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

Abstract

In this paper, we describe a machine learning approach for sequence-based prediction of proteinprotein interaction sites. A support vector machine (SVM) classifier was trained to predict whether or not a surface residue is an interface residue (i.e., is located in the protein-protein interaction surface), based on the identity of the target residue and its ten sequence neighbors. Separate classifiers were trained on proteins from two categories of complexes, antibody-antigen and protease-inhibitor. The effectiveness of each classifier was evaluated using leave-one-out (jack-knife) cross-validation. Interface and non-interface residues were classified with relatively high sensitivity (82.3% and 78.5%) and specificity (81.0% and 77.6%) for proteins in the antigen-antibody and protease-inhibitor complexes, respectively. The correlation between predicted and actual labels was 0.430 and 0.462, indicating that the method performs substantially better than chance (zero correlation). Combined with recently developed methods for identification of surface residues from sequence information, this offers a promising approach to predict residues involved in protein-protein interactions from sequence information alone.

Prediction of RNA binding sites in proteins from amino acid sequence

“…Recent preliminary experiments using PSSMs as inputs for RNABindR resulted in improved prediction performance comparable with that of Jeong and Miyano (data not shown). Other methods to improve prediction of interface residues may include, for example, adding ''filters'' that eliminate false positives based on the estimated probability that a particular interface residue should be located near other interface residues within the primary sequence, as has been done to improve performance of classifiers for identifying protein-protein interface residues Yan et al 2004b). Alternatively, training on larger data sets of structurally or functionally related RNA binding proteins, generated by relaxing the redundancy criterion, may generate higher accuracy predictions for specific subclasses of RNA binding proteins.…”

“…The tendency of protein-protein interface residues to be clustered along the primary sequence of proteins has been noted previously (Jones et al 2001;Ofran and Rost 2003;Yan et al 2004b). We examined the tendency of RNAprotein interface residues to be similarly clustered in our data set of RNA binding proteins by calculating the log-likelihood that a residue is an interface residue, given that it is at a certain distance from another interface residue (Fig.…”

“…A classifier that simply predicts each residue to be noninterface would have an accuracy of 0.86, but such a classifier would be completely useless in correctly identifying the interface residues. Thus, it is desirable to consider multiple performance measures collectively to evaluate the performance of a classifier and compare its performance with other classifiers (Baldi et al 2000;Yan et al 2004b).…”

“…using only the amino acid sequence (Han et al 2004), a combination of sequence and pseudo-amino acid composition as input (Cai and Lin 2003), or a variety of sequence-based information, including predicted solvent accessibility and predicted secondary structure (Yu et al 2006). Our previous work (Yan et al 2004a(Yan et al ,b, 2006 has demonstrated the feasibility of constructing classifiers for protein-protein and protein-DNA binding site identification using machine learning approaches. However, there has been little work using machine learning approaches to construct classifiers for identifying RNA binding sites from primary amino acid sequence.…”

Computational Methods for Prediction of Protein-Protein Interaction Sites

“…Consequently, the problem may be solved using statistical and machine learning techniques, such as neural networks (Ofran and Rost, 2003b;Zhou and Shan, 2001) or Support Vector Machines (Bock and Gough, 2001;Yan et al, 2004).…”