Using Stochastic Roadmap Simulation to Predict Experimental Quantities in Protein Folding Kinetics: Folding Rates and Phi-Values

Abstract

This paper presents a new method for studying protein folding kinetics. It uses the recently introduced Stochastic Roadmap Simulation (SRS) method to estimate the transition state ensemble (TSE) and predict the rates and the Phi-values for protein folding. The new method was tested on 16 proteins, whose rates and Phi-values have been determined experimentally. Comparison with experimental data shows that our method estimates the TSE much more accurately than an existing method based on dynamic programming. This improvement leads to better folding-rate predictions. We also compute the mean first passage time of the unfolded states and show that the computed values correlate with experimentally determined folding rates. The results on Phi-value predictions are mixed, possibly due to the simple energy model used in the tests. This is the first time that results obtained from SRS have been compared against a substantial amount of experimental data. The results further validate the SRS method and indicate its potential as a general tool for studying protein folding kinetics.

Tracing conformational changes in proteins

“…Motion planning algorithms have been applied extensively in the past to solve biological problems due to the analogy between protein chains and robotic articulated mechanisms [23][24][25]. The search methodology applied in this paper is based on the PathDirected Subdivision Tree (PDST) planner [34][35][36].…”

“…The conformational space of the protein is explored so that high energy regions are avoided and feasible conformational pathways are obtained more efficiently than with traditional simulation methods. Among the many applications of motion planning to biology are the characterization of near-native protein conformational ensembles [20], the study of conformational flexibility in proteins [21,22], protein folding and binding simulation [23][24][25], modeling protein loops [21,26], simulation of RNA folding kinetics [27] and recently the elucidation of conformational pathways in proteins, subject to pre-specified constraints [28]. The search methods described above strike a balance between accuracy and efficiency.…”

Randomized tree construction algorithm to explore energy landscapes

“…Recent works show that algorithms originating from robotics can be the basis for the development of efficient conformational sampling and exploration methods in computational biochemistry. For instance, methods based on robotics algorithms have been proposed to analyze protein loop mobility [29,30], to compute large-amplitude conformational transitions in proteins [31,32], to investigate protein and RNA folding pathways [33,34], or to simulate ligand diffusion inside proteins considering flexible molecular models [35,36]. The present work proposes a conformational exploration method, called Transition-RRT (T-RRT) [37], which is inspired by robotic path planning algorithms and by methods in statistical physics.…”

Modeling Structures and Motions of Loops in Protein Molecules

“…As a joint rotation changes the positions of following links, so does rotation about a bond change the positions of following atoms [21]. These analogies have long been employed by robotics researchers to apply algorithms that plan motions for kinematic chains with revolute dofs to the study of protein conformations [26][27][28][29][30][31][32][33][34][35][36][37]. Unlike typical articulated mechanisms, protein chains have a high number of dofs.…”