- Email: [email protected]
CHARMM-GUI Ligand Reader & Modeler
Monday, February 13, 2017
Computational Methods and Bioinformatics I 1416-Pos Board B484 CHARMM-GUI Ligand Reader & Modeler Seonghoon Kim1, Jumin Lee1, Sunhwan Jo2, Wonpil Im1. 1 Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA, 2Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, USA. Reading ligand structures into any simulation program is often very difficult and time consuming, as the corresponding molecular force field parameters or structure files may not be available immediately. To resolve this issue and simplify the ligand model preparation, we have developed Ligand Reader & Modeler in CHARMM-GUI. Users can upload ligand structure information to the server in various ways (using PDB ID, ligand ID, SMILES, MOL/ MOL2/SDF file, and PDB file), and the uploaded structure will be displayed on the sketchpad for verification and further modification. Ligand Reader & Modeler will prepare the ligand force filed parameters and necessary structure files by searching for the ligand in the entire CHARMM force field library or using CHARMM General Force Field (CGenFF). In addition, a user can draw chemical substitution sites and substituents in each attachment site to generate a set of combinatorial structure files and corresponding force field parameters for high-throughput screening simulation. Finally, the output can be used in other CHARMM-GUI modules to build a simulation system for all supported simulation programs such as CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, and OpenMM. 1417-Pos Board B485 Molecular Dynamics Simulation Workflow for Nucleosome Studies Ran Sun1, Zilong Li2, Thomas C. Bishop3. 1 Engineering Physics Program, Louisiana Tech University, Ruston, LA, USA, 2Computational Analysis and Modeling Program, Louisiana Tech University, Ruston, LA, USA, 3Departments of Chemistry and Physics, Louisiana Tech University, Ruston, LA, USA. Nucleosomes are the building blocks of eukaryote genomes. Given that a nucleosome contains 147 base pairs of DNA, the DNA sequence complexity is ~ 4147. With the advent of genome wide association studies attention can be focused on one nucleosome. Today’s supercomputers and simulation tools are sufficiently powerful to conduct comparative studies of ensembles representing 10’s to 100’s of mononucleosomes in a few days. Here we demonstrate a workflow that characterizes 21 different positions of 601: 10 upstream, 10 downstream and the ideal position in atomic detail. Atomic models were constructed by automatically docking the 601-177 fragment to the histone core of 1KX5. In all 21 simulations, the superhelix geometry evolves in less than 100 ns to a conformation that is significantly different from the original 1KX5 geometry. We previously demonstrated that all x-ray structures of the nucleosome exhibit a specific distribution of Roll, Slide, and Twist. Twelve Fourier components are necessary and sufficient to describe the DNA base pair step parameters required to recreate a nucleosome superhelix in atomic resolution. Here we employ our Fourier filtering technique to show that 100 ns is sufficient to observe global conformational changes associated with positioning and mispositioning the 601-177 fragment. Specifically, ideal positioning yields a DNA superhelix conformation with a significantly different distribution of Slide than mispositioned nucleosomes. The phase and amplitude of Slide associated with wave number 14, approximately the DNA helix repeat 146bp/14, is strongly influenced by nucleosome mispositioning. Thus we have validated a workflow that allows us to efficiently simulate and analyze nucleosomes and demonstrated that simulations as short as 100 ns are sufficient to investigate sequence specific properties of the nucleosome. All simulation and analysis data is published via our iBIOMES server at http://www.latech.edu/~bishop. 1418-Pos Board B486 Evaluating Molecular Mechanics Force Fields with a Quantum Chemical Approach David Koes, John Vries. Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA. Accurate chemical shifts for the atoms in molecular mechanics (MD) trajectories can be obtained from de novo quantum mechanical (QM) calculations that depend solely on the coordinates of the atoms in the localized regions surrounding atoms of interest. If these coordinates are correct and the sample size is adequate, the ensemble average of these chemical shifts should be equal to the chemical shifts obtained from NMR spectroscopy. If this is not the case, the coordinates must be incorrect. We have utilized this fact to quantify the errors associated with the backbone atoms in MD simulations of proteins. A library of regional conformers was constructed from 6 model proteins. The
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chemical shifts associated with the backbone atoms in each of these conformers was obtained from QM calculations using density functional theory at the B3LYP level with a 6-311þG(2d,p) basis set. Chemical shifts were assigned to each backbone atom in each MD simulation frame using a template matching approach. The ensemble average of these chemical shifts was compared to chemical shifts from NMR spectroscopy. We investigate the chemical shift error induced by a variety of forcefield and water models. All methods exhibit systematic errors, especially for hydrogen atoms involved in hydrogen bonding, but some methods generate lower errors and are more amenable to systematic correction. 1419-Pos Board B487 One Constraint at a Time: Using Viability Principles in Integrative Modeling of Macromolecular Assemblies Giorgio E. Tamo1, Andrea Maesani1, Sylvain Traeger1, Matteo Thomas Degiacomi2, Dario Floreano1, Matteo Dal Peraro1. 1 EPFL, Lausanne, Switzerland, 2Oxford University, Oxford, United Kingdom. Macromolecular assemblies are involved in several cellular processes of living organisms. The atomic characterization of their structure is of fundamental importance for the understanding of their function. Integrative modeling approaches provides candidate structural predictions starting from experimental information and force-field-derived energy potentials. The prediction process is often modeled an optimization problem, where a scoring function has to be minimized. In several cases, however, these approaches combine in the same scoring function different objectives and constraints that have to be satisfied. Unfortunately, tuning the weights of the various components is a tedious and error-prone process, usually empirically performed. Here, we propose a novel protein assembly prediction protocol that eliminates the need for aggregating composite scoring functions. Our approach is computationally efficient and competitive with previously proposed protocols. 1420-Pos Board B488 A Balanced Approach to Adaptive Density Estimation in Biophysical Analytics Julio A. Kovacs, Willy Wriggers. Dept. of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA, USA. Our recent development of a Fast (Mutual) Information Matching (FIM) of molecular dynamics time series data led to the general problem of how to efficiently estimate the probability density function of very unevenly sampled random variables. Here, we propose a novel Balanced Adaptive Density Estimation (BADE) that effectively optimizes the amount of smoothing at each point. However, unlike previous methods, BADE does not involve kernel functions and instead relies on an efficient nearest-neighbor search which results in good scaling for large data sizes. Our tests on simulated data show that BADE exhibits equal or better accuracy than existing methods, and visual tests on univariate and bivariate experimental data show that the results are also aesthetically pleasing. Our results suggest that BADE offers an attractive new take on the fundamental density estimation problem in statistics. We expect it to be useful for low-dimensional applications in various statistical application domains such as molecular dynamics, signal processing and computational neuroscience. 1421-Pos Board B489 Mechanism Beyond Markov Models: History Information is Needed for Unbiased Pathway Reconstruction of Protein Folding Ernesto Sua´rez1, Joshua L. Adelman2, Daniel M. Zuckerman3. 1 Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA, 2Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA, 3OHSU Center for Spatial Systems Biomedicine and Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA. Understanding the mechanisms (pathways through configuration space) of biomolecular processes such as protein folding is a major challenge for molecular dynamics (MD) simulations. Markov state models (MSMs), which discretize simulation data by projecting onto a relatively small number of states, have become increasingly popular compact representations of the overwhelming complexity of protein configuration space and the vast quantity of trajectory data. Building on previous work showing the potential for significant bias in MSM estimates of folding and unfolding rates [Sua´rez et al., JCTC 2016, 12 (8), 3473], here we study MSM descriptions of (un)folding mechanisms. By comparing to independent long-time MD simulations, as well as non-Markov (NM) analyses which include history information, we find that MSMs tend to infer overly broad distributions of more equally probable pathways. By contrast, NM analyses using MSM states agree well with the narrower
Computational Methods and Bioinformatics I 1416-Pos Board B484 CHARMM-GUI Ligand Reader & Modeler Seonghoon Kim1, Jumin Lee1, Sunhwan Jo2, Wonpil Im1. 1 Department of Biological Sciences and Bioengineering Program, Lehigh University, Bethlehem, PA, USA, 2Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, USA. Reading ligand structures into any simulation program is often very difficult and time consuming, as the corresponding molecular force field parameters or structure files may not be available immediately. To resolve this issue and simplify the ligand model preparation, we have developed Ligand Reader & Modeler in CHARMM-GUI. Users can upload ligand structure information to the server in various ways (using PDB ID, ligand ID, SMILES, MOL/ MOL2/SDF file, and PDB file), and the uploaded structure will be displayed on the sketchpad for verification and further modification. Ligand Reader & Modeler will prepare the ligand force filed parameters and necessary structure files by searching for the ligand in the entire CHARMM force field library or using CHARMM General Force Field (CGenFF). In addition, a user can draw chemical substitution sites and substituents in each attachment site to generate a set of combinatorial structure files and corresponding force field parameters for high-throughput screening simulation. Finally, the output can be used in other CHARMM-GUI modules to build a simulation system for all supported simulation programs such as CHARMM, NAMD, GROMACS, AMBER, GENESIS, LAMMPS, Desmond, and OpenMM. 1417-Pos Board B485 Molecular Dynamics Simulation Workflow for Nucleosome Studies Ran Sun1, Zilong Li2, Thomas C. Bishop3. 1 Engineering Physics Program, Louisiana Tech University, Ruston, LA, USA, 2Computational Analysis and Modeling Program, Louisiana Tech University, Ruston, LA, USA, 3Departments of Chemistry and Physics, Louisiana Tech University, Ruston, LA, USA. Nucleosomes are the building blocks of eukaryote genomes. Given that a nucleosome contains 147 base pairs of DNA, the DNA sequence complexity is ~ 4147. With the advent of genome wide association studies attention can be focused on one nucleosome. Today’s supercomputers and simulation tools are sufficiently powerful to conduct comparative studies of ensembles representing 10’s to 100’s of mononucleosomes in a few days. Here we demonstrate a workflow that characterizes 21 different positions of 601: 10 upstream, 10 downstream and the ideal position in atomic detail. Atomic models were constructed by automatically docking the 601-177 fragment to the histone core of 1KX5. In all 21 simulations, the superhelix geometry evolves in less than 100 ns to a conformation that is significantly different from the original 1KX5 geometry. We previously demonstrated that all x-ray structures of the nucleosome exhibit a specific distribution of Roll, Slide, and Twist. Twelve Fourier components are necessary and sufficient to describe the DNA base pair step parameters required to recreate a nucleosome superhelix in atomic resolution. Here we employ our Fourier filtering technique to show that 100 ns is sufficient to observe global conformational changes associated with positioning and mispositioning the 601-177 fragment. Specifically, ideal positioning yields a DNA superhelix conformation with a significantly different distribution of Slide than mispositioned nucleosomes. The phase and amplitude of Slide associated with wave number 14, approximately the DNA helix repeat 146bp/14, is strongly influenced by nucleosome mispositioning. Thus we have validated a workflow that allows us to efficiently simulate and analyze nucleosomes and demonstrated that simulations as short as 100 ns are sufficient to investigate sequence specific properties of the nucleosome. All simulation and analysis data is published via our iBIOMES server at http://www.latech.edu/~bishop. 1418-Pos Board B486 Evaluating Molecular Mechanics Force Fields with a Quantum Chemical Approach David Koes, John Vries. Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA. Accurate chemical shifts for the atoms in molecular mechanics (MD) trajectories can be obtained from de novo quantum mechanical (QM) calculations that depend solely on the coordinates of the atoms in the localized regions surrounding atoms of interest. If these coordinates are correct and the sample size is adequate, the ensemble average of these chemical shifts should be equal to the chemical shifts obtained from NMR spectroscopy. If this is not the case, the coordinates must be incorrect. We have utilized this fact to quantify the errors associated with the backbone atoms in MD simulations of proteins. A library of regional conformers was constructed from 6 model proteins. The
289a
chemical shifts associated with the backbone atoms in each of these conformers was obtained from QM calculations using density functional theory at the B3LYP level with a 6-311þG(2d,p) basis set. Chemical shifts were assigned to each backbone atom in each MD simulation frame using a template matching approach. The ensemble average of these chemical shifts was compared to chemical shifts from NMR spectroscopy. We investigate the chemical shift error induced by a variety of forcefield and water models. All methods exhibit systematic errors, especially for hydrogen atoms involved in hydrogen bonding, but some methods generate lower errors and are more amenable to systematic correction. 1419-Pos Board B487 One Constraint at a Time: Using Viability Principles in Integrative Modeling of Macromolecular Assemblies Giorgio E. Tamo1, Andrea Maesani1, Sylvain Traeger1, Matteo Thomas Degiacomi2, Dario Floreano1, Matteo Dal Peraro1. 1 EPFL, Lausanne, Switzerland, 2Oxford University, Oxford, United Kingdom. Macromolecular assemblies are involved in several cellular processes of living organisms. The atomic characterization of their structure is of fundamental importance for the understanding of their function. Integrative modeling approaches provides candidate structural predictions starting from experimental information and force-field-derived energy potentials. The prediction process is often modeled an optimization problem, where a scoring function has to be minimized. In several cases, however, these approaches combine in the same scoring function different objectives and constraints that have to be satisfied. Unfortunately, tuning the weights of the various components is a tedious and error-prone process, usually empirically performed. Here, we propose a novel protein assembly prediction protocol that eliminates the need for aggregating composite scoring functions. Our approach is computationally efficient and competitive with previously proposed protocols. 1420-Pos Board B488 A Balanced Approach to Adaptive Density Estimation in Biophysical Analytics Julio A. Kovacs, Willy Wriggers. Dept. of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA, USA. Our recent development of a Fast (Mutual) Information Matching (FIM) of molecular dynamics time series data led to the general problem of how to efficiently estimate the probability density function of very unevenly sampled random variables. Here, we propose a novel Balanced Adaptive Density Estimation (BADE) that effectively optimizes the amount of smoothing at each point. However, unlike previous methods, BADE does not involve kernel functions and instead relies on an efficient nearest-neighbor search which results in good scaling for large data sizes. Our tests on simulated data show that BADE exhibits equal or better accuracy than existing methods, and visual tests on univariate and bivariate experimental data show that the results are also aesthetically pleasing. Our results suggest that BADE offers an attractive new take on the fundamental density estimation problem in statistics. We expect it to be useful for low-dimensional applications in various statistical application domains such as molecular dynamics, signal processing and computational neuroscience. 1421-Pos Board B489 Mechanism Beyond Markov Models: History Information is Needed for Unbiased Pathway Reconstruction of Protein Folding Ernesto Sua´rez1, Joshua L. Adelman2, Daniel M. Zuckerman3. 1 Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA, 2Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA, 3OHSU Center for Spatial Systems Biomedicine and Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA. Understanding the mechanisms (pathways through configuration space) of biomolecular processes such as protein folding is a major challenge for molecular dynamics (MD) simulations. Markov state models (MSMs), which discretize simulation data by projecting onto a relatively small number of states, have become increasingly popular compact representations of the overwhelming complexity of protein configuration space and the vast quantity of trajectory data. Building on previous work showing the potential for significant bias in MSM estimates of folding and unfolding rates [Sua´rez et al., JCTC 2016, 12 (8), 3473], here we study MSM descriptions of (un)folding mechanisms. By comparing to independent long-time MD simulations, as well as non-Markov (NM) analyses which include history information, we find that MSMs tend to infer overly broad distributions of more equally probable pathways. By contrast, NM analyses using MSM states agree well with the narrower