples were aerobically incubated in a sandy soil and amounts of C and N mineralized determined after 28 days. IVDMD and quality attributes were determined using wet chemistry methods. Repeatability was higher with NIR than the original wet chemistry methods: on average NIR halved the measurement standard deviation. NIR predicted IVDMD and C and N mineralization more accurately than models based on wet chemical analysis of residue quality attributes: reduction in root mean square error of prediction with NIR, compared with using quality attributes, was IVDMD, 6%; C mineralization after 28 days, 8%; and N mineralization after 28 days, 8%. Cross-validated r ² values for measured wet chemistry vs. NIR-predicted values were: IVDMD, 0.88; C mineralization, 0.82; and N mineralization, 0.87. Direct prediction of decomposition and mineralization from NIR is faster, more accurate and more repeatable than prediction from residue quality attributes determined using wet chemistry. Further research should be directed towards establishment of diverse NIR calibration libraries under controlled conditions and direct calibration of soil quality, crop and livestock responses in the field to NIR characteristics of residues.     

Predicted residue–residue contacts can help the scoring of 3D models

During the 7th Critical Assessment of Protein Structure Prediction (CASP7) experiment, it was suggested that the real value of predicted residue–residue contacts might lie in the scoring of 3D model structures. Here, we have carried out a detailed reassessment of the contact predictions made during the recent CASP8 experiment to determine whether predicted contacts might aid in the selection of close‐to‐native structures or be a useful tool for scoring 3D structural models. We used the contacts predicted by the CASP8 residue–residue contact prediction groups to select models for each target domain submitted to the experiment. We found that the information contained in the predicted residue–residue contacts would probably have helped in the selection of 3D models in the free modeling regime and over the harder comparative modeling targets. Indeed, in many cases, the models selected using just the predicted contacts had better GDT‐TS scores than all but the best 3D prediction groups. Despite the well‐known low accuracy of residue–residue contact predictions, it is clear that the predictive power of contacts can be useful in 3D model prediction strategies. Proteins 2010. © 2010 Wiley‐Liss, Inc.