Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Yao, Songyuan; Van, Richard; Pan, Xiaoliang; Park, Ji Hwan; Mao, Yuezhi; Pu, Jingzhi; Ye, Mei; Shao, Yihan

doi:10.1039/d2ra08180f

Cited by 12 publications

(5 citation statements)

References 129 publications

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“…Our conclusions on the importance of multibody terms are consistent with the findings of Wang et al, who, by using a bottom-up approach, showed that multibody terms are needed for small proteins to reproduce the folding free energy landscape of atomistic simulations correctly. For this reason and to allow a more expressive functional form for the multibody terms, recent advances in the development of protein CG models make use of neural networks to represent nonbonded interaction potentials. ,− …”

Section: Discussionmentioning

confidence: 99%

Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective

Zaporozhets

Clementi

2023

J. Phys. Chem. B

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Coarse-grained models allow computational investigation of biomolecular processes occurring on long time and length scales, intractable with atomistic simulation. Traditionally, many coarse-grained models rely mostly on pairwise interaction potentials. However, the decimation of degrees of freedom should, in principle, lead to a complex many-body effective interaction potential. In this work, we use experimental data on mutant stability to parametrize coarse-grained models for two proteins with and without many-body terms. We demonstrate that many-body terms are necessary to reproduce quantitatively the effects of point mutations on protein stability, particularly to implicitly take into account the effect of the solvent.

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Section: Discussionmentioning

confidence: 99%

Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective

Zaporozhets

Clementi

2023

J. Phys. Chem. B

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“…Alternatively, one can adopt an implicit description of the MM environment through the use of MM‐perturbed semi‐empirical QM charges, 82,83 MM electrostatic potential or field at QM atom positions, 84,85 or through polarizable embedding 86 . One can also use both MM electrostatic potential and field in the training of QM/MM MLPs 81,87 using our QM/MM‐AC scheme 80 for separating inner and outer MM atoms and projecting outer MM charges onto inner MM atom positions 80,88,89 …”

Section: Discussionmentioning

confidence: 99%

Training machine learning potentials for reactive systems: A Colab tutorial on basic models

Pan,

Snyder,

Wang

et al. 2023

J Comput Chem

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In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field — the training of system‐specific MLPs for reactive systems — with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self‐guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel‐based (using Gaussian process regression, GPR) models by fitting the two‐dimensional Müller‐Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot‐SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.

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“…A classic example is the alanine dipeptidea small, thoroughly studied system where a biomolecule exhibits rare events in solution at room temperature. It is typically used for benchmarking various enhanced sampling methods and lately, the quality of machine learning potentials. , Therefore, generating more reference (“training”) data is essential for the development and validation of novel force field parameters. The ever-increasing computational power, including promising advances in quantum computing, is anticipated to become increasingly important in producing such data, e.g., through accelerated and AI-enhanced first-principles calculations.…”

Section: Coupling Molecular Dynamics and Artificial Intelligencementioning

confidence: 99%

Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050

Biriukov,

Vácha

2024

ACS Phys. Chem Au

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In the last quarter-century, the field of molecular dynamics (MD) has undergone a remarkable transformation, propelled by substantial enhancements in software, hardware, and underlying methodologies. In this Perspective, we contemplate the future trajectory of MD simulations and their possible look at the year 2050. We spotlight the pivotal role of artificial intelligence (AI) in shaping the future of MD and the broader field of computational physical chemistry. We outline critical strategies and initiatives that are essential for the seamless integration of such technologies. Our discussion delves into topics like multiscale modeling, adept management of ever-increasing data deluge, the establishment of centralized simulation databases, and the autonomous refinement, cross-validation, and self-expansion of these repositories. The successful implementation of these advancements requires scientific transparency, a cautiously optimistic approach to interpreting AI-driven simulations and their analysis, and a mindset that prioritizes knowledge-motivated research alongside AI-enhanced big data exploration. While history reminds us that the trajectory of technological progress can be unpredictable, this Perspective offers guidance on preparedness and proactive measures, aiming to steer future advancements in the most beneficial and successful direction.

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Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Abstract: Here we investigated the use of machine learning (ML) techniques to “derive” an implicit solvent model based on the average solvent environment configurations from explicit solvent molecular dynamics (MD) simulations.

Cited by 12 publications

References 129 publications

Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective

Multibody Terms in Protein Coarse-Grained Models: A Top-Down Perspective

Training machine learning potentials for reactive systems: A Colab tutorial on basic models

Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050

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