Binding mechanism of inhibitors to DFG-in and DFG-out P38α deciphered using multiple independent Gaussian accelerated molecular dynamics simulations and deep learning.

Abstract

P38α has been identified as a key target for drug design to treat a wide range of diseases. In this study, multiple independent Gaussian accelerated molecular dynamics (GaMD) simulations, deep learning (DL), and the molecular mechanics generalized Born surface area (MM-GBSA) method were used to investigate the binding mechanism of inhibitors (SB2, SK8, and BMU) to DFG-in and DFG-out P38α and clarify the effect of conformational differences in P38α on inhibitor binding. GaMD trajectory-based DL effectively identified important functional domains, such as the A-loop and N-sheet. Post-processing analysis on GaMD trajectories showed that binding of the three inhibitors profoundly affected the structural flexibility and dynamical behaviour of P38α situated at the DFG-in and DFG-out states. The MM-GBSA calculations not only revealed that differences in the binding ability of inhibitors are affected by DFG-in and DFG-out conformations of P38α, but also confirmed that van der Waals interactions are the primary force driving inhibitor-P38α binding. Residue-based free energy estimation identifies hot spots of inhibitor-P38α binding across DFG-in and DFG-out conformations, providing potential target sites for drug design towards P38α. This work is expected to offer valuable theoretical support for the development of selective inhibitors of P38α family members.