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A Multiscale Approach To Understanding Protein Ligand Binding Process
642a
Wednesday, March 2, 2016
Hamiltonian exchange for free energy landscape calculation of SH3 domainp41 peptide binding complex were carried out on IBM Blue Gene/Q computer to demonstrate efficacy of REST2 based on the present implementation. For the (AAQAA)3 system, alpha-helix contents in agreement with experiment are observed within 200ns simulation; for the T4 Lysozyme-pxylene binding complex, accurate sampling of rotameric states orthogonal to the thermodynamic axis results in binding free energy in excellent agreement with experiment; for SH3 domain-p41 peptide binding complex, REST2 significantly accelerates the convergence of PMF by enhancing structural reorganization along the reaction path.
namics (PaCS-MD) [J. Chem. Phys. 139, 035103 (2013)] to generate multiple dissociation pathways without applying force biases and specific targeting scheme. We perform dissociation simulation for Hen-egg Lysozyme and a ligand Tri-N-Acetylchitotiose as a test case. Applying Umbrella Sampling (US) for the selected PaCS-MD snapshots, the obtained free energy profile is in good agreement with experimental binding free energy. In addition, from the trajectories of PaCS-MD, we build the Markov State Model and calculate the free energy profile. Without any further simulations as in US, the difference of free energy of the bound state and un-bound state is also well-reproduced.
3166-Pos Board B543 Improved Estimation of Long-Time Kinetics using Non-Markovian Analysis of Trajectory Segments: Application to Protein Folding and Unfolding Ernesto Suarez, Daniel M. Zuckerman. Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA. What is the best way to estimate long-time kinetics from finite trajectory data for molecular simulations? A range of sophisticated Markov-model schemes have been developed to analyze behavior arising from discretization of a continuous configuration space into states. However, discretization into finite states leads intrinsically to non-Markovian behavior, and hence to bias in Markov-based estimates of quantities such as the mean first-passage time (MFPT). We have shown previously that it is sufficient to include just part of the history information [JCTC 2014, 10, pp 2658-2667] to correct bias in MFPT estimation. Here, we attempt to develop practical nonMarkovian analyses applicable to protein simulations. The schemes are tested using msec msec-scale simulation data [Science 2011, 334(6055), pp 517-520], which provide reference MFPT values. We progressively reduce the amount of data used by the non-Markovian estimators by cutting the trajectories in small fragments and selecting just a fraction of them. In every case we are able to obtain reasonably good results for both folding and unfolding MFPTs even when the total length of trajectory data used is well below the longer of the MFPTs. Our estimators are relatively insensitive to the construction of states, reducing the bias over a wide range of lag times, yielding reasonable MFPT values even with very crude discretizations.
3169-Pos Board B546 A Multiscale Approach To Understanding Protein Ligand Binding Process Tohru Terada1,2, Tatsuki Negami1, Kentaro Shimizu1,2. 1 Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan, 2Department of Biotechnology, The University of Tokyo, Tokyo, Japan. Owing to the recent rapid increase in computational power and the improvement of the algorithms, it is now possible to reproduce the whole process of protein–ligand binding in a molecular dynamics (MD) simulation. However, because the ligand-binding process is a stochastic process, it is necessary to repeat the MD simulation many times to fully understand its statistical nature. Therefore, it is still difficult to apply the MD simulation to various protein– ligand systems and to elucidate the general properties of the ligand-binding process. To solve this problem, we are developing a multi-scale approach that combines the coarse-grained (CG) and the all-atom (AA) MD methods. Recently, we have shown that the ligand-binding process can be reproduced in the CGMD simulations with the MARTINI force field [Negami et al. J Comput. Chem. 35, 1835–1845 (2014)]. In this study, the CGMD simulations were applied to two different protein–ligand systems. For each system, 1–4 ms simulations were performed 50–100 times with different initial ligand placement and different initial velocities. The binding and unbinding rate constants and the dissociation constants calculated from the CGMD trajectories were consistent with the experimental values. Furthermore, the ligands tended to enter the ligand-binding pockets through specific pathways. In the present study, we optimized the pathway of a protein–ligand system toward the minimum freeenergy pathway using the string method. The pathway was discretized with 32 images represented by a set of interatomic distances between the protein and the ligand. At each position of the image, free-energy gradient was calculated using the AAMD simulation and the pathway was optimized accordingly. The simulation was performed for 3032 ns in total. We will discuss the ligand-binding process based on the free-energy profile calculated along the optimized pathway.
3167-Pos Board B544 HDGB Implicit Membrane Model with a van Der Waals Dispersion Term Bercem Dutagaci1, Maryam Sayadi1, Michael Feig1,2. 1 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA, 2Department of Chemistry, Michigan State University, East Lansing, MI, USA. The Heterogeneous Dielectric Generalized Born (HDGB) model(1) allows simulations of membrane-bound systems with an implicit membrane formalism using dielectric profiles matched against explicit membrane simulations. In this study, an extension of the original model is described where the solventaccessible surface area term is extended with a van der Waals dispersion term to improve the estimation of the non-polar components of the free energy of solvation. This is especially important in the hydrophobic region of bilayer systems where charged and polar groups are rarely interacting. The dispersion term was modeled after the AGBNP method originally introduced by Gallicchio et al.(2) but extended to the membrane environment via atom-type dependent density profiles along the membrane normal. The model was initially parametrized against membrane insertion free energy profiles of amino acid analogs from explicit lipid simulations. Further validation involved the energetics of model compound interactions and peptide-peptide interactions within the membrane. [1] Tanizaki, S. et al. 2005, J. Chem. Phys. 122, 124706. [2] Gallicchio, E. et al. 2004, J Comput. Chem. 25, 479-499. 3168-Pos Board B545 Obtaining Binding Free Energy from a Path Sampling without Force Bias Duy Phuoc Tran1, Akio Kitao2. 1 Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa city, CHIBA prefecture, Japan, 2Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo city, TOKYO, Japan. Binding/dissociation free energy is among the most important quantities for computer-aided drug design. During generating dissociation pathways, the use of force bias can lead to the production of unnatural pathways, which may not sample more plausible dissociation configurations of protein/ligand complexes. Therefore, we apply the Parallel Cascade Selection Molecular Dy-
3170-Pos Board B547 How to Easily Extract Physical Properties from MD Simulations of Lipid Membranes with Fatslim Sebastien Buchoux. Dept Enzy/Cell Eng, Univ Picardie Jule Verne, Amiens, France. Extracting physical parameters from MD simulations of lipid membranes is crucial when comparing simulations with experimental observations. Unfortunately, the analysis of the trajectories may be problematic because of the size of the generated files and/or because extracting relevant data from atom coordinates stored in the trajectories may not be trivial. In order to overcome these difficulties, FATSLiM (‘‘Fast Analysis Toolbox for Simulations of Lipid Membranes’’ - https://fatslim.github.io/) was developed. The main goal of this free open source software is to provide a fast and robust analytical tool to extract physical parameters such as bilayer thickness, area per lipid, lateral diffusion or lipid order parameter from molecular dynamics simulations of lipid membranes. FATSLiM is computationally efficient as it scales linearly with the number of lipids and is parallelized. As an illustration, on a 8-core machine, extracting both the thickness and the area per lipid from a 100-frame trajectory of a MD simulation of 12800 coarse-grained lipids takes about 15 seconds (35 seconds for All-Atom lipids) while needing 56 MB of memory (155 MB for AllAtom lipids). APL@voro, one the fastest tools, gives comparable results in 28 seconds (3.5 min for All-Atom lipids) but requires almost 1.6 GB of memory (11 GB for All-Atom lipids) and is not able to work with non-planar bilayers. Indeed, as opposed to other softwares, FATSLiM does not rely on any assumption regarding the bilayer planarity. In contrast, It calculates the local topology so it can then perform analysis on a planar membrane, an ondulating bilayer, or even a vesicle with the same accuracy and reliability. As such, it is perfectly suited to investigate the effect of a protein (on any molecule) on the properties/morphology of a membrane.
Wednesday, March 2, 2016
Hamiltonian exchange for free energy landscape calculation of SH3 domainp41 peptide binding complex were carried out on IBM Blue Gene/Q computer to demonstrate efficacy of REST2 based on the present implementation. For the (AAQAA)3 system, alpha-helix contents in agreement with experiment are observed within 200ns simulation; for the T4 Lysozyme-pxylene binding complex, accurate sampling of rotameric states orthogonal to the thermodynamic axis results in binding free energy in excellent agreement with experiment; for SH3 domain-p41 peptide binding complex, REST2 significantly accelerates the convergence of PMF by enhancing structural reorganization along the reaction path.
namics (PaCS-MD) [J. Chem. Phys. 139, 035103 (2013)] to generate multiple dissociation pathways without applying force biases and specific targeting scheme. We perform dissociation simulation for Hen-egg Lysozyme and a ligand Tri-N-Acetylchitotiose as a test case. Applying Umbrella Sampling (US) for the selected PaCS-MD snapshots, the obtained free energy profile is in good agreement with experimental binding free energy. In addition, from the trajectories of PaCS-MD, we build the Markov State Model and calculate the free energy profile. Without any further simulations as in US, the difference of free energy of the bound state and un-bound state is also well-reproduced.
3166-Pos Board B543 Improved Estimation of Long-Time Kinetics using Non-Markovian Analysis of Trajectory Segments: Application to Protein Folding and Unfolding Ernesto Suarez, Daniel M. Zuckerman. Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA. What is the best way to estimate long-time kinetics from finite trajectory data for molecular simulations? A range of sophisticated Markov-model schemes have been developed to analyze behavior arising from discretization of a continuous configuration space into states. However, discretization into finite states leads intrinsically to non-Markovian behavior, and hence to bias in Markov-based estimates of quantities such as the mean first-passage time (MFPT). We have shown previously that it is sufficient to include just part of the history information [JCTC 2014, 10, pp 2658-2667] to correct bias in MFPT estimation. Here, we attempt to develop practical nonMarkovian analyses applicable to protein simulations. The schemes are tested using msec msec-scale simulation data [Science 2011, 334(6055), pp 517-520], which provide reference MFPT values. We progressively reduce the amount of data used by the non-Markovian estimators by cutting the trajectories in small fragments and selecting just a fraction of them. In every case we are able to obtain reasonably good results for both folding and unfolding MFPTs even when the total length of trajectory data used is well below the longer of the MFPTs. Our estimators are relatively insensitive to the construction of states, reducing the bias over a wide range of lag times, yielding reasonable MFPT values even with very crude discretizations.
3169-Pos Board B546 A Multiscale Approach To Understanding Protein Ligand Binding Process Tohru Terada1,2, Tatsuki Negami1, Kentaro Shimizu1,2. 1 Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan, 2Department of Biotechnology, The University of Tokyo, Tokyo, Japan. Owing to the recent rapid increase in computational power and the improvement of the algorithms, it is now possible to reproduce the whole process of protein–ligand binding in a molecular dynamics (MD) simulation. However, because the ligand-binding process is a stochastic process, it is necessary to repeat the MD simulation many times to fully understand its statistical nature. Therefore, it is still difficult to apply the MD simulation to various protein– ligand systems and to elucidate the general properties of the ligand-binding process. To solve this problem, we are developing a multi-scale approach that combines the coarse-grained (CG) and the all-atom (AA) MD methods. Recently, we have shown that the ligand-binding process can be reproduced in the CGMD simulations with the MARTINI force field [Negami et al. J Comput. Chem. 35, 1835–1845 (2014)]. In this study, the CGMD simulations were applied to two different protein–ligand systems. For each system, 1–4 ms simulations were performed 50–100 times with different initial ligand placement and different initial velocities. The binding and unbinding rate constants and the dissociation constants calculated from the CGMD trajectories were consistent with the experimental values. Furthermore, the ligands tended to enter the ligand-binding pockets through specific pathways. In the present study, we optimized the pathway of a protein–ligand system toward the minimum freeenergy pathway using the string method. The pathway was discretized with 32 images represented by a set of interatomic distances between the protein and the ligand. At each position of the image, free-energy gradient was calculated using the AAMD simulation and the pathway was optimized accordingly. The simulation was performed for 3032 ns in total. We will discuss the ligand-binding process based on the free-energy profile calculated along the optimized pathway.
3167-Pos Board B544 HDGB Implicit Membrane Model with a van Der Waals Dispersion Term Bercem Dutagaci1, Maryam Sayadi1, Michael Feig1,2. 1 Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA, 2Department of Chemistry, Michigan State University, East Lansing, MI, USA. The Heterogeneous Dielectric Generalized Born (HDGB) model(1) allows simulations of membrane-bound systems with an implicit membrane formalism using dielectric profiles matched against explicit membrane simulations. In this study, an extension of the original model is described where the solventaccessible surface area term is extended with a van der Waals dispersion term to improve the estimation of the non-polar components of the free energy of solvation. This is especially important in the hydrophobic region of bilayer systems where charged and polar groups are rarely interacting. The dispersion term was modeled after the AGBNP method originally introduced by Gallicchio et al.(2) but extended to the membrane environment via atom-type dependent density profiles along the membrane normal. The model was initially parametrized against membrane insertion free energy profiles of amino acid analogs from explicit lipid simulations. Further validation involved the energetics of model compound interactions and peptide-peptide interactions within the membrane. [1] Tanizaki, S. et al. 2005, J. Chem. Phys. 122, 124706. [2] Gallicchio, E. et al. 2004, J Comput. Chem. 25, 479-499. 3168-Pos Board B545 Obtaining Binding Free Energy from a Path Sampling without Force Bias Duy Phuoc Tran1, Akio Kitao2. 1 Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa city, CHIBA prefecture, Japan, 2Institute of Molecular and Cellular Biosciences, The University of Tokyo, Bunkyo city, TOKYO, Japan. Binding/dissociation free energy is among the most important quantities for computer-aided drug design. During generating dissociation pathways, the use of force bias can lead to the production of unnatural pathways, which may not sample more plausible dissociation configurations of protein/ligand complexes. Therefore, we apply the Parallel Cascade Selection Molecular Dy-
3170-Pos Board B547 How to Easily Extract Physical Properties from MD Simulations of Lipid Membranes with Fatslim Sebastien Buchoux. Dept Enzy/Cell Eng, Univ Picardie Jule Verne, Amiens, France. Extracting physical parameters from MD simulations of lipid membranes is crucial when comparing simulations with experimental observations. Unfortunately, the analysis of the trajectories may be problematic because of the size of the generated files and/or because extracting relevant data from atom coordinates stored in the trajectories may not be trivial. In order to overcome these difficulties, FATSLiM (‘‘Fast Analysis Toolbox for Simulations of Lipid Membranes’’ - https://fatslim.github.io/) was developed. The main goal of this free open source software is to provide a fast and robust analytical tool to extract physical parameters such as bilayer thickness, area per lipid, lateral diffusion or lipid order parameter from molecular dynamics simulations of lipid membranes. FATSLiM is computationally efficient as it scales linearly with the number of lipids and is parallelized. As an illustration, on a 8-core machine, extracting both the thickness and the area per lipid from a 100-frame trajectory of a MD simulation of 12800 coarse-grained lipids takes about 15 seconds (35 seconds for All-Atom lipids) while needing 56 MB of memory (155 MB for AllAtom lipids). APL@voro, one the fastest tools, gives comparable results in 28 seconds (3.5 min for All-Atom lipids) but requires almost 1.6 GB of memory (11 GB for All-Atom lipids) and is not able to work with non-planar bilayers. Indeed, as opposed to other softwares, FATSLiM does not rely on any assumption regarding the bilayer planarity. In contrast, It calculates the local topology so it can then perform analysis on a planar membrane, an ondulating bilayer, or even a vesicle with the same accuracy and reliability. As such, it is perfectly suited to investigate the effect of a protein (on any molecule) on the properties/morphology of a membrane.