scholarly journals Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

2018 ◽  
Vol 122 (49) ◽  
pp. 11174-11185 ◽  
Author(s):  
Yong Wang ◽  
Pengfei Tian ◽  
Wouter Boomsma ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Monte Carlo ◽  
Protein Folding ◽  
Monte Carlo Sampling ◽  
Native State

scholarly journals Monte Carlo Sampling of Protein Folding by Combining an All-Atom Physics-Based Model with a Native State Bias

2018 ◽  
Author(s):  
Yong Wang ◽  
Pengfei Tian ◽  
Wouter Boomsma ◽  
Kresten Lindorff-Larsen
Keyword(s):  
Monte Carlo ◽  
Protein Folding ◽  
Conformational Changes ◽  
Conformational Dynamics ◽  
Coarse Graining ◽  
Monte Carlo Sampling ◽  
Implicit Solvent ◽  
Great Success ◽  
Native State ◽  
Dynamics Simulations

AbstractEnergy landscape theory suggests that native interactions are a major determinant of the folding mechanism of a protein. Thus, structure-based (Gō) models have, aided by coarse-graining techniques, shown great success in capturing the mechanisms of protein folding and conformational changes. In certain cases, however, non-native interactions and atomic details are also essential to describe the protein dynamics, prompting the development of a variety of structure-based models which include non-native interactions, and differentiate between different types of attractive potentials. Here, we describe an all-protein-atom hybrid model, termed ProfasiGo, that integrates an implicit solvent all-atom physics-based model (called Profasi) and a structure-based Gō potential, and its implementation in two software packages (PHAISTOS and ProFASi) that are developed for Monte Carlo sampling of protein molecules. We apply the ProfasiGo model to study the folding free energy landscapes of four topologically similar proteins, one of which can be folded by the simplified potential Profasi, and two that have been folded by explicit solvent, all-atom molecular dynamics simulations with the CHARMM22∗ force field. Our results reveal that the hybrid ProfasiGo model is able to capture many of the details present in the physics-based potentials, while retaining the advantages of Gō models for sampling and guiding to the native state. We expect that the model will be widely applicable to study the folding of more complex proteins, or to study conformational dynamics and integration with experimental data.

scholarly journals DBFOLD: An efficient algorithm for computing folding pathways of complex proteins

2020 ◽  
Author(s):  
Amir Bitran ◽  
William M. Jacobs ◽  
Eugene Shakhnovich
Keyword(s):  
Monte Carlo ◽  
Protein Folding ◽  
Detailed Balance ◽  
Simulation Methods ◽  
Free Energies ◽  
Native State ◽  
Folding Rates ◽  
Computational Speed ◽  
Folding Pathways

AbstractAtomistic simulations can provide valuable, experimentally-verifiable insights into protein folding mechanisms, but existing ab initio simulation methods are restricted to only the smallest proteins due to severe computational speed limits. The folding of larger proteins has been studied using native-centric potential functions, but such models omit the potentially crucial role of non-native interactions.Here, we present an algorithm, entitled DBFOLD, which can predict folding pathways for a wide range of proteins while accounting for the effects of non-native contacts. In addition, DBFOLD can predict the relative rates of different transitions within a protein’s folding pathway. To accomplish this, rather than directly simulating folding, our method combines equilibrium Monte-Carlo simulations, which deploy enhanced sampling, with unfolding simulations at high temperatures. We show that under certain conditions, trajectories from these two types of simulations can be jointly analyzed to compute unknown folding rates from detailed balance. This requires inferring free energies from the equilibrium simulations, and extrapolating transition rates from the unfolding simulations to lower, physiologically-reasonable temperatures at which the native state is marginally stable. As a proof of principle, we show that our method can accurately predict folding pathways and Monte-Carlo rates for the well-characterized Streptococcal protein G. We then show that our method significantly reduces the amount of computation time required to compute the folding pathways of large, misfolding-prone proteins that lie beyond the reach of existing direct simulation methods. Our algorithm, which is available online, can generate detailed atomistic models of protein folding mechanisms while shedding light on the role of non-native intermediates which may crucially affect organismal fitness and are frequently implicated in disease.Author summaryMany proteins must adopt a specific structure in order to function. Computational simulations have been used to shed light on the mechanisms of protein folding, but unfortunately, realistic simulations can typically only be run for small proteins, due to severe limits in computational speed. Here, we present a method to solve this problem, whereby instead of directly simulating folding from an unfolded state, we run simulations that allow for computation of equilibrium folding free energies, alongside high temperature simulations to compute unfolding rates. From these quantities, folding rates can be computed using detailed balance. Importantly, our method can account for the effects of nonnative contacts which transiently form during folding and must be broken prior to adoption of the native state. Such contacts, which are often excluded from simple models of folding, may crucially affect real protein folding pathways and are often observed in folding intermediates implicated in disease.

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