To Milliseconds and Beyond: Challenges in the Simulation of Protein Folding

By T. J. Lane, D. Shukla, Kyle A. Beauchamp, Vijay S. Pande. Current Opinion in Structural Biology, 2012

373 citations recorded by the source corpus.

Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c1b6ef5645c0821455275865d1edf588f88da5ae

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Citations in the corpus

Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, neutral): “It consists of 68 systems with multiple chains and uses the amber ff99sb forcefield [26].”
Cloud-based simulations on Google Exacycle reveal ligand-modulation of GPCR activation pathways (2013, neutral): “(26).”
Coarse graining molecular dynamics with graph neural networks. (2020, background): “However, even the longest protein simulations still fail to reach total times exceeding milliseconds, and dedicated analysis methods are required to infer dynamics at longer timescales 5,6 .”
Markov state models of biomolecular conformational dynamics (2014, background): “Aside from a myriad of issues related to the accurate treatment of intramolecular and intermolecular interactions and appropriate modeling of the chemical environment, the timescales relevant to biomolecular folding and function (often microseconds to seconds) are enormously long compared to the tim”
Distributed under Creative Commons Cc-by 4.0 Mlitb: Machine Learning in the Browser (background): “Successes include Seti@Home (Anderson et al., 2002) and protein folding (Lane et al., 2013).”