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Calculation of Effective Interaction Potentials From
Methodologies of systematic structure-based coarse-graining Alexander Lyubartsev Division of Physical Chemistry Department of Material and Environmental Chemistry Stockholm University E-CAM workshop: State of the art in mesoscale and multiscale modeling Dublin 29-31 May 2017 Outline 1. Multiscale and coarse-grained simulations 2. Systematic structure-based coarse-graining by Inverse Monte Carlo 3. Examples - water and ions - lipid bilayers and lipid assemblies 4. Software - MagiC Example of systematic coarse-graining: DNA in Chromatin: presentation 30 May Computer modeling: from 1st principles to mesoscale Levels of molecular modeling: First-principles Atomistic Meso-scale (Quantum Classical Langevine/ BD, Mechanics) Molecular Dynamics DPD... nuclei electrons atoms coarse-grained biomolecules atoms molecules molecules soft matter 0.1 nm 1.0 nm 10 nm 100 nm 1 000 nm Larger scale more approximations Mesoscale Simulations Length scale: > 10 nm ( nanoscale: 10 - 1000 nm) Atomistic modeling is generally not possible box 10 nm - more than 105 atoms even if doable for 105 - 106 particles - do not forget about time scale! larger size : longer time for equilibration and reliable sampling 105 atoms - time scale should be above 1 ms = 1000 ns Need approximations - coarse-graining Coarse-graining – an example Original size – 2.4Mb Compressed to 24 Kb Levels of coarse- graining Level of coarse-graining can be different. For example, for a DMPC lipid: All-atom model United-atom Coarse-grained Coarse-grained 118 atoms model: 10 sites: 3 sites: 46 united atoms Martini model Cooke model more details - chemical specificity faster computations; larger systems Coarse-graining of solvent: 1) Explicit solvent: 2) Implicit solvent: One or several solvent molecules are No solvent particles but their effect is united in a single site. -
Calculation of Effective Interaction Potentials From
Systematic Coarse-Graining of Molecular Models by the Inverse Monte Carlo: Theory, Practice and Software Alexander Lyubartsev Division of Physical Chemistry Department of Material and Environmental Chemistry Stockholm University Modeling Soft Matter: Linking Multiple Length and Time Scales Kavli Institute of Theoretical Physics, UCSB, Santa Barbara 4-8 June 2012 Soft Matter Simulations length model method time 1Å electron w.f + ab-initio 100 ps 1 nm nuclei BOMD, CPMD 10 nm atomistic classical MD 100 ns coarse-grained Langevine MD, 100 nm µ more DPD, etc 100 s coarse-grained µ 1 m continuous The problem: Larger scale ⇔ more approximations Coarse-graining – an example Original size - 900K Compressed to 24K Coares-graining: reduction degrees of freedom All-atom model Coarse-grained model Large-scale 118 atoms 10 sites simulations We need to: 1) Design Coarse-Grained mapping: specify the important degrees of freedom 2) For “important” degrees of freedom we need interaction potential Question: what is the interaction potential for the coarse-grained model? Formal solution: N-body mean force potential Original (FG = fine grained system) FG ( ) =θ( ) H r 1, r 2,... ,rn R j r1, ...r n n = 118 j = 1,...,10 Usually, centers of mass of selected molecular fragments Partition function : n =∫∏ (−β ( ))= Z dr i exp H FG r 1, ...,r n i=1 n N =∫∏ ∏ δ( −θ ( )) (−β ( ))= dr i dR j R j j r1, ...rn exp H FG r1, ... ,rn = i1 j 1 N =∫∏ (−β ( )) dR j exp H CG R1, ... , RN j =1 1 where β= k B T n N ( )=−1 ∫∏ ∏ δ( −θ ( )) (− ( )) H CG R1, ..., R N β ln dr i R j -
Mai Muuttunut Pilit Muut Aidi Mini
MAIMUUTTUNUT US009963689B2 PILIT MUUT AIDI MINI (12 ) United States Patent ( 10 ) Patent No. : US 9 ,963 , 689 B2 Doudna et al. ( 45) Date of Patent: May 8 , 2018 ( 54 ) CASI CRYSTALS AND METHODS OF USE FOREIGN PATENT DOCUMENTS THEREOF WO WO 2013 / 126794 AL 8 / 2013 ( 71 ) Applicant: The Regents of the University of wo WO 2013 / 142578 A19 / 2013 California , Oakland , CA (US ) WO WO 2013 / 176772 A1 11/ 2013 ( 72 ) Inventors : Jennifer A . Doudna , Oakland , CA OTHER PUBLICATIONS (US ) ; Samuel H . Sternberg , Oakland , McPherson , A . Current Approaches to Macromolecular Crystalli CA (US ) ; Martin Jinek , Oakland , CA zation . European Journal of Biochemistry . 1990 . vol . 189 , pp . (US ) ; Fuguo Jiang , Oakland , CA (US ); 1 - 23 . * Emine Kaya , Oakland , CA (US ) ; Kundrot, C . E . Which Strategy for a Protein Crystallization Project ? Cellular Molecular Life Science . 2004 . vol . 61, pp . 525 - 536 . * David W . Taylor, Jr. , Oakland , CA Benevenuti et al. , Crystallization of Soluble Proteins in Vapor (US ) Diffusion for X - ray Crystallography , Nature Protocols , published on - line Jun . 28 , 2007 , 2 ( 7 ) : 1633 - 1651. * ( 73 ) Assignee : THE REGENTS OF THE Cudney R . Protein Crystallization and Dumb Luck . The Rigaku UNIVERSITY OF CALIFORNIA , Journal. 1999 . vol. 16 , No . 1 , pp . 1 - 7 . * Drenth , “ Principles of Protein X - Ray Crystallography ” , 2nd Edi Oakland , CA (US ) tion , 1999 Springer - Verlag New York Inc ., Chapter 1 , p . 1 - 21. * Moon et al . , “ A synergistic approach to protein crystallization : ( * ) Notice : Subject to any disclaimer , the term of this Combination of a fixed -arm carrier with surface entropy reduction ” , patent is extended or adjusted under 35 Protein Science , 2010 , 19 : 901 -913 . -
M.Dynamix Studies of Solvation, Solubility and Permeability
5 M.DynaMix Studies of Solvation, Solubility and Permeability Aatto Laaksonen1, Alexander Lyubartsev1 and Francesca Mocci1,2 1Stockholm University, Division of Physical Chemistry, Department of Materials and Environmental Chemistry, Arrhenius Laboratory, Stockholm 2Università di Cagliari, Dipartimento di Scienze Chimiche Cittadella Universitaria di Monserrato, Monserrato, Cagliari 1Sweden 2Italy 1. Introduction During the last four decades Molecular Dynamics (MD) simulations have developed to a powerful discipline and finally very close to the early vision from early 80’s that it would mature and become a computer laboratory to study molecular systems in conditions similar to that valid in experimental studies using instruments giving information about molecular structure, interactions and dynamics in condensed phases and at interfaces between different phases. Today MD simulations are more or less routinely used by many scientists originally educated and trained towards experimental work which later have found simulations (along with Quantum Chemistry and other Computational methods) as a powerful complement to their experimental studies to obtain molecular insight and thereby interpretation of their results. In this chapter we wish to introduce a powerful methodology to obtain detailed and accurate information about solvation and solubility of different categories of solute molecules and ions in water (and other solvents and phases including mixed solvents) and also about permeability and transport of solutes in different non-aqueous phases. Among the most challenging problems today are still the computations of the free energy and many to it related problems. The methodology used in our studies for free energy calculations is our Expanded Ensemble scheme recently implemented in a general MD simulation package called “M.DynaMix”. -
Modification of the CHARMM Force Field for DMPC Lipid Bilayer
Modification of the CHARMM Force Field for DMPC Lipid Bilayer CARL-JOHAN HÖGBERG,1 ALEXEI M. NIKITIN,1,2 ALEXANDER P. LYUBARTSEV1 1Division of Physical Chemistry, Arrhenius Laboratory, Stockholm University, Stockholm, SE-10691, Sweden 2Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russia Received 23 August 2007; Revised 8 February 2008; Accepted 10 February 2008 DOI 10.1002/jcc.20974 Published online 29 May 2008 in Wiley InterScience (www.interscience.wiley.com). Abstract: The CHARMM force field for DMPC lipids was modified in order to improve agreement with experiment for a number of important properties of hydrated lipid bilayer. The modification consists in introduction of a scaling factor 0.83 for 1–4 electrostatic interactions (between atoms separated by three covalent bonds), which provides correct transgauche ratio in the alkane tails, and recalculation of the headgroup charges on the basis of HF/6-311(d,p) ab-initio computations. Both rigid TIP3P and flexible SPC water models were used with the new lipid model, showing similar results. The new model in a 75 ns simulation has shown a correct value of the area per lipid at zero surface tension, as well as good agreement with the experiment for the electron density, structure factor, and order parameters, including those in the headgroup part of lipids. © 2008 Wiley Periodicals, Inc. J Comput Chem 29: 2359–2369, 2008 Key words: molecular dynamics; lipid bilayers; force field; DMPC; hydration Introduction to be necessary in order to address such -
“One Ring to Rule Them All”
OpenMM library “One ring to rule them all” https://simtk.org/home/openmm CHARMM * Abalone Presto * ACEMD NAMD * ADUN * Ascalaph Gromacs AMBER * COSMOS ??? HOOMD * Desmond * Culgi * ESPResSo Tinker * GROMOS * GULP * Hippo LAMMPS * Kalypso MD * LPMD * MacroModel * MDynaMix * MOLDY * Materials Studio * MOSCITO * ProtoMol The OpenM(olecular)M(echanics) API * RedMD * YASARA * ORAC https://simtk.org/home/openmm * XMD Goals of OpenMM l Complete library l provide what is most needed l easy to learn and use l Fast, general, extensible l optimize for speed l hide hardware specifics l support new hardware l add new force fields, integration methods etc. l Available APIs in C++, Fortran95, Python l Advantages l Object oriented l Modular l Extensible l Disadvantages l Programs written in other languages need to invoke it through a layer of C++ code Features • Electrostatics: cut-off, Ewald, PME • Implicit solvent • Integrators: Verlet, Langevin, Brownian, custom • Thermostat: Andersen • Barostat: MonteCarlo • Others: energy minimization, virtual sites 5 The OpenMM Architecture Public Interface OpenMM Public API Platform Independent Code OpenMM Implementation Layer Platform Abstraction Layer OpenMM Low Level API Computational Kernels CUDA/OpenCL/Brook/MPI etc. Public API Classes (1) l System l A collection of interaction particles l Defines the mass of each particle (needed for integration) l Specifies distance restraints l Contains a list of Force objects that define the interactions l OpenMMContext l Contains all state information for a particular simulation − Positions, velocities, other parmeters Public API Classes (2) l Force l Anything that affects the system's behavior: forces, barostats, thermostats, etc. l A Force may: − Apply forces to particles − Contribute to the potential energy − Define adjustable parameters − Modify positions, velocities, and parameters at the start of each time step l Existing Force subclasses − HarmonicBond, HarmonicAngle, PeriodicTorsion, RBTorsion, Nonbonded, GBSAOBC, AndersenThermostat, CMMotionRemover.. -
Advances in Computational Fluid Mechanics in Cellular Flow Manipulation: a Review
applied sciences Review Advances in Computational Fluid Mechanics in Cellular Flow Manipulation: A Review Masoud Arabghahestani 1,* , Sadegh Poozesh 2 and Nelson K. Akafuah 1 1 Institute of Research for Technology Development (IR4TD), University of Kentucky, Lexington, KY 40506, USA; [email protected] 2 Mechanical Engineering Department, Tuskegee University, Tuskegee, AL 36088, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-(859)-898-7700 Received: 28 August 2019; Accepted: 25 September 2019; Published: 27 September 2019 Abstract: Recently, remarkable developments have taken place, leading to significant improvements in microfluidic methods to capture subtle biological effects down to single cells. As microfluidic devices are getting sophisticated, design optimization through experimentations is becoming more challenging. As a result, numerical simulations have contributed to this trend by offering a better understanding of cellular microenvironments hydrodynamics and optimizing the functionality of the current/emerging designs. The need for new marketable designs with advantageous hydrodynamics invokes easier access to efficient as well as time-conservative numerical simulations to provide screening over cellular microenvironments, and to emulate physiological conditions with high accuracy. Therefore, an excerpt overview on how each numerical methodology and associated handling software works, and how they differ in handling underlying hydrodynamic of lab-on-chip microfluidic is crucial. These numerical means rely on molecular and continuum levels of numerical simulations. The current review aims to serve as a guideline for researchers in this area by presenting a comprehensive characterization of various relevant simulation techniques. Keywords: computational fluid mechanics; microfluidic devices; cellular flow; numerical simulations; molecular and continuum levels 1. Introduction 1.1. -
Proteome-Scale Prediction of Structure and Func- Tion
Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press The proteome folding project: proteome-scale prediction of structure and func- tion Kevin Drew 1, Patrick Winters 1, Glenn L. Butterfoss 1, Viktors Berstis 2, Keith Uplinger 2, Jonathan Armstrong 2, Michael Riffle 3, Erik Schweighofer 4, Bill Bovermann 2, David R. Good- lett 5, Trisha N. Davis 3, Dennis Shasha 6, Lars Malmström 7, Richard Bonneau 1,4,6 * 1 Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA 2 IBM, Austin, TX, 78758, USA 3 Department of Biochemistry, Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA 4 Institute for Systems Biology, Seattle, WA 98103, USA 5 Medicinal Chemistry Department, University of Washington, Seattle, WA 98195, USA 6 Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, New York, 10003, USA 7 Institute of Molecular Systems Biology, ETH Zurich, Zurich CH 8093, Switzerland * To whom correspondence should be addressed. Richard Bonneau, PhD. New York University Center for Genomics and Systems Biology 100 Washington Sq. E. 1009 Main Building New York, NY 10003 Voice: 646 460 3026 Email: [email protected] Running title: proteome folding Keywords: proteome, de novo folding, function prediction, Rosetta, grid computing 1 Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press 2 Downloaded from genome.cshlp.org on October 5, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract: The incompleteness of proteome structure and function annotation is a critical problem for biol- ogists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. -
Open Source Molecular Modeling
Accepted Manuscript Title: Open Source Molecular Modeling Author: Somayeh Pirhadi Jocelyn Sunseri David Ryan Koes PII: S1093-3263(16)30118-8 DOI: http://dx.doi.org/doi:10.1016/j.jmgm.2016.07.008 Reference: JMG 6730 To appear in: Journal of Molecular Graphics and Modelling Received date: 4-5-2016 Accepted date: 25-7-2016 Please cite this article as: Somayeh Pirhadi, Jocelyn Sunseri, David Ryan Koes, Open Source Molecular Modeling, <![CDATA[Journal of Molecular Graphics and Modelling]]> (2016), http://dx.doi.org/10.1016/j.jmgm.2016.07.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Open Source Molecular Modeling Somayeh Pirhadia, Jocelyn Sunseria, David Ryan Koesa,∗ aDepartment of Computational and Systems Biology, University of Pittsburgh Abstract The success of molecular modeling and computational chemistry efforts are, by definition, de- pendent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. 1. Introduction What is Open Source? Free and open source software (FOSS) is software that is both considered \free software," as defined by the Free Software Foundation (http://fsf.org) and \open source," as defined by the Open Source Initiative (http://opensource.org). -
Alexander Lyubartsev(3),* * Department of Materials And
Molecular Dynamics Simulations of Polybrominated Diphenyl Ethers in Biological Environments Inna Ermilova(1),* , Samuel Stenberg(2),* , Alexander Lyubartsev(3),* * Department of Materials and Environmental Chemistry, Stockholm University, Sweden (1) [email protected] ; (2) [email protected] ; (3) [email protected] Polybrominated diphenyl ethers (PBDEs) and hydroxylated polybrominated diphenyl ethers (OH-PBDEs) belong to a class of chemical substances which cause environmental and health concerns because of their toxicity. In this work we have been using molecular dynamics simulations with metadynamics (MetaD) and expanded ensemble (EE) approaches. EE calculations have been carried out in for 19 molecules in order to relate toxic mechanisms to partition coefficients between water and octanol systems while metadynamics simulations were supposed to give us answers on how binding free energies are related to toxic mechanisms for 4 chosen molecules. What is a toxicity and how is it studied EE simulations set-ups and MetaD simulations set-ups experimentally? Toxicity is a relative degree of being poisonous of a certain chemical and models compound. A toxicity doesn't have to be referred to deadly effects, it is EE simulations: 20 simulations in octanol (SLipids force field model)+ 20 simulations enough to damage a living organism. Experimentally such effects aren't in water (TIP3p model). Simulations were done at T=298K and p=1 bar for 50 ns. The studied on humans but some animals or other organisms are used in order number of subensembles was 26 for solutes in water and 41 for solutes in octanol. to predict how a specie can affect people. -
M.Dynamix Studies of Solvation, Solubility and Permeability
5 M.DynaMix Studies of Solvation, Solubility and Permeability Aatto Laaksonen1, Alexander Lyubartsev1 and Francesca Mocci1,2 1Stockholm University, Division of Physical Chemistry, Department of Materials and Environmental Chemistry, Arrhenius Laboratory, Stockholm 2Università di Cagliari, Dipartimento di Scienze Chimiche Cittadella Universitaria di Monserrato, Monserrato, Cagliari 1Sweden 2Italy 1. Introduction During the last four decades Molecular Dynamics (MD) simulations have developed to a powerful discipline and finally very close to the early vision from early 80’s that it would mature and become a computer laboratory to study molecular systems in conditions similar to that valid in experimental studies using instruments giving information about molecular structure, interactions and dynamics in condensed phases and at interfaces between different phases. Today MD simulations are more or less routinely used by many scientists originally educated and trained towards experimental work which later have found simulations (along with Quantum Chemistry and other Computational methods) as a powerful complement to their experimental studies to obtain molecular insight and thereby interpretation of their results. In this chapter we wish to introduce a powerful methodology to obtain detailed and accurate information about solvation and solubility of different categories of solute molecules and ions in water (and other solvents and phases including mixed solvents) and also about permeability and transport of solutes in different non-aqueous phases. Among the most challenging problems today are still the computations of the free energy and many to it related problems. The methodology used in our studies for free energy calculations is our Expanded Ensemble scheme recently implemented in a general MD simulation package called “M.DynaMix”. -
The History of the CATH Structural Classification of Protein Domains
Accepted Manuscript The History of the CATH Structural Classification of Protein Domains Ian Sillitoe, Natalie Dawson, Janet Thornton, Christine Orengo PII: S0300-9084(15)00251-5 DOI: 10.1016/j.biochi.2015.08.004 Reference: BIOCHI 4793 To appear in: Biochimie Received Date: 1 July 2015 Accepted Date: 1 August 2015 Please cite this article as: I. Sillitoe, N. Dawson, J. Thornton, C. Orengo, The History of the CATH Structural Classification of Protein Domains, Biochimie (2015), doi: 10.1016/j.biochi.2015.08.004. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. ACCEPTED MANUSCRIPT The History of the CATH Structural Classification of Protein Domains Ian Sillitoe*, Natalie Dawson, Janet Thornton and Christine Orengo University College London Darwin Building, Gower Street, University College London, WC1E 6BT, UK * Corresponding author: Email: [email protected] Phone: +442076792171 MANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Abstract This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains.