Living journal of computational molecular science最新文献

{"title":"Computing absolute binding affinities by Streamlined Alchemical Free Energy Perturbation [Article v1.0]","authors":"Ezry Santiago-McRae, Mina Ebrahimi, Jesse W Sandberg, Grace Brannigan, Jérôme Hénin","doi":"10.33011/livecoms.5.1.2067","DOIUrl":"https://doi.org/10.33011/livecoms.5.1.2067","url":null,"abstract":"","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0].","authors":"Anthony T Bogetti, Jeremy M G Leung, John D Russo, She Zhang, Jeff P Thompson, Ali S Saglam, Dhiman Ray, Barmak Mostofian, A J Pratt, Rhea C Abraham, Page O Harrison, Max Dudek, Paul A Torrillo, Alex J DeGrave, Upendra Adhikari, James R Faeder, Ioan Andricioaei, Joshua L Adelman, Matthew C Zwier, David N LeBard, Daniel M Zuckerman, Lillian T Chong","doi":"10.33011/livecoms.5.1.1655","DOIUrl":"10.33011/livecoms.5.1.1655","url":null,"abstract":"<p><p>The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present two sets of tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. The first set of more basic tutorials describes a range of simulation types, from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding. The second set ecompasses six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for larger systems and/or slower processes. The advanced tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of \"binless\" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling for systems biology models. Applications of the advanced tutorials include atomistic and non-spatial models, and consist of complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics or systems biology simulations.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10191340/pdf/nihms-1894701.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9496356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Molecules and Materials.","authors":"Andrew D White","doi":"10.33011/livecoms.3.1.1499","DOIUrl":"10.33011/livecoms.3.1.1499","url":null,"abstract":"<p><p>Deep learning is becoming a standard tool in chemistry and materials science. Although there are learning materials available for deep learning, none cover the applications in chemistry and materials science or the peculiarities of working with molecules. The textbook described here provides a systematic and applied introduction to the latest research in deep learning in chemistry and materials science. It covers the math fundamentals, the requisite machine learning, the common neural network architectures used today, and the details necessary to be a practitioner of deep learning. The textbook is a living document and will be updated as the rapidly changing deep learning field evolves.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10727448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69480860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Sampling Methods for Molecular Dynamics Simulations [Article v1.0]","authors":"J'erome H'enin, T. Lelièvre, M. Shirts, O. Valsson, L. Delemotte","doi":"10.33011/livecoms.4.1.1583","DOIUrl":"https://doi.org/10.33011/livecoms.4.1.1583","url":null,"abstract":"Enhanced sampling methods for molecular dynamics simulations [Article v1.0] Jérôme Hénin1,2*, Tony Lelièvre3*, Michael R. Shirts4*, Omar Valsson5,6*, Lucie Delemotte7* 1Laboratoire de Biochimie Théorique UPR 9080, CNRS, Paris, France; 2Institut de Biologie Physico-Chimique–Fondation Edmond de Rothschild, Paris, France; 3CERMICS, Ecole des Ponts, INRIA, Marne-la-Vallée, France; 4Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, USA, 80309; 5University of North Texas, Department of Chemistry, Denton, TX, USA; 6Max Planck Institute for Polymer Research, Mainz, Germany; 7KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49356292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introduction to in silico synthesis of polymers via PySIMM [Article v1.0]","authors":"Alexander G Demidov, B. Perera, Michael E Fortunato, Sibo Lin, C. Colina","doi":"10.33011/livecoms.4.1.1561","DOIUrl":"https://doi.org/10.33011/livecoms.4.1.1561","url":null,"abstract":"","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69480873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1].","authors":"David F Hahn, Christopher I Bayly, Hannah E Bruce Macdonald, John D Chodera, Antonia S J S Mey, David L Mobley, Laura Perez Benito, Christina E M Schindler, Gary Tresadern, Gregory L Warren","doi":"10.33011/livecoms.4.1.1497","DOIUrl":"10.33011/livecoms.4.1.1497","url":null,"abstract":"<p><p>Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (<i>benchmarking</i>) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (<b>PLBenchmarks</b>) and an open source toolkit for implementing standardized best practices assessments (<b>arsenic</b>) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9662604/pdf/nihms-1700409.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40477433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinase similarity assessment pipeline for off-target prediction [v1.0]","authors":"Talia B. Kimber, Dominique Sydow, Andrea Volkamer","doi":"10.33011/livecoms.3.1.1599","DOIUrl":"https://doi.org/10.33011/livecoms.3.1.1599","url":null,"abstract":"","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69480867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0].","authors":"Jack A Henderson, Ruibin Liu, Julie A Harris, Yandong Huang, Vinicius Martins de Oliveira, Jana Shen","doi":"10.33011/livecoms.4.1.1563","DOIUrl":"10.33011/livecoms.4.1.1563","url":null,"abstract":"<p><p>Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict p<i>K</i> _a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910290/pdf/nihms-1869625.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10712614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How To Be a Good Member of a Scientific Software Community [Article v1.0].","authors":"A. Grossfield","doi":"10.31219/osf.io/kgr45","DOIUrl":"https://doi.org/10.31219/osf.io/kgr45","url":null,"abstract":"Software is ubiquitous in modern science - almost any project, in almost any discipline, requires some code to work. However, many (or even most) scientists are not programmers, and must rely on programs written and maintained by others. A crucial but often neglected part of a scientist's training is learning how to use new tools, and how to exist as part of a community of users. This article will discuss key behaviors that can make the experience quicker, more efficient, and more pleasant for the user and developer alike.","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"3 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48362816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in atomistic simulation software usage [Article v1.0]","authors":"Leopold Talirz, L. Ghiringhelli, B. Smit","doi":"10.33011/livecoms.3.1.1483","DOIUrl":"https://doi.org/10.33011/livecoms.3.1.1483","url":null,"abstract":"Driven by the unprecedented computational power available to scientific research, the use of computers in solid-state physics, chemistry and materials science has been on a continuous rise. This review focuses on the software used for the simulation of matter at the atomic scale. We provide a comprehensive overview of major codes in the field, and analyze how citations to these codes in the academic literature have evolved since 2010. An interactive version of the underlying data set is available at https://atomistic.software .","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48561263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}