Absolute comparison of simulated and experimental protein-folding dynamics

Abstract

Protein folding is difficult to simulate with classical molecular dynamics. Secondary structure motifs such as alpha-helices and beta-hairpins can form in 0.1-10 micros (ref. 1), whereas small proteins have been shown to fold completely in tens of microseconds. The longest folding simulation to date is a single 1- micro s simulation of the villin headpiece; however, such single runs may miss many features of the folding process as it is a heterogeneous reaction involving an ensemble of transition states. Here, we have used a distributed computing implementation to produce tens of thousands of 5-20-ns trajectories (700 micros) to simulate mutants of the designed mini-protein BBA5. The fast relaxation dynamics these predict were compared with the results of laser temperature-jump experiments. Our computational predictions are in excellent agreement with the experimentally determined mean folding times and equilibrium constants. The rapid folding of BBA5 is due to the swift formation of secondary structure. The convergence of experimentally and computationally accessible timescales will allow the comparison of absolute quantities characterizing in vitro and in silico (computed) protein folding.

β‐strand interactions at the domain interface critical for the stability of human lens γD‐crystallin

“…[30][31][32][33][34] In a separate example, even the use of high resolution experimental techniques such as coupling of H/D exchange with 2D NMR only allows detection of intermediate states with almost native-like five-strand b-sheet conformation during the earliest stage of Ubiquitin folding. 35,36 Computer simulations of protein models at different resolutions, from simple lattice models (and off-lattice) [37][38][39] to continuum solvent models 38,40,41 to all-atom explicit solvent models, 42,43 have been used to supplement existing experimental techniques in understanding aspects of protein folding. 44,45 In principle, molecular dynamics (MD) simulations with an atomistic description of the protein and solvent molecules should provide the most realistic description of the protein folding landscapes.…”

“…44,45 In principle, molecular dynamics (MD) simulations with an atomistic description of the protein and solvent molecules should provide the most realistic description of the protein folding landscapes. However, such folding simulations starting from fully extended states are often limited to small peptides, 42,43 as the sampling of the huge conformational space of a typical protein is currently inaccessible even with the fastest supercomputers. In this endeavor, unfolding simulations, performed in either chemical denaturing or thermal denaturing conditions, have been widely used to shed light onto the folding scenario.…”

Conformational Transition Pathway of Polymerase β/DNA upon Binding Correct Incoming Substrate

“…Activated and long-time processes can also be studied by using high-temperature simulations, 9 aggregate dynamics, 10 enhanced sampling by MC/MD, [11][12][13][14] replica dynamics, 15,16 free-energy calculations, 17-19 path sampling [20][21][22] or the stochastic path approach. 23 All these methodologies are based on different approximations and address varied problems (see, for example, ref 24).…”

Protein folding: Then and now

“…In addition, world-wide parallel computing (e.g. Folding@Home [31]) and generalized ensemble sampling techniques that involve parallel simulations of molecular systems coupled with a Monte Carlo (MC) protocol [32,33] have been successfully applied to protein folding [25,[34][35][36].…”

“…Rate predictions have been likewise performed using molecular dynamics simulations using explicit water models such as the TIP3P [131] and SPC [132] to gain additional insight into folding kinetics. Examples of MD simulations using explicit solvent which yielded experimentally consistent rates were performed by Pande et al, who observed helix-coil transitions [133] and protein folding [34]. However, a potential drawback in the use of water models is that they are parameterized to a single temperature (~298 K), and thus may bias the dynamics in non-native temperature simulations.…”