Predicting surface exposure of amino acids from protein sequence

Abstract

The amino acid residues on a protein surface play a key role in interaction with other molecules, determined many physical properties, and constrain the structure of the folded protein. A database of monomeric protein crystal structures was used to teach computer-simulated neural networks rules for predicting surface exposure from local sequence. These trained networks are able to correctly predict surface exposure for 72% of residues in a testing set using a binary model, (buried/exposed) and for 54% of residues using a ternary model (buried/intermediate/exposed). In the ternary model, only 11% of the exposed residues are predicted as buried and only 5% of the buried residues are predicted as exposed. Also, since the networks are able to predict exposure with a quantitative confidence estimate, it is possible to assign exposure for over half of the residues in a binary model with greater than 80% accuracy. Even more accurate predictions are obtained by making a consensus prediction of exposure for a homologous family. The effect of the local environment of an amino acid on its accessibility, though smaller than expected, is significant and accounts for the higher success rate of prediction than obtained with previously used criteria. In the absence of a three-dimensional structure, the ability to predict surface accessibility of amino acids directly from the sequence is a valuable tool in choosing sites of chemical modification or specific mutations and in studies of molecular interaction.

Identification of Surface Residues Involved in Protein-Protein Interaction — A Support Vector Machine Approach

“…Several authors have recently reported success in predicting surface residues from amino acid sequence (Mandler 1988;Holbrook et al 1990;Benner et al 1994;Gallivan et al 1997;Mucchielli-Giorgi et al 1999;Naderi-Manesh et al 2001). This raises the possibility of first predicting surface residues based on sequence information and then using the predicted surface residue information to predict the interaction sites using the SVM classifier.…”

Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach

“…Knowledge of surface topology and the geometric neighbors of residues used in the other studies were not used in our study. Several authors have reported success in predicting surface residues from the amino acid sequence [2,10,12,19,20,21]. This raises the possibility of first predicting surface residues based on sequence information, and then using the predicted surface residue information to predict the interaction sites using an SVM classifier.…”

Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

“…Various approaches have been developed, using either amino acid identity (single-sequence input) or a sequence profile (multiplesequence input) as input attributes [240]. The algorithmic level includes neural networks [241][242][243][244][245][246], Bayesian statistics [247], multiple linear regression [248], information theory [249], support vector machine [250,251] and simple baseline approaches [252]. Reported accuracies for two-state (buried or exposed) classifications are 70-72% for single-sequence methods and 73-78% for multiplesequence methods.…”