Identifying a Feasible Transition Pathway between Two Conformational States for a Protein

Abstract

Proteins usually need to transit between different conformational states to fulfill their biological functions. In the mechanistic study of such transition processes by molecular dynamics simulations, identification of the minimum free energy path (MFEP) can substantially reduce the sampling space, thus enabling rigorous thermodynamic evaluation of the process. Conventionally, the MFEP is derived by iterative local optimization from an initial path, which is typically generated by simple brute force techniques like the targeted molecular dynamics (tMD). Therefore, the quality of the initial path determines the successfulness of MFEP estimation. In this work, we propose a method to improve derivation of the initial path. Through iterative relaxationbiasing simulations in a bidirectional manner, this method can construct a feasible transition pathway connecting two known states for a protein. Evaluation on small, fast-folding proteins against long equilibrium trajectories supports the good sampling efficiency of our method. When applied to larger proteins including the catalytic domain of human c-Src kinase as well as the converter domain of myosin VI, the paths generated by our method deviate significantly from those computed with the generic tMD approach. More importantly, free energy profiles and intermediate states obtained from our paths exhibit remarkable improvements over those from tMD paths with respect to both physical rationality and consistency with a priori knowledge.

Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction

“…Embedding interaction-type scalar features with invariant representations will enable maximum utilization of trajectory information. Future studies will also benefit from incorporating path optimization , and solvent dynamics to enhance the analysis of entropy contributions in a more rational scenario. Integrating these modules into the analysis will be useful in providing evidence for drug-related application scenarios.…”

Protein conformational ensembles in function: roles and mechanisms

“…25,26 It also fails in (ii) identifying the preferred activation pathway in e.g., kinases, which requires not only a catalogue of the conformational states but their occupancies. [27][28][29][30][31][32][33] And importantly, consequently it is unable to (iii) capture allosteric mechanisms. [34][35][36][37][38][39][40][41][42] In enzymatic reactions, 23,41,[43][44][45] conformational dynamic amplifies the heterogeneity of the transition state.…”

“…, kinases, which requires not only a catalogue of the conformational states but their occupancies. 27–33 And importantly, consequently it is unable to (iii) capture allosteric mechanisms. 34–42 In enzymatic reactions, 23,41,43–45 conformational dynamic amplifies the heterogeneity of the transition state.…”

Tackling Hysteresis in Conformational Sampling: How to Be Forgetful with MEMENTO

“…Instead, we focus on alternative path-generation algorithms. From various takes on morphing (coordinate interpolation) − and rigid-body approximations , to elastic network-based models , and elaborate analysis–biasing iterative schemes, the field is ripe with ways to connect protein conformations. Unfortunately, the potential for hysteresis in umbrella sampling remains understudied because a full validation of the PMFs is often not undertaken.…”