PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations.

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery.

INTRODUCTION: For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs. AREAS COVERED: End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (koff and kon) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations. EXPERT OPINION: The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Predicting Biomolecular Binding Kinetics: A Review.

Biomolecular binding kinetics including the association (kon) and dissociation (koff) rates are critical parameters for therapeutic design of small-molecule drugs, peptides, and antibodies. Notably, the drug molecule residence time or dissociation rate has been shown to correlate with their efficacies better than binding affinities. A wide range of modeling approaches including quantitative structure-kinetic relationship models, Molecular Dynamics simulations, enhanced sampling, and Machine Learning has been developed to explore biomolecular binding and dissociation mechanisms and predict binding kinetic rates. Here, we review recent advances in computational modeling of biomolecular binding kinetics, with an outlook for future improvements.