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Luthey-Schulten, “ Experimental and computational determination of tRNA dynamics,” FEBS Lett. Luthey-Schulten, “ Dynamical networks in tRNA:protein complexes,” Proc. Grossman, “ Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials,” Nat. Tiwary, “ Machine learning approaches for analyzing and enhancing molecular dynamics simulations,” Curr. Moosavi-Movahedi, “ Machine learning and network analysis of molecular dynamics trajectories reveal two chains of red/ox-specific residue interactions in human protein disulfide isomerase,” Sci. Bernardi, “ Direction matters: Monovalent streptavidin/biotin complex under load,” Nano Lett. De Fabritiis, “ HTMD: High-throughput molecular dynamics for molecular discovery,” J. Bonati, “ Conformational and functional analysis of molecular dynamics trajectories by self-organising maps,” BMC Bioinf. Schulten, “ Enhanced sampling techniques in molecular dynamics simulations of biological systems,” Biochim. Bussi, “ Enhanced sampling in molecular dynamics using metadynamics, replica-exchange, and temperature-acceleration,” Entropy 16, 163– 199 (2014). Schulten, “ Molecular dynamics simulations of large macromolecular complexes,” Curr. Isralewitz et al., “ Atoms to phenotypes: Molecular design principles of cellular energy metabolism,” Cell 179, 1098– 1111 (2019). McCammon, “ Protein structural fluctuations during a period of 100 ps,” Nature 277, 578 (1979). Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes. Our new implementation was employed to investigate three different systems, with up to 2.5M atoms, namely, the OMP-decarboxylase, the leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and respiratory complex I in a membrane environment.
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Using the popular visualization program visual molecular dynamics (VMD), high-quality renderings of the networks over the biomolecular structures can be produced. All data processing and analysis are conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination of optimal paths. In this work, we provide an evolution of the method, application, and interface. However, in the dawn of exascale computing, this method is frequently limited to relatively small biomolecular systems. In order to identify important residues and information pathways within molecular complexes, the dynamical network analysis method was developed and has since been broadly applied in the literature. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions or optimization of a chemical process using bioengineered molecules. Molecular interactions are essential for regulation of cellular processes from the formation of multi-protein complexes to the allosteric activation of enzymes. Note: This paper is part of the JCP Special Topic on Classical Molecular Dynamics (MD) Simulations: Codes, Algorithms, Force Fields, and Applications.
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2 Department of Chemistry, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, USA.1 Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois 61801, USA.