1
|
Airas J, Ding X, Zhang B. Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks. ACS CENTRAL SCIENCE 2023; 9:2286-2297. [PMID: 38161379 PMCID: PMC10755853 DOI: 10.1021/acscentsci.3c01160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 01/03/2024]
Abstract
Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.
Collapse
Affiliation(s)
- Justin Airas
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, United
States
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, United
States
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139-4307, United
States
| |
Collapse
|
2
|
Lemcke S, Appeldorn JH, Wand M, Speck T. Toward a structural identification of metastable molecular conformations. J Chem Phys 2023; 159:114105. [PMID: 37712784 DOI: 10.1063/5.0164145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/21/2023] [Indexed: 09/16/2023] Open
Abstract
Interpreting high-dimensional data from molecular dynamics simulations is a persistent challenge. In this paper, we show that for a small peptide, deca-alanine, metastable states can be identified through a neural net based on structural information alone. While processing molecular dynamics data, dimensionality reduction is a necessary step that projects high-dimensional data onto a low-dimensional representation that, ideally, captures the conformational changes in the underlying data. Conventional methods make use of the temporal information contained in trajectories generated through integrating the equations of motion, which forgoes more efficient sampling schemes. We demonstrate that EncoderMap, an autoencoder architecture with an additional distance metric, can find a suitable low-dimensional representation to identify long-lived molecular conformations using exclusively structural information. For deca-alanine, which exhibits several helix-forming pathways, we show that this approach allows us to combine simulations with different biasing forces and yields representations comparable in quality to other established methods. Our results contribute to computational strategies for the rapid automatic exploration of the configuration space of peptides and proteins.
Collapse
Affiliation(s)
- Simon Lemcke
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 7-9, 55128 Mainz, Germany
| | - Jörn H Appeldorn
- Institut für Physik, Johannes Gutenberg-Universität Mainz, Staudingerweg 7-9, 55128 Mainz, Germany
| | - Michael Wand
- Institut für Informatik, Johannes Gutenberg-Universität Mainz, Staudingerweg 9, 55128 Mainz, Germany
| | - Thomas Speck
- Institut für Theoretische Physik IV, Universität Stuttgart, Heisenbergstr. 3, 70569 Stuttgart, Germany
| |
Collapse
|
3
|
Tyler S, Laforge C, Guzzo A, Nicolaï A, Maisuradze GG, Senet P. Einstein Model of a Graph to Characterize Protein Folded/Unfolded States. Molecules 2023; 28:6659. [PMID: 37764437 PMCID: PMC10536427 DOI: 10.3390/molecules28186659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023] Open
Abstract
The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show that any connected, undirected, and simple graph can be associated with a linear chain of atoms in thermal equilibrium. This analogy provides an interpretation of the usual topological descriptors of a graph, namely the Kirchhoff index and Randić resistance, in terms of effective force constants of a linear chain. We derive an exact relation between the Kirchhoff index and the average shortest path length for a linear graph and define the free energies of a graph using an Einstein model. Second, we represent the three-dimensional protein structures by connected, undirected, and simple graphs. As a proof of concept, we compute the topological descriptors and the graph free energies for an all-atom molecular dynamics trajectory of folding/unfolding events of the proteins Trp-cage and HP-36 and for the ensemble of experimental NMR models of Trp-cage. The present work shows that the local, nonlocal, and global force constants and free energies of a graph are promising tools to quantify unfolded/disordered protein states and folding/unfolding dynamics. In particular, they allow the detection of transient misfolded rigid states.
Collapse
Affiliation(s)
- Steve Tyler
- Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR CNRS 6303, Université de Bourgogne, 21078 Dijon CEDEX, France
| | - Christophe Laforge
- Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR CNRS 6303, Université de Bourgogne, 21078 Dijon CEDEX, France
| | - Adrien Guzzo
- Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR CNRS 6303, Université de Bourgogne, 21078 Dijon CEDEX, France
| | - Adrien Nicolaï
- Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR CNRS 6303, Université de Bourgogne, 21078 Dijon CEDEX, France
| | - Gia G. Maisuradze
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA
| | - Patrick Senet
- Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR CNRS 6303, Université de Bourgogne, 21078 Dijon CEDEX, France
| |
Collapse
|
4
|
Airas J, Ding X, Zhang B. Transferable Coarse Graining via Contrastive Learning of Graph Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.08.556923. [PMID: 37745447 PMCID: PMC10515757 DOI: 10.1101/2023.09.08.556923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Coarse-grained (CG) force fields are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. These simulations employ simplified models grouping atoms into interaction sites, enabling the study of complex biomolecular systems over biologically relevant timescales. Efforts are underway to develop accurate and transferable CG force fields, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to many-body effects, lack of analytical expressions for the PMF, and limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent CG force fields and potential contrasting for parameterization from atomistic simulation data. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside the training data. Our study offers valuable insights for building accurate coarse-grained models bottom-up.
Collapse
Affiliation(s)
- Justin Airas
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xinqiang Ding
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
5
|
Vila JA. Rethinking the protein folding problem from a new perspective. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023:10.1007/s00249-023-01657-w. [PMID: 37165178 DOI: 10.1007/s00249-023-01657-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/16/2023] [Accepted: 04/30/2023] [Indexed: 05/12/2023]
Abstract
One of the main concerns of Anfinsen was to reveal the connection between the amino-acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, the thermodynamic hypothesis. As to the how, this remains an unsolved challenge. Consequently, the protein folding problem is examined here from a new perspective, namely, as an 'analytic whole'. Conceiving the protein folding in this way enabled us to (i) examine in detail why the force-field-based approaches have failed, among other purposes, in their ability to predict the three-dimensional structure of a protein accurately; (ii) propose how to redefine them to prevent these shortcomings, and (iii) conjecture on the origin of the state-of-the-art numerical-methods success to predict the tridimensional structure of proteins accurately.
Collapse
Affiliation(s)
- Jorge A Vila
- IMASL-CONICET, Universidad Nacional de San Luis, Ejército de Los Andes 950, 5700, San Luis, Argentina.
| |
Collapse
|
6
|
Yanaka S, Yagi-Utsumi M, Kato K, Kuwajima K. The B domain of protein A retains residual structures in 6 M guanidinium chloride as revealed by hydrogen/deuterium-exchange NMR spectroscopy. Protein Sci 2023; 32:e4569. [PMID: 36659853 PMCID: PMC9926473 DOI: 10.1002/pro.4569] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 01/21/2023]
Abstract
The characterization of residual structures persistent in unfolded proteins is an important issue in studies of protein folding, because the residual structures present, if any, may form a folding initiation site and guide the subsequent folding reactions. Here, we studied the residual structures of the isolated B domain (BDPA) of staphylococcal protein A in 6 M guanidinium chloride. BDPA is a small three-helix-bundle protein, and until recently its folding/unfolding reaction has been treated as a simple two-state process between the native and the fully unfolded states. We employed a dimethylsulfoxide (DMSO)-quenched hydrogen/deuterium (H/D)-exchange 2D NMR techniques with the use of spin desalting columns, which allowed us to investigate the H/D-exchange behavior of individually identified peptide amide (NH) protons. We obtained H/D-exchange protection factors of the 21 NH protons that form an α-helical hydrogen bond in the native structure, and the majority of these NH protons were significantly protected with a protection factor of 2.0-5.2 in 6 M guanidinium chloride, strongly suggesting that these weakly protected NH protons form much stronger hydrogen bonds under native folding conditions. The results can be used to deduce the structure of an early folding intermediate, when such an intermediate is shown by other methods. Among three native helical regions, the third helix in the C-terminal side was highly protected and stabilized by side-chain salt bridges, probably acting as the folding initiation site of BDPA. The present results are discussed in relation to previous experimental and computational findings on the folding mechanisms of BDPA.
Collapse
Affiliation(s)
- Saeko Yanaka
- Exploratory Research Center on Life and Living Systems (ExCELLS) and Institute for Molecular Science (IMS), National Institutes of Natural Sciences, Myodaiji, Okazaki, Aichi, Japan.,Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (the Graduate University for Advanced Studies), Myodaiji, Okazaki, Aichi, Japan.,Graduate School of Pharmaceutical Sciences, Nagoya City University, Mizuho-ku, Nagoya, Aichi, Japan
| | - Maho Yagi-Utsumi
- Exploratory Research Center on Life and Living Systems (ExCELLS) and Institute for Molecular Science (IMS), National Institutes of Natural Sciences, Myodaiji, Okazaki, Aichi, Japan.,Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (the Graduate University for Advanced Studies), Myodaiji, Okazaki, Aichi, Japan.,Graduate School of Pharmaceutical Sciences, Nagoya City University, Mizuho-ku, Nagoya, Aichi, Japan
| | - Koichi Kato
- Exploratory Research Center on Life and Living Systems (ExCELLS) and Institute for Molecular Science (IMS), National Institutes of Natural Sciences, Myodaiji, Okazaki, Aichi, Japan.,Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (the Graduate University for Advanced Studies), Myodaiji, Okazaki, Aichi, Japan.,Graduate School of Pharmaceutical Sciences, Nagoya City University, Mizuho-ku, Nagoya, Aichi, Japan
| | - Kunihiro Kuwajima
- Department of Physics, School of Science, University of Tokyo, Bunkyo-ku, Tokyo, Japan
| |
Collapse
|
7
|
Heilmann N, Wolf M, Kozlowska M, Sedghamiz E, Setzler J, Brieg M, Wenzel W. Sampling of the conformational landscape of small proteins with Monte Carlo methods. Sci Rep 2020; 10:18211. [PMID: 33097750 PMCID: PMC7585447 DOI: 10.1038/s41598-020-75239-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/12/2020] [Indexed: 12/24/2022] Open
Abstract
Computer simulation provides an increasingly realistic picture of large-scale conformational change of proteins, but investigations remain fundamentally constrained by the femtosecond timestep of molecular dynamics simulations. For this reason, many biologically interesting questions cannot be addressed using accessible state-of-the-art computational resources. Here, we report the development of an all-atom Monte Carlo approach that permits the modelling of the large-scale conformational change of proteins using standard off-the-shelf computational hardware and standard all-atom force fields. We demonstrate extensive thermodynamic characterization of the folding process of the α-helical Trp-cage, the Villin headpiece and the β-sheet WW-domain. We fully characterize the free energy landscape, transition states, energy barriers between different states, and the per-residue stability of individual amino acids over a wide temperature range. We demonstrate that a state-of-the-art intramolecular force field can be combined with an implicit solvent model to obtain a high quality of the folded structures and also discuss limitations that still remain.
Collapse
Affiliation(s)
- Nana Heilmann
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Moritz Wolf
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Mariana Kozlowska
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Elaheh Sedghamiz
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Julia Setzler
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Martin Brieg
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Wolfgang Wenzel
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
| |
Collapse
|
8
|
Wang J, Peng C, Yu Y, Chen Z, Xu Z, Cai T, Shao Q, Shi J, Zhu W. Exploring Conformational Change of Adenylate Kinase by Replica Exchange Molecular Dynamic Simulation. Biophys J 2020; 118:1009-1018. [PMID: 31995738 DOI: 10.1016/j.bpj.2020.01.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 12/28/2019] [Accepted: 01/03/2020] [Indexed: 12/19/2022] Open
Abstract
Replica exchange molecular dynamics (REMD) simulation is a popular enhanced sampling method that is widely used for exploring the atomic mechanism of protein conformational change. However, the requirement of huge computational resources for REMD, especially with the explicit solvent model, largely limits its application. In this study, the availability and efficiency of a variant of velocity-scaling REMD (vsREMD) was assessed with adenylate kinase as an example. Although vsREMD achieved results consistent with those from conventional REMD and experimental studies, the number of replicas required for vsREMD (30) was much less than that for conventional REMD (80) to achieve a similar acceptance rate (∼0.2), demonstrating high efficiency of vsREMD to characterize the protein conformational change and associated free-energy profile. Thus, vsREMD is a highly efficient approach for studying the large-scale conformational change of protein systems.
Collapse
Affiliation(s)
- Jinan Wang
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
| | - Cheng Peng
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yuqu Yu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Zhaoqiang Chen
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Zhijian Xu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China
| | - Tingting Cai
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Qiang Shao
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China
| | - Jiye Shi
- UCB Biopharma SPRL, Braine-l'Alleud, Belgium
| | - Weiliang Zhu
- CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China; Open Studio for Druggability Research of Marine Lead Compounds, Qingdao National Laboratory for Marine Science and Technology, Jimo, Qingdao, China; University of Chinese Academy of Sciences, Beijing, China.
| |
Collapse
|
9
|
Abstract
The folding simulations of three ββα-motifs and β-barrel structured proteins (NTL9, NuG2b, and CspA) were performed to determine the important roles of native and nonnative contacts in protein folding.
Collapse
Affiliation(s)
- Qiang Shao
- Drug Discovery and Design Center
- CAS Key Laboratory of Receptor Research
- Shanghai Institute of Materia Medica
- Chinese Academy of Sciences
- Shanghai
| | - Weiliang Zhu
- Drug Discovery and Design Center
- CAS Key Laboratory of Receptor Research
- Shanghai Institute of Materia Medica
- Chinese Academy of Sciences
- Shanghai
| |
Collapse
|
10
|
Shao Q, Zhu W. The effects of implicit modeling of nonpolar solvation on protein folding simulations. Phys Chem Chem Phys 2018; 20:18410-18419. [PMID: 29946610 DOI: 10.1039/c8cp03156h] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Implicit solvent models, in which the polar and nonpolar solvation free-energies of solute molecules are treated separately, have been widely adopted for molecular dynamics simulation of protein folding. While the development of the implicit models is mainly focused on the methodological improvement and key parameter optimization for polar solvation, nonpolar solvation has been either ignored or described by a simplistic surface area (SA) model. In this work, we performed the folding simulations of multiple β-hairpin and α-helical proteins with varied surface tension coefficients embedded in the SA model to clearly demonstrate the effects of nonpolar solvation treated by a popular SA model on protein folding. The results indicate that the change in the surface tension coefficient does not alter the ability of implicit solvent simulations to reproduce a protein native structure but indeed controls the components of the equilibrium conformational ensemble and modifies the energetic characterization of the folding transition pathway. The suitably set surface tension coefficient can yield explicit solvent simulations and/or experimentally suggested folding mechanism of protein. In addition, the implicit treatment of both polar and nonpolar components of solvation free-energy contributes to the overestimation of the secondary structure in implicit solvent simulations.
Collapse
Affiliation(s)
- Qiang Shao
- Drug Discovery and Design Center, CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai, 201203, China.
| | | |
Collapse
|
11
|
Zavadlav J, Sablić J, Podgornik R, Praprotnik M. Open-Boundary Molecular Dynamics of a DNA Molecule in a Hybrid Explicit/Implicit Salt Solution. Biophys J 2018; 114:2352-2362. [PMID: 29650370 PMCID: PMC6129463 DOI: 10.1016/j.bpj.2018.02.042] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 02/24/2018] [Accepted: 02/28/2018] [Indexed: 12/24/2022] Open
Abstract
The composition and electrolyte concentration of the aqueous bathing environment have important consequences for many biological processes and can profoundly affect the behavior of biomolecules. Nevertheless, because of computational limitations, many molecular simulations of biophysical systems can be performed only at specific ionic conditions: either at nominally zero salt concentration, i.e., including only counterions enforcing the system's electroneutrality, or at excessive salt concentrations. Here, we introduce an efficient molecular dynamics simulation approach for an atomistic DNA molecule at realistic physiological ionic conditions. The simulations are performed by employing the open-boundary molecular dynamics method that allows for simulation of open systems that can exchange mass and linear momentum with the environment. In our open-boundary molecular dynamics approach, the computational burden is drastically alleviated by embedding the DNA molecule in a mixed explicit/implicit salt-bathing solution. In the explicit domain, the water molecules and ions are both overtly present in the system, whereas in the implicit water domain, only the ions are explicitly present and the water is described as a continuous dielectric medium. Water molecules are inserted and deleted into/from the system in the intermediate buffer domain that acts as a water reservoir to the explicit domain, with both water molecules and ions free to enter or leave the explicit domain. Our approach is general and allows for efficient molecular simulations of biomolecules solvated in bathing salt solutions at any ionic strength condition.
Collapse
Affiliation(s)
- Julija Zavadlav
- Computational Science & Engineering Laboratory, ETH Zurich, Zurich, Switzerland
| | - Jurij Sablić
- Laboratory for Molecular Modeling, National Institute of Chemistry, Ljubljana, Slovenia
| | - Rudolf Podgornik
- Theoretical Physics Department, J. Stefan Institute, Ljubljana, Slovenia; Department of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
| | - Matej Praprotnik
- Laboratory for Molecular Modeling, National Institute of Chemistry, Ljubljana, Slovenia; Department of Physics, Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia.
| |
Collapse
|