A public workflow for predicting peptide binding structures

Peptides, short chains of amino acids, mediate up to 40% of known protein-protein interactions and play a key role in protein trafficking and cellular signaling. However, peptide-protein interactions present a challenge for conventional computational modeling, due to slow dynamics and high peptide flexibility. It remains difficult to predict binding structures of highly flexible peptides to target proteins.

The goal of this project is to accurately predict peptide binding structures through the development and validation of a public workflow termed “PepBinding”, which integrates peptide docking, accelerated molecular simulations and machine learning. PepBinding will be able to fully account for the peptide and protein flexibility, and thus greatly improve the accuracy of peptide binding structure predictions.

It will provide a generally applicable approach for the world-wide Critical Assessment of PRediction of Interactions (CAPRI) community to predict peptide-protein binding structures. In addition, the PI will combine research with evidence-based education and outreach programs of PepBinding for exceptional training of graduate, undergraduate and high school students as the next-generation computational biologists, especially underrepresented minorities and STEM science and technology students.

A peptide Gaussian accelerated molecular dynamics (Pep-GaMD) enhanced sampling method has been developed, which selectively boosts the peptide essential potential energy and has been shown to tremendously accelerate peptide motions by orders of magnitude. Tens to hundreds of nanosecond Pep-GaMD simulations are able to sufficiently sample peptide conformations in the bound state.

The project aims to (1) develop and benchmark PepBinding for accurate predictions of peptide binding structures by combining Pep-GaMD with peptide docking and machine learning, (2) assess PepBinding performance through blind tests in community challenges and validate new predictions in collaborative biochemical experiments, and (3) implement Pep-GaMD in widely used simulation packages and disseminate PepBinding through a public website. Successful development of PepBinding is expected to greatly drive research frontiers in peptide docking, molecular dynamics, enhanced sampling, and modeling of biomolecular interactions. The results of the project can be found at https://miaolab.ku.edu.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.