Collective variable-based enhanced sampling and machine learning

Chen, Ming

doi:10.1140/epjb/s10051-021-00220-w

Cited by 53 publications

(56 citation statements)

References 171 publications

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“…However, the

dimensional space is too large to efficiently sample functionally relevant processes. Collective coordinates should be low-dimensional representations of the complex biomolecular process that is to be sampled [ 126 , 127 ]. The transformation to CVs should be a differential function of the

atomic coordinates.…”

Section: Selected Applications Of Machine Learning In Computational B...mentioning

confidence: 99%

Protein Function Analysis through Machine Learning

et al. 2022

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Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics.

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“…However, the

atomic coordinates.…”

Section: Selected Applications Of Machine Learning In Computational B...mentioning

confidence: 99%

Protein Function Analysis through Machine Learning

et al. 2022

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“…Fortunately, there have been several efforts to develop methods that circumvent the “curse of dimensionality” by either enabling the use of a greater number of CVs or through the design of complex CV definitions that adequately capture the slow degrees of freedom. , With regard to antibiotic permeation, the latest development has been the application of the temperature accelerated sliced sampling (TASS) method to the study of ciprofloxacin permeation across OmpF. The approach enables the inclusion of rotational and translational degrees of freedom of the antibiotic, the antibiotic-water coordination number, and protein side chain motions as indicated in Figure B.…”

Section: Understanding Antibiotic Permeation Through Bacterial Nanoporesmentioning

confidence: 99%

Atomistic Simulation of Molecules Interacting with Biological Nanopores: From Current Understanding to Future Directions

2022

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Biological nanopores have been at the focus of numerous studies due to their role in many biological processes as well as their (prospective) technological applications. Among many other topics, recent studies on nanopores have addressed two key areas: antibiotic permeation through bacterial channels and sensing of analytes. Although the two areas are quite far apart in terms of their objectives, in both cases atomistic simulations attempt to understand the solute dynamics and the solute−protein interactions within the channel lumen. While decades of studies on various channels have culminated in an improved understanding of the key molecular factors and led to practical applications in some cases, successful utilization is limited. In this Perspective we summarize recent progress in understanding key issues in molecular simulations of antibiotic translocation and in the development of nanopore sensors. Moreover, we comment on possible advancements in computational algorithms that can potentially resolve some of the issues.

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“…Temporal evolution along CVs provide thermodynamic and kinetic informations about conformational changes. 5–7 It is worth mentioning that the use of buzz-word “automatic identification” to highlight new methods for CV discovery from MD simulation gives a false impression i.e. selection of good CV is a streamlined and well-defined process.…”

Section: Introductionmentioning

confidence: 99%