Structure‐based prediction of protein–peptide specificity in rosetta

Abstract

Protein-peptide interactions mediate many of the connections in intracellular signaling networks. A generalized computational framework for atomically precise modeling of protein-peptide specificity may allow for predicting molecular interactions, anticipating the effects of drugs and genetic mutations, and redesigning molecules for new interactions. We have developed an extensible, general algorithm for structure-based prediction of protein-peptide specificity as part of the Rosetta molecular modeling package. The algorithm is not restricted to any one peptide-binding domain family and, at minimum, does not require an experimentally characterized structure of the target protein nor any information about sequence specificity; although known structural data can be incorporated when available to improve performance. We demonstrate substantial success in specificity prediction across a diverse set of peptide-binding proteins, and show how performance is affected when incorporating varying degrees of input structural data. We also illustrate how structure-based approaches can provide atomic-level insight into mechanisms of peptide recognition and can predict the effects of point mutations on peptide specificity. Shortcomings and artifacts of our benchmark predictions are explained and limits on the generality of the method are explored. This work provides a promising foundation upon which further development of completely generalized, de novo prediction of peptide specificity may progress.

The identification of short linear motif-mediated interfaces within the human interactome

“…The growth in the number of protein complexes with a determined 3D structure has facilitated the development of structural tools to predict SLiM specificities (Betel et al , 2007; Encinar et al , 2009; King and Bradley, 2010; Petsalaki et al , 2009; Stein and Aloy, 2010). The ADAN database (Encinar et al , 2009) utilizes the FoldX algorithm (Schymkowitz et al , 2005) to perform an assessment of the stability and affinity of peptide–domain complexes under in silico mutagenesis analysis.…”

Binding Free Energy Landscape of Domain-Peptide Interactions

“…Most PRD families, however, display significant diversity in how peptides interact with domains, which fundamentally limits this approach. In a recent effort to alleviate this problem, King et al [15] combined peptide docking and subsequent structure-based binding prediction using the Rosetta scoring function. Molecular Dynamics simulations of domain-peptide bound states have also been carried out, emphasizing the importance of dynamics and flexibility for understanding the molecular basis of peptide binding [23]–[25].…”

Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

“…Moreover, our best performing protocol (FPD + IRM) included both flexible peptide docking and gradient-based minimization of the backbone, emphasizing the need for backbone flexibility in attaining the best possible predictions of affinity and specificity. Smith and Kortemme [26] and King and Bradley [27] both found that introducing backbone flexibility by using conformational ensembles as inputs improved specificity prediction. This can be achieved through ensemble generators such as backrub [42] or the use of NMR structures.…”