From 0a151af38ce38ecf75a983a14808792290d4c1d2 Mon Sep 17 00:00:00 2001 From: jmichel80 Date: Thu, 14 Dec 2023 13:53:51 +0000 Subject: [PATCH] preparing V1 release --- LIVECOMS/02_funnel_metad/livecoms.tex | 6 +++-- LIVECOMS/03_steered_md/livecoms.tex | 11 +++++---- LIVECOMS/04_fep/livecoms.tex | 16 ++++++------ LIVECOMS/main.tex | 33 ++++++++++++------------- LIVECOMS/references.bib | 34 ++++++++++++++++++++------ releases/LiveCoMS_Article_v1.pdf | Bin 0 -> 7586108 bytes releases/header_v1.jpg | Bin 0 -> 282933 bytes 7 files changed, 60 insertions(+), 40 deletions(-) create mode 100644 releases/LiveCoMS_Article_v1.pdf create mode 100644 releases/header_v1.jpg diff --git a/LIVECOMS/02_funnel_metad/livecoms.tex b/LIVECOMS/02_funnel_metad/livecoms.tex index a79df69..6117d6d 100644 --- a/LIVECOMS/02_funnel_metad/livecoms.tex +++ b/LIVECOMS/02_funnel_metad/livecoms.tex @@ -7,7 +7,9 @@ \subsubsection{Introduction}\label{Introduction}} and unbound phases, as well as increase the rate of convergence of the binding free energy, the exploration of the ligand in 3D space is limited by funnel-shaped restraints. -\emph{fun-metaD} was originally implemented by Limogelli \emph{et al}~\cite{Limongelli2013}. This tutorial is based on the implementation described by Rhys \emph{et al}~\cite{Evans2020}, and Saleh \emph{et al}~\cite{Saleh2017}. The main difference between the original and later implementations is the functional form of the funnel restraints: the original \emph{fun-metaD} relied on a cone and a cylinder joined to make a funnel using a step function, while the new implementation uses a single +Readers that lack familiarity with metadynamics may wish to study first a simple \href{https://biosimspace.openbiosim.org/tutorials/metadynamics.html}{BioSimSpace metadynamics tutorials} for a alanine dipeptide molecule in the gas phase. + +\emph{fun-metaD} was originally implemented by Limogelli et al.~\cite{Limongelli2013}. This tutorial is based on the implementation described by Rhys et al~\cite{Evans2020}, and Saleh et al~\cite{Saleh2017}. The main difference between the original and later implementations is the functional form of the funnel restraints: the original \emph{fun-metaD} relied on a cone and a cylinder joined to make a funnel using a step function, while the new implementation uses a single sigmoid function. The Limogelli implementation also requires the protein to be realigned with a reference structure to keep the funnel strictly in place over the binding site, which negatively affects performance. The implementation described here allows the funnel to move with the protein. A challenge with using \emph{fun-metaD} for ABFE calculations is that it can be difficult to select optimised funnel parameters, and tedious to write multiple input files for PLUMED. To facilitate use of \emph{fun-metaD} by non-experts BioSimSpace implements automated setup algorithms for selecting funnel parameters, and producing input files. @@ -55,7 +57,7 @@ \subsubsection{Theory}\label{the-theory}} In order to have a good separation between the bound and unbound phases for the ABFE estimations, the funnel needs to point \emph{out}, with the narrow end in the solvent, and away from any protein residues. The BSS automated funnel assignment code does this generally well. Typically the vectors picked for defining the $P_{0}$ and $P_{1}$ points offer an unobstructed exit path for the ligand. It is nonetheless still a good idea to visually check the proposed funnel, especially for new protein systems. This can be done through use of the funnel visualisation functionality within BSS (see below). -The size of the funnel radius has to be determined on a case-by-case basis. The metadynamics code tracks the exploration of the center of mass of the ligand, so the volume that the small molecule will explore is much larger than implied by the visualization of the funnel. There is usually only one binding site and the funnel should enclose only it, excluding other protein features, by setting a small value of $h$. This helps accelerate convergence by preventing the ligand from exploring irrelevant regions in the free energy surface (FES). +The size of the funnel radius has to be determined on a case-by-case basis. The metadynamics code tracks the center of mass the ligand, therefore ligand atoms are able to sample positions outside the visualised funnel, provided the center of mass remains inside the funnel. the volume that the small molecule will explore is much larger than implied by the visualization of the funnel. There is usually only one binding site and the funnel should enclose only it, excluding other protein features, by setting a small value of $h$. This helps accelerate convergence by preventing the ligand from exploring irrelevant regions in the free energy surface (FES). \hypertarget{setupfunmetad}{% \subsubsection{Setting up funnel metadynamics simulations}\label{setupfunmetad}} diff --git a/LIVECOMS/03_steered_md/livecoms.tex b/LIVECOMS/03_steered_md/livecoms.tex index eaf4e14..5a9f6e6 100644 --- a/LIVECOMS/03_steered_md/livecoms.tex +++ b/LIVECOMS/03_steered_md/livecoms.tex @@ -1,16 +1,16 @@ \subsubsection{Introduction} -Many relevant biological processes, such as transmembrane permeation or transitions between active and inactive protein conformations, occur on a timescale of $\mu$ s-s~\cite{Zwier2010,Choy2017,Wells2007}. However, even with GPU acceleration, the timescales accessible via MD simulations are only a few hundred ns/day~\cite{HecBioSim_benchmark}. One of the methods to get around this limitation is steered molecular dynamics (sMD). sMD involves applying a harmonic restraint to bias the system towards a conformation defined through one or more collective variables (CVs): +Many relevant biological processes, such as transmembrane permeation or transitions between active and inactive protein conformations, occur on a timescale of microseconds to seconds ~\cite{Zwier2010,Choy2017,Wells2007}. However, even with GPU acceleration, the timescales accessible via MD simulations are only a few hundred ns/day ~\cite{HecBioSim_benchmark}. One of the methods to get around this limitation is steered molecular dynamics (sMD). sMD involves applying a harmonic restraint to bias the system towards a conformation defined through one or more collective variables (CVs): \begin{equation} V(\vec{s},t) = \frac{1}{2} \kappa(t) ( \vec{s} - \vec{s}_0(t) )^2, \label{eq:sMD} \end{equation} -where $\kappa$ is the force constant, $\vec{s}_0$ is the expected CV value at a specific timestep, and $\vec{s}$ is the actual CV value at that timestep\cite{Isralewitz2001,Tribello2014}. +where $\kappa$ is the force constant, $\vec{s}_0$ is the expected CV value at a specific timestep, and $\vec{s}$ is the actual CV value at that timestep \cite{Isralewitz2001,Tribello2014}. This section of the tutorial summarizes how to use BioSimSpace to set up and run sMD simulations. BSS prepares input files for PLUMED, which is the software that works together with MD engines such as AMBER and GROMACS to add the restraint in eq~\ref{eq:sMD}. -We use protein tyrosine phosphatase 1B (PTP1B) as the system of choice for this tutorial. It is a negative regulator of insulin signalling~\cite{sMD_ptp1b-diabetes} and is an attractive target for type II diabetes~\cite{sMD_Wiesman}. The function of PTP1B depends on the conformation of its WPD loop, which can be closed (active) or open (inactive) (Figure~\ref{fig:ptp1b}). The WPD loop of PTP1B opens and closes on a $\mu$s timescale~\cite{Choy2017}, and therefore this transition is not observed on conventional computational timescales. +We use protein tyrosine phosphatase 1B (PTP1B) as the system of choice for this tutorial. It is a negative regulator of insulin signalling ~\cite{sMD_ptp1b-diabetes}, and is an attractive target for type II diabetes ~\cite{sMD_Wiesman}. The function of PTP1B depends on the conformation of its WPD loop, which can be closed (active) or open (inactive) (Figure~\ref{fig:ptp1b}). The WPD loop of PTP1B opens and closes on a $\mu$s timescale ~\cite{Choy2017}, and therefore this transition is not observed on conventional computational timescales. \begin{figure}[htp] \includegraphics[width=\linewidth]{LIVECOMS/03_steered_md/open-close.png} @@ -40,8 +40,9 @@ \subsubsection{sMD trajectory analysis} The notebook also illustrates a "failed" steered MD trajectory, where the steering duration and force were insufficient to reach the target CV value. -\subsubsection{Markov State Models} -While the information provided here focuses on running sMD simulations with BSS, there are multiple potential applications, such as studying membrane permeability~\cite{Wells2007} or ligand residence time\cite{Potterton2019}. Another use is for the additional exploration of conformational space for predicting allosteric modulation using Markov State Models (MSMs). MSMs give the probability of protein conformations and therefore can be used to model how a ligand affects the conformation ensemble of a target (e.g. whether it decreases the active state probability and therefore is an allosteric inhibitor). There is a lot to consider when building MSMs, and the method is not covered in this tutorial. Here the Python library \href{http://emma-project.org/latest/}{PyEMMA} was used, which has extensive examples and documentation\cite{Wehmeyer_2019}. The integration of sMD in this allosteric modulation prediction workflow is illustrated in Figure \ref{fig:ensemble-protocol}. \href{https://github.com/OpenBioSim/BioSimSpaceTutorials/blob/main/03_steered_md/02_trajectory_analysis.ipynb}{02-trajectory-analysis} shows how the sMD trajectory can be sampled to extract a range of protein conformations. Hardie \emph{et al} report a detailed study of allosteric modulators of PTP1B using this sMD/MSM methodology~\cite{Hardie2023}. +\subsubsection{Example application - combining steered MD with Markov State Modeling} +While the information provided here focuses on running sMD simulations with BSS, there are multiple potential applications, such as studying membrane permeability~\cite{Wells2007} or ligand residence time\cite{Potterton2019}. Here we briefly highlight one application of sMD simulations enabled by BioSimSpace in the AMMo software project. AMMo ("Allostery in Markov Models") was developed to evaluate the allosteric effects of protein mutations or ligand binding by combining sMD with Markov State Models (MSMs). MSMs are used to give the probability of protein conformations and therefore can be used to model how a ligand affects the conformation ensemble of a target (e.g. whether the presence of a ligand decreases the active state probability and therefore is an allosteric inhibitor). + There is a lot to consider when building MSMs, and the method is not covered in this tutorial. AMMo uses the Python library \href{http://emma-project.org/latest/}{PyEMMA} was to implement MSMs. Extensive examples and documentation for PyEMMA are available elsewhere ~\cite{Wehmeyer_2019}. The integration of sMD with MSM in this allosteric modulation prediction workflow is illustrated in Figure \ref{fig:ensemble-protocol}. The notebook \href{https://github.com/OpenBioSim/BioSimSpaceTutorials/blob/main/03_steered_md/02_trajectory_analysis.ipynb}{02-trajectory-analysis} shows how a sMD trajectory can be sampled to extract a range of protein conformations suitable for inputs to an MSM workflow. Hardie et al. report a detailed study of allosteric modulators of PTP1B using this sMD/MSM methodology ~\cite{Hardie2023} and notebooks for the PTP1B case study are available on the \href{https://github.com/michellab/AMMo/tree/main/examples/ptp1b}{GitHub} page for AMMo. \begin{figure}[htp] \includegraphics[width=\linewidth]{LIVECOMS/03_steered_md/ensemble-md-protocol.png} diff --git a/LIVECOMS/04_fep/livecoms.tex b/LIVECOMS/04_fep/livecoms.tex index a00af27..13dad21 100644 --- a/LIVECOMS/04_fep/livecoms.tex +++ b/LIVECOMS/04_fep/livecoms.tex @@ -12,7 +12,7 @@ \subsubsection{Introduction} Computational chemists can support the study of structure-activity relationships in medicinal chemistry by making computer models that can predict the binding affinity of ligands to proteins. Alchemical free energy (AFE) methods are a popular class of methodologies to do so. Some -introductory reading is recommended~\cite{mey2020best, cournia_allen_sherman_2017, kuhn_firth-clark_tosco_mey_mackey_michel_2020, Hahn2022}. +introductory reading is recommended ~\cite{mey2020best, cournia_allen_sherman_2017, kuhn_firth-clark_tosco_mey_mackey_michel_2020, Hahn2022}. This tutorial covers the basic principles of alchemical free energy calculations with BioSimSpace; how to setup, simulate, and analyze alchemical Relative Binding Free Energy (RBFE) calculations for congeneric series of protein-ligand complexes; how to set up and analyze alchemical ABFE calculations of a ligand bound to a protein. The notebooks prompt the readers to complete a series of exercises that typically involve completing cells to test their understanding of the material presented. @@ -21,7 +21,7 @@ \subsubsection{Setting up AFE calculations using BioSimSpace} The first notebook of this tutorial \href{https://github.com/OpenBioSim/biosimspace_tutorials/blob/main/04_fep/01_intro_to_alchemy/alchemical_introduction.ipynb}{alchemical-introduction} introduces the basic functionality available in BioSimSpace to implement AFE calculations. The notebook first introduces the functionality of the \href{https://biosimspace.openbiosim.org/api/index_Align.html}{BSS.Align} module. BSS.Align implements a variety of mapping algorithms based on maximum common substructure searches between a supplied pair of molecules to generate a 'merged' molecule used to describe an alchemical transformation. This is used to generate a merged molecule that describes the alchemical transformation of ethane into methanol. -Next, the use of \href{https://biosimspace.openbiosim.org/api/generated/BioSimSpace.Protocol.FreeEnergy.html#BioSimSpace.Protocol.FreeEnergy}{BSS.Protocol.FreeEnergy} is illustrated using the relative hydration free energy of ethanol to methane as an example, using the AFE engines \emph{SOMD}\cite{Calabr2016} or \emph{mdrun}. +Next, the use of \href{https://biosimspace.openbiosim.org/api/generated/BioSimSpace.Protocol.FreeEnergy.html#BioSimSpace.Protocol.FreeEnergy}{BSS.Protocol.FreeEnergy} is illustrated using the relative hydration free energy of ethanol to methane as an example, using the AFE engines \emph{SOMD} \cite{Calabr2016} or \emph{mdrun}. The AFE simulations can in principle be executed directly from the notebook as a set of independent MD simulations run serially, but this is often too slow to be practical. The next section describes protocols to submit calculations in parallel on HPC resources. Finally, the notebook describes the setup of the relative binding free energy calculation of benzene to o-xylene bound to the protein T4 lyzozyme mutant. \\ @@ -36,7 +36,7 @@ \subsubsection{Setting up AFE calculations using BioSimSpace} \subsubsection{RBFE calculation pipelines} -The second notebook of this tutorial \href{https://github.com/OpenBioSim/biosimspace_tutorials/blob/main/04_fep/02_RBFE/01_setup_rbfe.ipynb}{01-setup-rbfe.ipynb} shows how to design an RBFE campaign for a congeneric series of protein-ligand complexes. This is illustrated using a dataset of ligands for the protein TYK2, taken from the benchmark set of Wang et al.\cite{Wang2015} +The second notebook of this tutorial \href{https://github.com/OpenBioSim/biosimspace_tutorials/blob/main/04_fep/02_RBFE/01_setup_rbfe.ipynb}{01-setup-rbfe.ipynb} shows how to design an RBFE campaign for a congeneric series of protein-ligand complexes. This is illustrated using a dataset of ligands for the protein TYK2, taken from the benchmark set of Wang et al. \cite{Wang2015} First, a drop-down menu is presented to the user to enable the selection of a variety of configuration settings (such as the choice of forcefields to use for the ligands, the protein, or the FEP engine to select). In BSS 2023.3.0 the FEP engines \emph{SOMD} and \emph{mdrun} (from the GROMACS software suite) are supported. An \href{https://github.com/michellab/BioSimSpace/tree/feature-amber-fep}{experimental feature branch} that implements support for RBFE calculations with the MD engine pmemd from the AMBER software suite is also available. Next, \href{https://biosimspace.openbiosim.org/api/generated/BioSimSpace.Align.generateNetwork.html}{BSS.Align.generateNetwork} is used to interface with the LOMAP software~\cite{Liu2013} to propose a network of relative transformations that span the provided ligand dataset. The notebook illustrates interactive plotting of the proposed network and how to make manual adjustments such as adding or deleting edges or inserting new ligands into the network. Once the user is satisfied with the chosen FEP network, setup instructions are saved to disk. @@ -68,7 +68,7 @@ \subsubsection{ABFE calculations} % The fourth notebook \href{https://github.com/OpenBioSim/biosimspace_tutorials/blob/main/04_fep/03_ABFE/01_setup_abfe.ipynb}{01-setup-abfe} describes how to set up alchemical ABFE calculations with BSS for use with either \emph{mdrun} or \emph{SOMD}. This free energy functionality is currently implemented in Exscientia's sandpit area of this version of BioSimSpace. Sandpits are used to test experimental features without accidentally breaking core functionality of the toolkit. -Although RBFE calculations can be very useful in drug discovery, several important problems lie outside the scope of standard RBFE calculations. These include the calculation of the binding free energies of structurally dissimilar ligands to a common target; the calculation of the binding free energies of the same ligand to the same protein with different binding poses; the calculation of the binding free energies of a single ligand to a range of targets, as would be required to optimize selectivity or promiscuity. These quantities can be calculated using alchemical ABFE calculations because they utilize a more general \emph{double decoupling} thermodynamic cycle than in RBFE calculations.\cite{gilson_statistical-thermodynamic_1997} This involves entirely removing the ligand's intermolecular interactions in the presence of restraints between the ligand and receptor, as shown in Figure \ref{abfe_fig}A. +Although RBFE calculations can be very useful in drug discovery, several important problems lie outside the scope of standard RBFE calculations. These include the calculation of the binding free energies of structurally dissimilar ligands to a common target; the calculation of the binding free energies of the same ligand to the same protein with different binding poses; the calculation of the binding free energies of a single ligand to a range of targets, as would be required to optimize selectivity or promiscuity. These quantities can be calculated using alchemical ABFE calculations because they utilize a more general \emph{double decoupling} thermodynamic cycle than in RBFE calculations \cite{gilson_statistical-thermodynamic_1997}. This involves entirely removing the ligand's intermolecular interactions in the presence of restraints between the ligand and receptor, as shown in Figure \ref{abfe_fig}A. \begin{figure}[htp] \includegraphics[width=\linewidth]{LIVECOMS/04_fep/abfe-tutorial.png} @@ -76,12 +76,12 @@ \subsubsection{ABFE calculations} \label{abfe_fig} \end{figure} -The notebook describes the use of \textit{BSS.Align.decouple} to tag a molecule in a BSS system that should have all its intermolecular interactions removed to compute its absolute binding free energy. This is illustrated here with the use of the ligand MIF180 bound to the protein MIF.\cite{Qian2019, Clark2023} +The notebook describes the use of \textit{BSS.Align.decouple} to tag a molecule in a BSS system that should have all its intermolecular interactions removed to compute its absolute binding free energy. This is illustrated here with the use of the ligand MIF180 bound to the protein MIF \cite{Qian2019, Clark2023}. -Next, it is shown how to run and analyse a simulation of the fully-interacting protein-ligand complex to generate the required intermolecular restraints. Both actions are handled using \textit{BSS.FreeEnergy.RestraintSearch}. Currently, only the common Boresch restraints are supported,\cite{boresch_absolute_2003} but we plan to integrate multiple distance restraints in the future.\cite{Clark2023} -The notebook illustrates the automated generation of Boresch restraints with BSS. The algorithm implemented aims to select stable restraints which mimic strong native receptor-ligand interactions. The notebook includes visualization of the chosen restraints before free energy inputs are prepared, which allows the user to identify cases where the selected restraints may not be optimal, or where symmetry corrections may be required.\cite{duboue-dijon_building_2021} The notebook then demonstrates the use of \textit{BSS.FreeEnergy.AlchemicalFreeEnergy} to generate input files for the engines SOMD or GROMACS. This class in the Exscientia sandpit contains all of the functionality from \textit{BSS.FreeEnergy.Relative} in the main version of the code, as well as additional functionality required for ABFE calculations. +Next, it is shown how to run and analyse a simulation of the fully-interacting protein-ligand complex to generate the required intermolecular restraints. Both actions are handled using \textit{BSS.FreeEnergy.RestraintSearch}. The popular 6-degrees of freedom Boresch restraints are supported \cite{boresch_absolute_2003}, as well as a Clark's multiple distance restraints (MDR) methodology \cite{Clark2023}. +The notebook illustrates the automated generation of Boresch or MDR restraints with BSS. The algorithm implemented aims to select stable restraints which mimic strong native receptor-ligand interactions. The notebook includes visualization of the chosen restraints before free energy inputs are prepared, which allows the user to identify cases where the selected restraints may not be optimal, or where symmetry corrections may be required \cite{duboue-dijon_building_2021}. The notebook then demonstrates the use of \textit{BSS.FreeEnergy.AlchemicalFreeEnergy} to generate input files for the engines SOMD or GROMACS. This class in the Exscientia sandpit contains all of the functionality from \textit{BSS.FreeEnergy.Relative} in the main version of the code, as well as additional functionality required for ABFE calculations. -Since ABFE calculations can be time consuming we recommend parallel execution of the different legs of the thermodynamic cycle using an approach similar to that used in the RBFE tutorial. +Since ABFE calculations can be time-consuming we recommend parallel execution of the different legs of the thermodynamic cycle using an approach similar to that used in the RBFE tutorial. \\ The fifth notebook \href{https://github.com/OpenBioSim/biosimspace_tutorials/blob/main/04_fep/03_ABFE/02_analysis_abfe.ipynb}{02-analysis-abfe} describes how to analyse an ABFE calculation to estimate the free energy of binding of a ligand. Sample output simulation data are provided for each leg of the double decoupling thermodynamic cycle and analysed using \textit{BSS.FreeEnergy.AlchemicalFreeEnergy.Analyse} to plot potentials of mean forces. The standard free energy of binding is then obtained by summing the free energy changes from each leg and adding a standard state correction term for the use of Boresch restraints, along with any symmetry corrections required. The notebook also describes how to carry out convergence analyses (see Figure \ref{abfe_fig}B) to assess the robustness of the ABFE estimates. diff --git a/LIVECOMS/main.tex b/LIVECOMS/main.tex index d61cfe7..83bf689 100644 --- a/LIVECOMS/main.tex +++ b/LIVECOMS/main.tex @@ -3,7 +3,7 @@ %%% ADAPTED FROM ELIFE ARTICLE TEMPLATE (8/10/2017) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% PREAMBLE -\documentclass[9pt,tutorial]{livecoms} +\documentclass[9pt,tutorial,pubversion]{livecoms} % Use the 'onehalfspacing' option for 1.5 line spacing % Use the 'doublespacing' option for 2.0 line spacing % Use the 'lineno' option for adding line numbers. @@ -41,7 +41,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% ARTICLE SETUP %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -\title{A suite of tutorials for the BioSimSpace framework for interoperable biomolecular simulation [Article v\versionnumber]} +\title{A Suite of Tutorials for the BioSimSpace Framework for Interoperable Biomolecular Simulation [Article v\versionnumber]} % Everyone in alphabetical order other than Lester \author[1,2*]{Lester O. Hedges} @@ -73,12 +73,12 @@ \corr{julien.michel@ed.ac.uk}{JM} % Correspondence emails. FMS and FS are the appropriate authors initials. \corr{lester@openbiosim.org}{LH} -%\orcid{Lester Hedges}{EEEE-FFFF-GGGG-HHHH} -%\orcid{Matthew Burman}{EEEE-FFFF-GGGG-HHHH} +\orcid{Lester Hedges}{0000-0002-5624-0500} +\orcid{Matthew Burman}{0000-0001-5360-0024} \orcid{Sofia Bariami}{0000-0002-5240-7684} \orcid{Finlay Clark}{0000-0003-0474-5475} -%\orcid{Benjamin P. Cossins}{EEEE-FFFF-GGGG-HHHH} -%\orcid{Adele Hardie}{EEEE-FFFF-GGGG-HHHH} +\orcid{Benjamin P. Cossins}{0000-0002-6699-8833} +\orcid{Adele Hardie}{0009-0002-9943-9920} \orcid{Anna M. Herz}{0000-0003-2831-6691} \orcid{Dominykas Lukauskis}{0000-0002-4999-2691} \orcid{Antonia Mey}{0000-0001-7512-5252} @@ -104,13 +104,13 @@ %%% Fill out these parameters when available %%% These are used when the "pubversion" option is invoked %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -\pubDOI{10.XXXX/YYYYYYY} -\pubvolume{} -\pubissue{} -\pubyear{} -\articlenum{} -\datereceived{Day Month Year} -\dateaccepted{Day Month Year} +\pubDOI{10.33011/livecoms.5.1.2375} +\pubvolume{5} +\pubissue{1} +\pubyear{2023} +\articlenum{2375} +\datereceived{20 July 2023} +\dateaccepted{13 December 2023} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% ARTICLE START @@ -122,7 +122,7 @@ \maketitle \begin{abstract} -This tutorial serves as a getting-started guide for BioSimSpace (BSS), an interoperable molecular simulation framework, that allows simulations with different sets of molecular dynamics software packages. This tutorial will cover four main use cases for BioSimSpace. The introductory tutorial introduces the basic structure of BioSimSpace, how to use the API to access functionality, and how to write code for setting up and running standard molecular dynamics simulations. Three advanced use cases of BSS are then provided, describing how to set up and run a funnel metadynamics simulation, steered molecular dynamics in combination with Markov State modeling, and relative or absolute alchemical binding free energy calculations. +This tutorial serves as a getting-started guide for BioSimSpace (BSS), an interoperable molecular simulation framework, that allows simulations with different sets of molecular dynamics software packages. This tutorial will cover four main use cases for BioSimSpace. The introductory tutorial introduces the basic structure of BioSimSpace, how to use the API to access functionality, and how to write code for setting up and running standard molecular dynamics simulations. Three advanced use cases of BSS are then provided, describing how to set up and run a funnel metadynamics simulation, steered molecular dynamics, and relative or absolute alchemical binding free energy calculations. \end{abstract} \end{frontmatter} @@ -137,8 +137,7 @@ \section{Introduction} \section{Prerequisites and Scope} %TODO expand this: \subsection{Background knowledge} -To get the most out of this tutorial the reader should be familiar with Python and how to use Jupyter notebooks. For the introductory set of tutorials a background in biomolecular dynamics simulations is expected,~\cite{Huggins2018} and if you are unfamiliar with this topic the best practices guide on foundations in biomolecular simulations is a good starting point~\cite{Braun2019}. Tutorial 2 on funnel metadynamics expects some background understanding of funnel metadynamics; a good starting point for studying this topic can be found in ref.~\cite{Limongelli2013}. Tutorial 3, expects knowledge on steered MD and Markov state modeling. If you are unfamiliar with these topics Isralewitz \textit{et al.} provides a good introduction to steered MD~\cite{Isralewitz2001} and for more details on how to build MSM you can refer to the pyEMMA tutorial~\cite{Wehmeyer_2019} and a general introduction to the topic by Husic \textit{et al.}~\cite{sMD_Husic-msm}. -Tutorial 4 covers how to use BSS to run alchemical free energy calculations. The best-practice guide by Mey \textit{et al.} for alchemical free energy calculations provides a broad introduction and overview of this method~\cite{mey2020best}. +To get the most out of this tutorial the reader should have at least Intermediate proficiency in Python and be familiar with Jupyter notebooks. For the introductory set of tutorials a background in biomolecular dynamics simulations is expected~\cite{Huggins2018}, and if you are unfamiliar with this topic the best practices guide on foundations in biomolecular simulations is a good starting point~\cite{Braun2019}. Tutorial 2 on funnel metadynamics expects some background understanding of funnel metadynamics; a good starting point for studying this topic can be found in ref~\cite{Limongelli2013}. Tutorial 3, expects knowledge on steered MD and collective variables (CVs). If you are unfamiliar with these topics Isralewitz et al. provides a good introduction to steered MD~\cite{Isralewitz2001}, while a chapter in \textit{Biomolecular Simulations} by Bussi and Tribello gives a practical overview on how CVs are used by PLUMED\cite{Bussi2019}. Tutorial 4 covers how to use BSS to run alchemical free energy calculations. The best-practice guide by Mey et al. for alchemical free energy calculations provides a broad introduction and overview of this method~\cite{mey2020best}. \subsection{Software/system requirements and installation} @@ -172,7 +171,7 @@ \subsection{Tutorial 1: Introduction to BioSimSpace} \subsection{Tutorial 2: funnel metadynamics} \input{LIVECOMS/02_funnel_metad/livecoms.tex} -\subsection{Tutorial 3: Steered molecular dynamics \& Markov state modeling} +\subsection{Tutorial 3: Steered molecular dynamics} \input{LIVECOMS/03_steered_md/livecoms.tex} \subsection{Tutorial 4: Alchemical free energy calculations} diff --git a/LIVECOMS/references.bib b/LIVECOMS/references.bib index 26af8c7..7c43983 100644 --- a/LIVECOMS/references.bib +++ b/LIVECOMS/references.bib @@ -121,7 +121,8 @@ @article{Choy2017 journal = {Mol. Cell}, volume = {65}, year = {2017}, - pages = {644--658} + pages = {644--658}, + doi = {10.1016/j.molcel.2017.01.014} } @article{Wells2007, @@ -147,7 +148,8 @@ @article{Isralewitz2001 journal = {Curr. Opin. Struct. Biol.}, year = {2001}, volume = {11}, - pages = {224-230} + pages = {224-230}, + doi= {10.1016/s0959-440x(00)00194-9} } @article{Tribello2014, @@ -156,7 +158,8 @@ @article{Tribello2014 year = {2014}, journal = {Comput. Phys. Commun.}, volume = {185}, - pages = {604--613} + pages = {604--613}, + doi = {10.1016/j.cpc.2013.09.018} } @article{Potterton2019, @@ -255,7 +258,8 @@ @article{sMD_Wiesman journal = {Nature Structural and Molecular Biology}, volume = {11}, year = {2004}, - pages = {730--737} + pages = {730--737}, + doi = {10.1038/nsmb803} } @article{bss_paper, @@ -266,13 +270,13 @@ @article{bss_paper volume = {4}, number = {43}, pages = {1831}, - author = {Lester O. Hedges and Antonia S.j.s. Mey and Charles A. Laughton and Francesco L. Gervasio and Adrian J. Mulholland and Christopher J. Woods and Julien Michel}, + author = {Lester O. Hedges and Antonia S. J. S. Mey and Charles A. Laughton and Francesco L. Gervasio and Adrian J. Mulholland and Christopher J. Woods and Julien Michel}, title = {BioSimSpace: An interoperable Python framework for biomolecular simulation}, journal = {Journal of Open Source Software} } @unpublished{amber_ref, author = {D.A. Case and K. Belfon and I.Y. Ben-Shalom and S.R. Brozell and D.S. Cerutti and T.E. Cheatham III and V.W.D. Cruzeiro and T.A. Darden and R.E. Duke and G. Giambasu and M.K. Gilson and H. Gohlke and A.W. Goetz and R. Harris and S. Izadi and S.A. Izmailov and K. Kasavajhala and A. Kovalenko and R. Krasny and T. Kurtzman and T.S. Lee and S. LeGrand and P. Li and C. Lin and J. Liu and T. Luchko and R. Luo and V. Man and K.M. Merz and Y. Miao and O. Mikhailovskii and G. Monard and H. Nguyen and A. Onufriev and F.Pan and S. Pantano and R. Qi and D.R. Roe and A. Roitberg and C. Sagui and S. Schott-Verdugo and J. Shen and C.L. Simmerling and N.R.Skrynnikov and J. Smith and J. Swails and R.C. Walker and J. Wang and L. Wilson and R.M. Wolf and X. Wu and Y. Xiong and Y. Xue and D.M. York and P.A. Kollman}, - note = {AMBER 2020}, + note = { AMBER 2020}, year = {2020}, institution = {University of California, San Francisco} } @@ -285,6 +289,7 @@ @article{gromacs_ref issue = {2}, pages = {19--25}, year = {2015}, + doi = {10.1016/j.softx.2015.06.001} } @article{McGibbon2015MDTraj, @@ -330,7 +335,7 @@ @software{oliver_beckstein_2023_7809117 } @InProceedings{ oliver_beckstein-proc-scipy-2016, - author = { {R}ichard {J}. {G}owers and {M}ax {L}inke and {J}onathan {B}arnoud and {T}yler {J}. {E}. {R}eddy and {M}anuel {N}. {M}elo and {S}ean {L}. {S}eyler and {J}an {D}omański and {D}avid {L}. {D}otson and {S}ébastien {B}uchoux and {I}an {M}. {K}enney and {O}liver {B}eckstein }, + author = { {R}ichard {J}. {G}owers and {M}ax {L}inke and {J}onathan {B}arnoud and {T}yler {J}. {E}. {R}eddy and {M}anuel {N}. {M}elo and {S}ean {L}. {S}eyler and {J}an {D}omanski and {D}avid {L}. {D}otson and {S}ebastien {B}uchoux and {I}an {M}. {K}enney and {O}liver {B}eckstein }, title = { {M}{D}{A}nalysis: {A} {P}ython {P}ackage for the {R}apid {A}nalysis of {M}olecular {D}ynamics {S}imulations }, booktitle = { {P}roceedings of the 15th {P}ython in {S}cience {C}onference }, pages = { 98 - 105 }, @@ -352,7 +357,8 @@ @article{namd timestamp = {2008-03-01T00:16:22.000+0100}, title = {Scalable molecular dynamics with NAMD}, volume = 26, - year = 2005 + year = 2005, + doi = {10.1002/jcc.20289} } @article{Limongelli2013, @@ -543,4 +549,16 @@ @article{Calabr2016 author = {Gaetano Calabr{\`{o}} and Christopher J. Woods and Francis Powlesland and Antonia S. J. S. Mey and Adrian J. Mulholland and Julien Michel}, title = {Elucidation of Nonadditive Effects in Protein{\textendash}Ligand Binding Energies: Thrombin as a Case Study}, journal = {The Journal of Physical Chemistry B} +} + +@book{Bussi2019, + author = {Giovanni Bussi and Gareth A. Tribello}, + title = {Analyzing and Biasing Simulations with PLUMED}, + editor = {Massimiliano Bonomi and Carlo Camilloni}, + publisher = {Springer Science}, + address = {New York, USA}, + year = {2019}, + booktitle = {Biomolecular Simulations}, + chapter = {21}, + doi={10.1007/978-1-4939-9608-7_21} } \ No newline at end of file diff --git a/releases/LiveCoMS_Article_v1.pdf b/releases/LiveCoMS_Article_v1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..59d0d7d8157b73a2bfdb67551bd9716dadd35b02 GIT binary patch literal 7586108 zcmeFYV{oNixA&W*W4q(5*tV^XZQHhO+jhrB$F^;o9dvlo`+lDH?ERj7s?MkPeA%mN z)m-zM{*PI;e&d>BlE?{*(lF4nK$CPo6?{N5GBW_^0k#Gf(A?YrIw^A-V|@p64`U+$ z!&eT##=_3ZK+g`K69;Gl7+IJY0gTLS0Br!B?AH$#0G$YcorRf&5kMycVEAgF^tS;s z69)r;mlxXD#^`Uifd4a}uZ1xFr-dlF+ZhAs)D(0O%BKZJoXfnf}Qse#r`8p=bM#US0mB z6yyJ<4xNNBK$Dxz#898Xn2C|qkday6gn^yGn32JN#hBHYL!W__$%vkpo5jF@!HA97 zghk(o-H?NY*~CDf-I$4`VT0OwKKL6Fmy7vwE-}*{zaBAP>^+Yvic$~ 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