Promoting transparency and reproducibility in enhanced molecular simulations

Bonomi, Massimiliano; Giovanni, Bruno; Camilloni, Carlo; Tribello, Gareth A.; Pavel., B Sorokin; Alessandro, Belardini; Biesuz, Mattia; Bolhuis, Peter G.; Sandro, Binda; Davide, Bigoni; Capelli, Riccardo; Paolo, C.; Michele, Cristiano De; Andréa, Carla; Haochuan, C.; Wei, Chenyang; Cellini, Francesco; Sandip, Das; Pierre, Marco De La; Davide, Di Ruscio; Dorer, Viktor; Bernd, E.; Ferguson, Andrew L.; Marta, Fernandez-Garcia; Fraser, James S.; Haohao, F.; Piero, G.; Gervasio, Francesco Luigi; Federico, Garcia-Maroto; Alejandro, G.; Gabaldón, Toni; Heller, Gabriella T.; Hocky, Glen M.; Marcella, I.; Michele, Incerti; Jelfs, Kim E.; Alexander, Jude; Evgeny, Kolesnikov; Alessandro, Latini; Limongelli, Vittorio; Kresten, L.; Thomas, Lin; Fabrizio, Michele; Layla, M.; Matteo, Mauro; Ralf, Miesel; Angelos, M.; Carla, Martins; Masuda, Toshio; Marco, N Di; Paissoni, Cristina; Elena, Palmieri; Michele, Perego; Jim, P.; Pablo, Perez; GiovanniMaria, P.; Pružinská, Adriana; Fabio, Passetti; Silvio, Pappada; Provasi, Davide; David, Q.; Paolo, Romano Giovanni; Stefano, Raffaele Di; Jakub, R.; Seita, Matteo; Sosso, Gabriele C.; Vojtech, Sveboda; Jiri, Spousta; Swenson, David W.; Tiwary, Pratyush; Omar, Vilchez; Vacca, Michele; Voth, Gregory A.; Andrew, W Harrell

doi:10.1038/s41592-019-0506-8

Cited by 779 publications

(645 citation statements)

References 29 publications

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“…We implemented the new method, called on-the-fly probability enhanced sampling (OPES), in the enhanced sampling library PLUMED [29] and tested it on a variety of different systems. The code and all the files needed to reproduce the simulations are openly available in the PLUMED-NEST website [30], as plumID:19.068 . Here we only present the results obtained on the benchmark system of alanine dipeptide in vacuum, using both the convergence (OPES-c) and the exploration (OPES-e) variant.…”

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confidence: 99%

Rethinking Metadynamics: From Bias Potentials to Probability Distributions

Invernizzi

Parrinello

2020

J. Phys. Chem. Lett.

193

270

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Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is function of a selected number of collective variables. We propose here a drastic change in perspective that shifts the focus from the bias to the probability distribution reconstruction, leading to an improvement in usability and convergence speed. The resulting enhanced sampling method can be seen as a combination of metadynamics and adaptive umbrella sampling approaches, that aims at taking the best from the two worlds. This new method has a straightforward reweighting scheme and allows for efficient importance sampling, avoiding uninteresting high free energy regions. Thanks to a compressed kernel density estimation it can handle a higher dimensional collective variable space, and does not require any prior knowledge of the boundaries of such space. The new method comes in two variants. The first aims at a quick convergence, avoiding oscillations and maximizing the quasi-static bias regime, while in the second the main focus is on a rapid exploration of the free energy landscape. We demonstrate the performance of the method in a number of representative examples.Enhanced sampling plays a crucial role in modern simulation techniques, and is a very active area of research [1]. Of particular importance has been the work of Torrie and Valleau [2]. They consider a system with an interaction potential U (R), where R denotes the atomic coordinates. Sampling is accelerated by adding a bias potential V (s) that depends on R via a set of collective variables (CVs), s = s(R). The CVs are chosen so as to describe the modes of the system that are more difficult to sample. The choice of a proper set of CVs is critical, as it determines the efficiency of the method. The properties of the unbiased system are then calculated by using a reweighting procedure. In fact the unbiased probability densitycan be written as an average over the biased ensemble:where β is the inverse temperature. Since this work, a large number of CV based methods have been proposed. Adaptive umbrella sampling[3] (AUS) was the first to build iteratively V (s) so that it would compensate for the free energy surface (FES), F (s) = − 1 β log P (s). At the n-th iteration the bias is given by:where the probability estimate P n (s) can be obtained via a weighted histogram, with weights w k = e βV k , or some more elaborate estimator [4], and can be updated iteratively or on the fly [5,6]. At convergence one has V (s) = −F (s). A different approach has been introduced by metadynamics[7, 8] (MetaD). Here one builds V (s) directly, instead of first reconstructing the probability distribution. The bias is updated on the fly by adding a Gaussian centered at every new point sampled s k :

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confidence: 99%

Rethinking Metadynamics: From Bias Potentials to Probability Distributions

Invernizzi

Parrinello

2020

J. Phys. Chem. Lett.

193

270

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“…• As has long been the case in the method development field, there remains an urgent need to prove the potential of new methods on the types of complex biological systems that users are interested in, not just simple model systems. There is an increasing focus on improving the reproducibility and reliability of simulations [105]; ideally, use of multiple different force fields and simulation methods should become the norm, aided by tools for improving interoperability between different MD codes [106], and convergence should be monitored, for which robust and widely applicable tools are required. Together, these will greatly improve the quality of investigations of protein conformational dynamics.…”

Section: Perspectivesmentioning

confidence: 99%

Computational methods for exploring protein conformations

Allison

2020

Biochemical Society Transactions

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Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

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“…Notably, the out-of-sample problem and the requirement of a smooth mapping of the configurational space for computing biasing forces (i.e., the differentiability) have been elegantly bypassed by Spiwok and Králová [65] through a generalization of the path CVs previously introduced by Branduardi et al [66]. These variables are nowadays referred to as "Property Maps" within the PLUMED community [23,67], but we highlight here that an Isomap embedding can also be used as a CV space through the more general "Smooth and Nonlinear Data-Driven CV" (SandCV) formalism developed by Hashemian et al [68]. Finally, we mention that Isomap was recently used by Schuetz et al to map the unbinding pathways of drug-like molecules from their target as obtained by high-effective temperature MD simulations [69].…”

Section: Beyond Linear Dimensionality Reductionmentioning

confidence: 99%

Data-Driven Molecular Dynamics: A Multifaceted Challenge

2020

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The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

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Promoting transparency and reproducibility in enhanced molecular simulations

Abstract: The PLUMED consortium (2019). Promoting transparency and reproducibility in enhanced molecular simulations. Nature Methods, 16(8), 670-673. https://doi.

Cited by 779 publications

References 29 publications

Rethinking Metadynamics: From Bias Potentials to Probability Distributions

Rethinking Metadynamics: From Bias Potentials to Probability Distributions

Computational methods for exploring protein conformations

Data-Driven Molecular Dynamics: A Multifaceted Challenge

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