Biomolecular recognition such as binding of small molecules and flexible peptides to target proteins plays key roles in cellular function. It is critical to characterize thermodynamics and kinetics of biomolecular recognition for drug design. However, such tasks have proven challenging in computational chemistry. Building on a robust Gaussian accelerated molecular dynamics (GaMD) technique, we have developed selective Ligand GaMD (LiGaMD) and Peptide GaMD (Pep-GaMD) algorithms, which allow for highly efficient simulations of biomolecular binding. In LiGaMD, we selectively boost the nonbonded interaction energy of the small-molecule ligand to speed up its dissociation. In Pep-GaMD, since peptides often fold with dramatic conformational changes during binding, a boost potential is applied to the total potential energy of the peptide (both non-bonded and bonded) in order to model the peptide high flexibility. In both algorithms, another boost potential can be applied to the remaining potential energy of the system to facilitate ligand and peptide rebinding. LiGaMD and Pep-GaMD have enabled, <i>for the first time</i>, microsecond all-atom simulations to capture repetitive dissociation and binding of small-molecule ligands and highly flexible peptides. The simulations then allowed us to efficiently calculate the biomolecular binding free energies and kinetic rates simultaneously. As demonstrated on model systems, the calculated ligand/peptide binding free energies and kinetic rate constants were in excellent agreements with the available converged conventional molecular dynamics simulations and/or experimental data. Therefore, the selective LiGaMD and Pep-GaMD provide novel approaches to computing binding thermodynamics and kinetics of biomolecules, which is expected to greatly facilitate computer-aided drug design.

Computing binding thermodynamics and kinetics of biomolecules using novel accelerated molecular simulation algorithms

ACS Fall 2021

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