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Communication: Entropic measure to prevent energy over-minimization in molecular dynamics simulations
1.A. Kryshtafovych, K. Fidelis, and J. Moult, “CASP10 results compared to those of previous CASP experiments,” Proteins: Struct., Funct., Bioinf. 82(S2), 164–174 (2014).
2.J. Moult, K. Fidelis, A. Kryshtafovych, T. Schwede, and A. Tramontano, “Critical assessment of methods of protein structure prediction (CASP)—Round X,” Proteins: Struct., Funct., Bioinf. 82(S2), 1–6 (2014).
3.T. Schlick, Molecular Modeling and Simulation: An Interdisciplinary Guide (Springer Science & Business Media, 2010), Vol. 21.
4.J. W. Ponder and F. M. Richards, “An efficient Newton-like method for molecular mechanics energy minimization of large molecules,” J. Comput. Chem. 8(7), 1016–1024 (1987).
5.K. D. Gibson and H. A. Scheraga, “Revised algorithms for the build-up procedure for predicting protein conformations by energy minimization,” J. Comput. Chem. 8(6), 826–834 (1987).
6.J. Yang, R. Yan, A. Roy, D. Xu, J. Poisson, and Y. Zhang, “The I-TASSER suite: Protein structure and function prediction,” Nat. Methods 12(1), 7–8 (2015).
8.S. Ramachandran, P. Kota, F. Ding, and N. V. Dokholyan, “Automated minimization of steric clashes in protein structures,” Proteins: Struct., Funct., Bioinf. 79(1), 261–270 (2011).
14.E. Alm and D. Baker, “Prediction of protein-folding mechanisms from free-energy landscapes derived from native structures,” Proc. Natl. Acad. Sci. U. S. A. 96(20), 11305–11310 (1999).
15.E. M. Boczko and C. L. Brooks, “First-principles calculation of the folding free energy of a three-helix bundle protein,” Science 269(5222), 393–396 (1995).
16.V. I. Abkevich, A. M. Gutin, and E. I. Shakhnovich, “Free energy landscape for protein folding kinetics: Intermediates, traps, and multiple pathways in theory and lattice model simulations,” J. Chem. Phys. 101(7), 6052–6062 (1994).
17.A. K. Felts, E. Gallicchio, A. Wallqvist, and R. M. Levy, “Distinguishing native conformations of proteins from decoys with an effective free energy estimator based on the OPLS all-atom force field and the surface generalized Born solvent model,” Proteins: Struct., Funct., Genet. 48(2), 404–422 (2002).
18.S. V. Krivov and M. Karplus, “Hidden complexity of free energy surfaces for peptide (protein) folding,” Proc. Natl. Acad. Sci. U. S. A. 101(41), 14766–14770 (2004).
21.R. Adamczak and J. Meller, “On the transferability of folding and threading potentials and sequence-independent filters for protein folding simulations,” Mol. Phys. 102(11-12), 1291–1305 (2004).
22.S. E. Toal, D. J. Verbaro, and R. Schweitzer-Stenner, “Role of enthalpy–entropy compensation interactions in determining the conformational propensities of amino acid residues in unfolded peptides,” J. Phys. Chem. B 118(5), 1309–1318 (2014).
23.J. Schymkowitz, J. Borg, F. Stricher, R. Nys, F. Rousseau, and L. Serrano, “The FoldX web Server: An online force field,” Nucleic Acids Res. 33(Suppl. 2), W382–W388 (2005).
24.F. C. Bernstein, T. F. Koetzle, G. J. Williams, E. F. Meyer, M. D. Brice, J. R. Rodgers, O. Kennard, T. Shimanouchi, and M. Tasumi, “The protein data bank,” Eur. J. Biochem. 80(2), 319–324 (1977).
26.J. C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R. D. Skeel, L. Kale, and K. Schulten, “Scalable molecular dynamics with NAMD,” J. Comput. Chem. 26(16), 1781–1802 (2005).
27.A. D. MacKerell, D. Bashford, M. Bellott, R. L. Dunbrack, J. D. Evanseck, M. J. Field, S. Fischer, J. Gao, H. Guo, and S. Ha, “All-atom empirical potential for molecular modeling and dynamics studies of proteins,” J. Phys. Chem. B 102(18), 3586–3616 (1998).
28.R. Guerois, J. E. Nielsen, and L. Serrano, “Predicting changes in the stability of proteins and protein complexes: A study of more than 1000 mutations,” J. Mol. Biol. 320(2), 369–387 (2002).
30.W. Li, Z. Liu, R. K. Koripella, R. Langlois, S. Sanyal, and J. Frank, “Activation of GTP hydrolysis in mRNA-tRNA translocation by elongation factor G,” Sci. Adv. 1(4), e1500169 (2015).
33.B. Kamaraj and A. Bogaerts, “Structure and function of p53-DNA complexes with inactivation and rescue mutations: A molecular dynamics simulation study,” PLoS One 10(8), e0134638 (2015).
34.L. Jiang, X. Zhang, X. Chen, Y. He, L. Qiao, Y. Zhang, G. Li, and Y. Xiang, “Virtual screening and molecular dynamics study of potential negative allosteric modulators of mGluR1 from Chinese herbs,” Molecules 20(7), 12769–12786 (2015).
35.T. R. Caulfield, F. C. Fiesel, E. L. Moussaud-Lamodiere, D. F. Dourado, S. C. Flores, and W. Springer, “Phosphorylation by PINK1 releases the UBL domain and initializes the conformational opening of the E3 ubiquitin ligase Parkin,” PLoS Comput. Biol. 10, e1003935 (2014).
36.CHARMM Tutorial, 2014.
38.V. Munoz and L. Serrano, “Intrinsic secondary structure propensities of the amino acids, using statistical Φ–ψ matrices: Comparison with experimental scales,” Proteins: Struct., Funct., Genet. 20(4), 301–311 (1994).
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This work examines the impact of energy over-minimization on an ensemble of biological molecules subjected to the potential energy minimization procedure in vacuum. In the studied structures, long potential energy minimization stage leads to an increase of the main- and side-chain entropies in proteins. We show that such over-minimization may diverge the protein
structures from the near-native attraction basin which possesses a minimum of free energy. We propose a measure based on the Pareto front of total entropy for quality assessment of minimized protein
conformation. This measure may help in selection of adequate number of energy minimization steps in protein modelling and, thus, in preservation of the near-native protein
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