Living journal of computational molecular science最新文献

{"title":"Quantifying Spatially Resolved Hydration Thermodynamics Using Grid Inhomogeneous Solvation Theory [Article v1.0].","authors":"Valentin J Egger-Hoerschinger, Franz Waibl, Vjay Molino, Helmut Carter, Monica L Fernández-Quintero, Steven Ramsey, Daniel R Roe, Klaus R Liedl, Michael K Gilson, Tom Kurtzman","doi":"10.33011/livecoms.6.1.3059","DOIUrl":"10.33011/livecoms.6.1.3059","url":null,"abstract":"<p><p>Grid Inhomogeneous Solvation Theory (GIST) is a method to compute the free energy of solvation of a solute molecule on a three-dimensional grid based on sampling from molecular dynamics (MD) simulations. The high spatial resolution of the GIST output, as well as the decomposition into energy and entropy contributions, allow for highly detailed analyses of solvation around both proteins and small molecules. However, this versatility also comes with a significant entry barrier for new users. In this tutorial, we aim to guide the reader through the most common steps involved in a GIST analysis using the streptavidin-biotin complex as a demonstrative system. To this end, Jupyter notebooks and a Python package (gisttools) are provided to simplify the analysis. Furthermore, we discuss the theory of GIST with a focus on practical aspects. We highlight potential pitfalls and provide strategies to avoid technical difficulties. This tutorial assumes familiarity with molecular dynamics simulations and the AmberTools package.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13038301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147596425","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":"Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields [Article v1.0].","authors":"Chapin E Cavender, David A Case, Julian C-H Chen, Lillian T Chong, Daniel A Keedy, Kresten Lindorff-Larsen, David L Mobley, O H Samuli Ollila, Chris Oostenbrink, Paul Robustelli, Vincent A Voelz, Michael E Wall, David C Wych, Michael K Gilson","doi":"10.33011/livecoms.6.1.3871","DOIUrl":"10.33011/livecoms.6.1.3871","url":null,"abstract":"<p><p>This review article provides an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss what the observables are, what they tell us about structure and dynamics, what makes them useful for assessing force field accuracy, and how they can be connected to molecular dynamics simulations carried out using the force field one wishes to benchmark. We also touch on best practices for setup and analysis of benchmark simulations. We hope this article will be particularly useful to computational researchers and trainees who develop, benchmark, or use protein force fields or machine learning models that generate protein ensembles.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146031885","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":"Running Gaussian-accelerated Molecular Dynamics Simulations in NAMD [Article v1.0].","authors":"Haley M Michel, Marcelo D Polêto, Justin A Lemkul","doi":"10.33011/livecoms.6.1.3815","DOIUrl":"https://doi.org/10.33011/livecoms.6.1.3815","url":null,"abstract":"<p><p>Gaussian-accelerated molecular dynamics (GaMD) simulations are an advanced technique that enhances the sampling of configurational space by applying biasing potentials that reduce energy barriers, enabling faster exploration of the free energy landscape. This tutorial demonstrates the application of GaMD to the alanine dipeptide, serving as an accessible model system, and guides users through all GaMD simulation stages: conventional MD, GaMD equilibration, GaMD production, and reweighting. Users will gain practical insights into the preparation of input files, monitoring of GaMD convergence, and analysis of free energy profiles using PyReweighting. We make a particular effort to connect the underlying theory with the GaMD workflow. This tutorial is intended for users with prior molecular dynamics experience, Linux and command-line navigation, and with basic Python knowledge. The step-by-step instructions and accompanying scripts aim to streamline the GaMD workflow, making it accessible for the broader research community to explore enhanced sampling for a range of biomolecular systems.</p>","PeriodicalId":74084,"journal":{"name":"Living journal of computational molecular science","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981414","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":"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}