Improving Sequence-Based Prediction of Protein–Peptide Binding Residues by Introducing Intrinsic Disorder and a Consensus Method

Protein-peptide interaction is crucial for many cellular processes. It is difficult to determine the interaction by experiments as peptides are often very flexible in structure. Accurate sequence-based prediction of peptide-binding residues can facilitate the study of this interaction. In this work, we developed two novel sequence-based methods SVMpep and PepBind to identify the peptide-binding residues. Recent studies demonstrate that the protein-peptide binding is closely associated with intrinsic disorder. We thus introduced intrinsic disorder in our feature design and developed the ab initio method SVMpep. Experiments show that intrinsic disorder contributes to 1.2-5.2% improvement in area under the receiver operating characteristic curve (AUC). Comparison to the recent sequence-based method SPRINT-Seq reveals that SVMpep improves the AUC and Matthews correlation coefficient (MCC) by at least 7.7% and 70%, respectively. In addition, by combining SVMpep with two template-based methods S-SITE and TM-SITE, we next proposed the consensus-based method PepBind. Remarkably, compared with the latest structure-based method SPRINT-Str, PepBind improves the AUC and MCC by 1.7% and 28.3%, respectively, on the same independent test set of SPRINT-Str. The success of PepBind is attributed to the improved prediction of the ab initio method SVMpep by introducing intrinsic disorder and the consensus prediction by combining three complementary methods. A web server that implements the proposed methods is freely available at http://yanglab.nankai.edu.cn/PepBind/ .