Machine learning in the analysis of biomolecular simulations

Kaptan, Shreyas; Vattulainen, Ilpo

doi:10.1080/23746149.2021.2006080

Cited by 16 publications

(28 citation statements)

References 92 publications

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“…As can be seen from this excellent review, machine learning has already been making a significant impact on the development of approximate methods for complex atomic systems. The innovation in the development and integration of MD simulations with deep learning can reproduce, interpret, predict, and generate data relating to the behavior of biological macromolecules. − Deep learning methods can help MD simulations excel in their efficiency and scales, with AI bridging between deep learning technologies and simulations. Challenges toward broad usage include smooth connection of AI and MD and automation of workflows.…”

Section: Appications Of Artificial Intelligence and Machine Languagementioning

confidence: 99%

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AlphaFold, Artificial Intelligence (AI), and Allostery

et al. 2022

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AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

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Section: Appications Of Artificial Intelligence and Machine Languagementioning

confidence: 99%

“…The review cited above provides additional diverse examples. Deep learning has also been already exploited in structural modeling and design ,− and analysis and linking these to function …”

Section: Appications Of Artificial Intelligence and Machine Languagementioning

confidence: 99%

AlphaFold, Artificial Intelligence (AI), and Allostery

et al. 2022

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“…The system segmentation would allow for efficient quantitative comparison of the cytosolic properties within and between distinct regions of the cell. Furthermore, machine-learning can be invoked to extract interaction patterns and other emergent behavior that might be missed by standard analysis tools ( Noé et al, 2020 ; Wang et al, 2020 ; Kaptan and Vattulainen, 2022 ). We foresee that our data sets will generate novel ways of dealing with this unprecedented level of complexity.…”

Section: Discussionmentioning

confidence: 99%

Molecular dynamics simulation of an entire cell

et al. 2023

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The ultimate microscope, directed at a cell, would reveal the dynamics of all the cell’s components with atomic resolution. In contrast to their real-world counterparts, computational microscopes are currently on the brink of meeting this challenge. In this perspective, we show how an integrative approach can be employed to model an entire cell, the minimal cell, JCVI-syn3A, at full complexity. This step opens the way to interrogate the cell’s spatio-temporal evolution with molecular dynamics simulations, an approach that can be extended to other cell types in the near future.

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“…Identification of suitable CVs is facilitated by prior knowledge and expertise in the field, but often the generic dimensionality reduction techniques are applied to the collected data. These approaches utilize a traditional principle component analysis (PCA), a time-lagged independent component analysis (tICA), various multidimensional scaling methods (e.g., sketch-MAP), and neural-network-based autoencoders , as an example of a machine learning approach. , …”

Section: Introductionmentioning

confidence: 99%

“…These approaches utilize a traditional principle component analysis (PCA), 12 a time-lagged independent component analysis (tICA), 13 various multidimensional scaling methods (e.g., sketch-MAP 14 ), and neural-network-based autoencoders 15,16 as an example of a machine learning approach. 17,18 Additionally, classical MD simulations suffer from insufficient sampling of rare events, which include most of the biologically relevant conformational transitions. 19 Therefore, if the interconversion between various states is slow, the simulations fail to sample the equilibrium distribution.…”

Section: ■ Introductionmentioning

confidence: 99%

Iterative Landmark-Based Umbrella Sampling (ILBUS) Protocol for Sampling of Conformational Space of Biomolecules

Vymětal

Vondrášek

2022

J. Chem. Inf. Model.

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Computer simulations of biomolecules such as molecular dynamics often suffer from insufficient sampling. Due to limited computational resources, insufficient sampling prevents obtaining proper equilibrium distributions of observed properties. To deal with this problem, we proposed a simulation protocol for efficient resampling of collected off-equilibrium trajectories. These trajectories are utilized for the initial mapping of the conformational space, which is later properly resampled by the introduced Iterative Landmark-Based Umbrella Sampling (ILBUS) method. Reconstruction of static equilibrium properties is achieved by the multistate Bennett acceptance ratio (MBAR) method, which enables efficient use of simulated data. The ILBUS protocol is geometry-based and does not demand any additional collective variable or a dimensional-reduction technique. The only requirement is a set of suitably spaced reference conformations, which serve as landmarks in the mapped conformational space. Additionally, the ILBUS protocol encompasses an iterative process that optimizes the force constant used in the umbrella sampling simulation. Such tuning is an inherent feature of the protocol and does not need to be performed by the user in advance. Furthermore, even the simulations with suboptimal force constants can be used in estimates by MBAR. We demonstrate the feasibility and the performance of this approach in the study of the conformational landscape of the alanine dipeptide, met-enkephalin, and adenylate kinase.

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Machine learning in the analysis of biomolecular simulations

Cited by 16 publications

References 92 publications

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold, Artificial Intelligence (AI), and Allostery

Molecular dynamics simulation of an entire cell

Iterative Landmark-Based Umbrella Sampling (ILBUS) Protocol for Sampling of Conformational Space of Biomolecules

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