Using SMOG 2 to Simulate Complex Biomolecular Assemblies.

Over the last 20 years, the application of structure-based (Gō-like) models has ranged from protein folding with coarse-grained models to all-atom representations of large-scale molecular assemblies. While there are many variants that may be employed, the common feature of these models is that some (or all) of the stabilizing energetic interactions are defined based on the knowledge of a particular experimentally obtained conformation. With the generality of this approach, there was a need for a versatile computational platform for designing and implementing this class of models. To this end, the SMOG 2 software package provides an easy-to-use interface, where the user has full control of the model parameters. This software allows the user to edit XML-formatted files in order to provide definitions of new structure-based models. SMOG 2 reads these "template" files and maps the interactions onto specific structures, which are provided in PDB format. The force field files produced by SMOG 2 may then be used to perform simulations with a variety of popular molecular dynamics suites. In this chapter, we describe some of the key features of the SMOG 2 package, while providing examples and strategies for applying these techniques to complex (often large-scale) molecular assemblies, such as the ribosome.

[1]  Laxmikant V. Kalé,et al.  Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..

[2]  John Karanicolas,et al.  Improved Gō-like models demonstrate the robustness of protein folding mechanisms towards non-native interactions. , 2003, Journal of molecular biology.

[3]  J. Onuchic,et al.  The Many Faces of Structure-Based Potentials: From Protein Folding Landscapes to Structural Characterization of Complex Biomolecules , 2012 .

[4]  José N Onuchic,et al.  Accommodation of aminoacyl-tRNA into the ribosome involves reversible excursions along multiple pathways. , 2010, RNA.

[5]  Yaakov Levy,et al.  Protein sliding along DNA: dynamics and structural characterization. , 2009, Journal of molecular biology.

[6]  M. James,et al.  Crystal and molecular structure of the serine proteinase inhibitor CI-2 from barley seeds. , 1988, Biochemistry.

[7]  Yaakov Levy,et al.  DNA search efficiency is modulated by charge composition and distribution in the intrinsically disordered tail , 2010, Proceedings of the National Academy of Sciences.

[8]  N. C. Robinson,et al.  The kinetic stability of cytochrome C oxidase: effect of bound phospholipid and dimerization. , 2014, Biophysical journal.

[9]  J. Noel,et al.  Capturing transition paths and transition states for conformational rearrangements in the ribosome. , 2014, Biophysical journal.

[10]  P. Wolynes,et al.  The experimental survey of protein-folding energy landscapes , 2005, Quarterly Reviews of Biophysics.

[11]  Berk Hess,et al.  GROMACS 3.0: a package for molecular simulation and trajectory analysis , 2001 .

[12]  Marco Biasini,et al.  SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information , 2014, Nucleic Acids Res..

[13]  Jinyuan Yan,et al.  Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network , 2017, PLoS Comput. Biol..

[14]  P. Wolynes,et al.  Intermediates and barrier crossing in a random energy model , 1989 .

[15]  J. Howard,et al.  The Motility of Axonemal Dynein Is Regulated by the Tubulin Code , 2014, Biophysical journal.

[16]  Alexey Savelyev,et al.  Molecular renormalization group coarse-graining of electrolyte solutions: application to aqueous NaCl and KCl. , 2009, The journal of physical chemistry. B.

[17]  Jianpeng Ma,et al.  CHARMM: The biomolecular simulation program , 2009, J. Comput. Chem..

[18]  Nikolay V Dokholyan Computational Modeling of Biological Systems , 2012 .

[19]  J. Onuchic,et al.  Molecular Simulations Suggest a Force-Dependent Mechanism of Vinculin Activation. , 2017, Biophysical journal.

[20]  Ryan L. Hayes,et al.  SMOG 2: A Versatile Software Package for Generating Structure-Based Models , 2016, PLoS Comput. Biol..

[21]  Vijay S. Pande,et al.  OpenMM 7: Rapid development of high performance algorithms for molecular dynamics , 2016, bioRxiv.

[22]  J. Onuchic,et al.  Biomolecular dynamics: order–disorder transitions and energy landscapes , 2012, Reports on progress in physics. Physical Society.

[23]  J. Noel,et al.  How EF-Tu can contribute to efficient proofreading of aa-tRNA by the ribosome , 2016, Nature Communications.

[24]  J. Onuchic,et al.  Topological and energetic factors: what determines the structural details of the transition state ensemble and "en-route" intermediates for protein folding? An investigation for small globular proteins. , 2000, Journal of molecular biology.

[25]  J. Onuchic,et al.  Interplay among tertiary contacts, secondary structure formation and side-chain packing in the protein folding mechanism: all-atom representation study of protein L. , 2003, Journal of molecular biology.

[26]  J. Onuchic,et al.  An all‐atom structure‐based potential for proteins: Bridging minimal models with all‐atom empirical forcefields , 2009, Proteins.

[27]  P. Kollman,et al.  How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? , 2000 .

[28]  J. Åqvist,et al.  Bridging the gap between ribosome structure and biochemistry by mechanistic computations. , 2012, Current opinion in structural biology.

[29]  Shachi Gosavi,et al.  Using the folding landscapes of proteins to understand protein function. , 2016, Current opinion in structural biology.

[30]  D Thirumalai,et al.  Effect of finite size on cooperativity and rates of protein folding. , 2006, The journal of physical chemistry. A.

[31]  J. Noel,et al.  Anisotropic Fluctuations in the Ribosome Determine tRNA Kinetics. , 2017, The journal of physical chemistry. B.

[32]  J. Åqvist,et al.  Origin of the omnipotence of eukaryotic release factor 1 , 2017, Nature Communications.

[33]  Kien Nguyen,et al.  Exploring the Balance between Folding and Functional Dynamics in Proteins and RNA , 2015, International journal of molecular sciences.

[34]  B. Schuler,et al.  Integrated view of internal friction in unfolded proteins from single-molecule FRET, contact quenching, theory, and simulations , 2017, Proceedings of the National Academy of Sciences.

[35]  J. Onuchic,et al.  Connecting thermal and mechanical protein (un)folding landscapes. , 2014, Biophysical journal.