These short stretches of residues or peptide regions are often disordered alone and only obtain structure upon binding. Among these methods, template-based approaches have shown great success in the past.Ī significant fraction (15–40%) of protein–protein interactions are peptide-mediated interactions ( Petsalaki and Russell, 2008), in which a short stretch of residues interact with a larger protein receptor ( Mohan et al., 2006). Methods such as HADDOCK, PIPER and ZDOCK ( Dominguez et al., 2003 Kozakov et al., 2006 Pierce et al., 2014) make predictions directly from sequence or energy function information, while others such as InterPred, PRISM and M-TASSER ( Baspinar et al., 2014 Chen and Skolnick, 2008 Wallner and Mirabello, 2017) use already solved structures as templates for prediction. However, because of the complexity, cost and time it takes to perform experiments, computational methods have been developed to support and supplement. Structures of interacting proteins can be experimentally solved through a multitude of methods such as X-ray crystallography, NMR and cryo-EM ( Rhodes, 2010 Topf et al., 2008 Wüthrich, 1986). To understand these processes, it is important to know the structural details of the interactions. Protein–protein interactions are vital in most biological processes, from metabolism to cell life-cycle ( Midic et al., 2009 Tu et al., 2015). In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. InterPep2 is a freely available method for predicting the structure of peptide–protein interactions.
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