Dynamics Sampling in Transition Pathway Space

Abstract

The minimum energy pathway contains important information describing the transition between two states on a potential energy surface (PES). Chain-of-states methods were developed to efficiently calculate minimum energy pathways connecting two stable states. In the chain-of-states framework, a series of structures are generated and optimized to represent the minimum energy pathway connecting two states. However, multiple pathways may exist connecting two existing states and should be identified to obtain a full view of the transitions. Therefore, we developed an enhanced sampling method, named as the direct pathway dynamics sampling (DPDS) method, to facilitate exploration of a PES for multiple pathways connecting two stable states as well as addition minima and their associated transition pathways. In the DPDS method, molecular dynamics simulations are carried out on the targeting PES within a chain-of-states framework to directly sample the transition pathway space. The simulations of DPDS could be regulated by two parameters controlling distance among states along the pathway and smoothness of the pathway. One advantage of the chain-of-states framework is that no specific reaction coordinates are necessary to generate the reaction pathway, because such information is implicitly represented by the structures along the pathway. The chain-of-states setup in a DPDS method greatly enhances the sufficient sampling in high-energy space between two end states, such as transition states. By removing the constraint on the end states of the pathway, DPDS will also sample pathways connecting minima on a PES in addition to the end points of the starting pathway. This feature makes DPDS an ideal method to directly explore transition pathway space. Three examples demonstrate the efficiency of DPDS methods in sampling the high-energy area important for reactions on the PES.

Side-Chain Polarity Modulates the Intrinsic Conformational Landscape of Model Dipeptides

“… 57 The potential energy barrier, ΔV , for the transition is estimated to be around 7.5 kcal/mol with the AMBER force field, and about 8.4 kcal/mol at the MP2 level. These values are consistent with previous estimates from transition path sampling, 57 , 59 , 113 as well as milestoning. 114 …”

“…57 The potential energy barrier, ΔV, for the transition is estimated to be around 7.5 kcal/mol with the AMBER force field, and about 8.4 kcal/mol at the MP2 level. These values are consistent with previous estimates from transition path sampling, 57,59,113 as well as milestoning. 114 In Ac-Ser-NH 2 , the transitions from the C 5 -I and C 5 -III forms to C 7 eq -I occur in two steps on the MP2 potential energy surface.…”

Allosteric mechanism of the circadian protein Vivid resolved through Markov state model and machine learning analysis

“…Long-time scale molecular dynamics simulations could provide sufficient sampling of the conformational landscape of proteins. But obtaining statistically significant insights into protein dynamics from massive simulation datasets presents a major challenge[20–22].…”

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

“…Dimension reduction is often performed using time-lagged independent component analysis (TICA) (Schwantes and Pande, 2014 ; Perez-Hernandez and Noe, 2016 ; Noe and Clementi, 2017 ; Olsson et al, 2017 ). In these approaches, the simulation samples can be divided into substates assuming that conformations within each substate share kinetic similarity and could interconvert rapidly (Bowman et al, 2009 ; Zhou and Tao, 2018 ; Zhou et al, 2018a , b ). t-SNE method was recently developed as a dimensionality reduction method with minimum structural information loss revealing that both one-dimensional (1D) and two-dimensional (2D) models of t-SNE method are superior to other tools in distinguishing functional states of allosteric proteins (Zhou et al, 2018a , b ).…”