PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian...

Area of Science:

• Computational Biology
• Biophysics
• Drug Discovery

Background:

• Accurate prediction of protein-peptide interactions is vital for understanding biological processes and developing peptide-based therapeutics.
• Traditional computational methods struggle with the inherent flexibility and slow dynamics of peptides, limiting the precision of binding structure predictions.

Purpose of the Study:

• To develop and validate an efficient computational workflow, named PepBinding, for predicting peptide-protein binding structures.
• To enhance the accuracy of initial peptide docking models using advanced simulation techniques.

Main Methods:

• The PepBinding workflow integrates peptide docking (HPEPDOCK) with all-atom enhanced sampling simulations using Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD).
• Structural clustering is employed to refine and analyze the simulation results.
• The workflow was tested on seven distinct model peptides.

Main Results:

• Initial peptide docking yielded models with backbone root-mean-square deviations (RMSDs) from 3.8 to 16.0 Å, classified as medium to inaccurate by CAPRI criteria.
• Short Pep-GaMD simulations (200 ns) significantly improved docking models, achieving five medium and two acceptable quality predictions.
• The combined approach demonstrates enhanced accuracy in predicting peptide-peptide binding conformations.

Conclusions:

• PepBinding offers an efficient and effective computational strategy for predicting peptide-protein binding structures.
• The workflow successfully refines docking predictions, providing higher quality models crucial for drug design and biological studies.
• PepBinding is publicly available, facilitating broader research in peptide-protein interaction prediction.