Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems

Ravindra, Pavan; Smith, Zachary; Tiwary, Pratyush

doi:10.1039/c9me00115h

Cited by 53 publications

(61 citation statements)

References 38 publications

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“…We conclude by noting that despite the limitations of our simple illustrative strategies, this work can also be combined with sampling methods that are used to determine effective sets of basis functions for describing molecular systems 43 or those that are used for dimensionality reduction. 36,39,44 We envision that these methods will be used to develop an automated framework for sampling biomolecules that takes a starting structure and runs enhanced sampling simulations without the necessity for expert knowledge.…”

Section: Discussionmentioning

confidence: 99%

Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

Smith

Tiwary

2021

Preprint

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Molecular dynamics (MD) simulations provide a wealth of high-dimensional data at all-atom and femtosecond resolution but deciphering mechanistic information from this data is an ongoing challenge in physical chemistry and biophysics. Theoretically speaking, joint probabilities of the equilibrium distribution contain all thermodynamic information, but they prove increasingly difficult to compute and interpret as the dimensionality increases. Here, inspired by tools in probabilistic graphical modeling, we develop a factor graph trained through belief propagation that helps factorize the joint probability into an approximate tractable form that can be easily visualized and used. We validate the study through the analysis of the conformational dynamics of two small peptides with 5 and 9 residues. Our validations include testing the conditional dependency predictions through an intervention scheme inspired by Judea Pearl. Secondly we directly use the belief propagation based approximate probability distribution as a high-dimensional static bias for enhanced sampling, where we achieve spontaneous back-and-forth motion between metastable states that is up to 350 times faster than unbiased MD. We believe this work opens up useful ways to thinking about and dealing with high-dimensional molecular simulations.

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Section: Discussionmentioning

confidence: 99%

Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

Smith

Tiwary

2021

Preprint

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“…In this work, we constructed our RC as a linear combination of a set of basis functions, which we call order parameters (OPs). AMINO starts with a large dictionary of OPs and uses an information theoretic approach to identify redundancies in the full dictionary to return a reduced set of OPs for use in RC construction (30).…”

Section: Aminomentioning

confidence: 99%

“…AMINO decides the optimal value of k automatically as discussed in Ref. 30. This forms the procedure that allows for automated selection of OPs with minimal prior knowledge of the system.…”

Section: Aminomentioning

confidence: 99%

“…Our aim in this work is to demonstrate how our recent enhanced molecular dynamics (MD) simulation algorithms grounded in statistical mechanics and AI (26)(27)(28)(29)(30) can be used to obtain such insight in this classic test-piece system, thereby opening the possibility to answer such questions in the future in generic proteins with related unanswered questions of practical and fundamental relevance (31,32). In principle, MD simulations can give such insights into the thermodynamics and biophysical mechanisms behind these protein conformational changes by providing data at the all-atom and femtosecond resolution, which can be expensive to obtain in experiments (33).…”

Section: Introductionmentioning

confidence: 99%

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Discovering loop conformational flexibility in T4 lysozyme mutants through artificial intelligence aided molecular dynamics

Smith

Ravindra

Wang

et al. 2020

Preprint

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# These two authors contributed equally.Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and pathways for moving between them contain enormous information about protein function that is hidden from a purely structural perspective. Molecular dynamics simulations can uncover these higher energy states but often at a prohibitively high computational cost.Here we apply our recent statistical mechanics and artificial intelligence based molecular dynamics framework for enhanced sampling of protein loops in three mutants of the protein T4 lysozyme. We are able to correctly rank these according to the stability of their excited state. By analyzing reaction coordinates, we also obtain crucial insight into why these specific perturbations in sequence space lead to tremendous variations in conformational flexibility.Our framework thus allows accurate comparison of loop conformation populations with minimal prior human bias, and should be directly applicable to a range of macromolecules in biology, chemistry and beyond.

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“…Not surprisingly, much effort has been devoted to the identification and improvement of useful CVs. Very recently, machine learning methods with different levels of complexity have been also used to this effect [3][4][5][6][7][8]. Here we describe a new method that is based on deep neural networks (NNs).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Collective Variables for Enhanced Sampling

Bonati

Rizzi

Parrinello

2020

J. Phys. Chem. Lett.

163

186

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Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize the states by a large set of descriptors and employ neural networks to compress this information on a lower-dimensional space, using Fisher's linear discriminant as objective function to maximize the discriminative power of the network. We benchmark this method on alanine dipeptide, using the non-linearly separable dataset composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing non-linear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we inspect the behavior of the neural network by studying its relation to physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.

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Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems

Abstract: AMINO uses techniques from information theory to generate new order parameters for molecular dyanmics simulations.

Cited by 53 publications

References 38 publications

Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

Making high-dimensional molecular distribution functions tractable through Belief Propagation on Factor Graphs

Discovering loop conformational flexibility in T4 lysozyme mutants through artificial intelligence aided molecular dynamics

Data-Driven Collective Variables for Enhanced Sampling

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