Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Abstract

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation. Graphical Abstract

Identification of FDA-approved drugs with triple targeting mode of action for the treatment of monkeypox: a high throughput virtual screening study

“…The binding free energy for the six complexes was calculated using fastDRH [ 30 ], an online web server, by submitting the cartesian coordinates of the corresponding receptor molecule and the ligand molecule. For binding free energy calculation, we have used PB4 MM/PBSA procedure based on the pose ranking.…”

Uncovering the Kinetic Characteristics and Degradation Preference of PROTAC Systems with Advanced Theoretical Analyses

“…Here, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) was used to analyze the binding characteristics of the cMD and US trajectories, which has been widely applied in various biological systems, such as protein–ligand, − protein–protein/peptide, , and protein–RNA systems. The calculation of MM/GBSA binding free energy follows the below formulas: .25ex2ex Δ G bind = Δ G com − false( normalΔ G rec + normalΔ G lig false) .25ex2ex G bind = Δ H − T Δ S ≈ Δ E MM + Δ G sol − T Δ S .25ex2ex Δ E MM = Δ E int + Δ E ele + Δ E vdw .25ex2ex Δ G sol = …”

“…Here, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) was used to analyze the binding characteristics of the cMD and US trajectories, which has been widely applied in various biological systems, such as protein–ligand, − protein–protein/peptide, , and protein–RNA systems. The calculation of MM/GBSA binding free energy follows the below formulas: where Δ G bind represents the binding free energy that is numerically equal to the energy difference between the protein–ligand complex (bound state) and the protein/ligand individuals (free state) (eq ).…”

Assessment of Different Parameters on the Accuracy of Computational Alanine Scanning of Protein–Protein Complexes with the Molecular Mechanics/Generalized Born Surface Area Method

“…In most studies using this method, the interior dielectric constant is set to one when calculating the electrostatic energy (the Coulomb term in the force field) and polar solvation energy. However, multiple studies have shown that an interior dielectric constant of one is not appropriate for some systems. , Using a small solute dielectric constant leads to an overestimation of the electrostatic attractions and repulsions between the protein and the ligand because electrostatic interactions are not shielded enough. Hou et al found that in most cases, MM/PB­( GB )­SA produces better results with a higher dielectric constant, such as 2 or 4 .…”