Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD): Characterization of Ligand Binding Thermodynamics and Kinetics

Abstract

Calculations of ligand binding free energies and kinetic rates are important for drug design.However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method "LiGaMD", which selectively boosts the ligand non-bonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the βcyclodextrin host were observed in hundreds-of-nanosecond LiGaMD simulations. The calculated binding free energies of guest molecules with sufficient sampling agreed excellently with experimental data (< 1.0 kcal/mol error). In comparison with previous microsecond-timescale conventional molecular dynamics simulations, accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a promising approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

Investigation of the Differences in Antithrombin to Heparin Binding among Antithrombin Budapest 3, Basel, and Padua Mutations by Biochemical and In Silico Methods

“…However, the insufficient conformational sampling of partially or fully dissociated conformations did not allow us to calculate free energies from these data. Such calculations would require a different enhanced sampling technique, for example the recently published LiGaMD method [ 66 ].…”

Pathways and Mechanism of Caffeine Binding to Human Adenosine A2A Receptor

“…However, it is important to note that only two ligand-binding events and one dissociation event were observed in the presented GaMD simulations ( Figure 1 ). Quantitative characterization of the ligand-binding free energy and kinetics would require sampling of significantly more ligand-binding events, which will be investigated in the future using a very recently developed, potentially more efficient Ligand GaMD (LiGaMD) method ( Miao et al, 2020 ) and other applicable algorithms. Nevertheless, energetic reweighting of the GaMD simulations enabled us to identify relatively low-energy conformational states of CFF binding to the A 2A AR.…”

Mechanism of Ligand Recognition by Human ACE2 Receptor

“…Moreover, because the boost potential exhibits a Gaussian distribution, biomolecular free energy profiles can be properly recovered through cumulant expansion to the second order. 17 In LiGaMD, 16 the ligand nonbonded interaction potential energy is selectively boosted to enable ligand dissociation. Another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm to facilitate ligand rebinding.…”

“…The calculated ligand binding free energy and kinetic rate constants compared very well with experimental data. 16 …”

“…This finding suggested that electrostatic interactions played an important role in the recognition and binding/dissociation of the MLN-4760 inhibitor to the ACE2 receptor, being consistent with previous findings of “electrostatic steering” in recognition of charged ligands by proteins. 16 , 20 …”