An Efficient Algorithm to Perform Local Concerted Movements of a Chain Molecule

The devising of efficient concerted rotation moves that modify only selected local portions of chain molecules is a long studied problem. Possible applications range from speeding the uncorrelated sampling of polymeric dense systems to loop reconstruction and structure refinement in protein modeling. Here, we propose and validate, on a few pedagogical examples, a novel numerical strategy that generalizes the notion of concerted rotation. The usage of the Denavit-Hartenberg parameters for chain description allows all possible choices for the subset of degrees of freedom to be modified in the move. They can be arbitrarily distributed along the chain and can be distanced between consecutive monomers as well. The efficiency of the methodology capitalizes on the inherent geometrical structure of the manifold defined by all chain configurations compatible with the fixed degrees of freedom. The chain portion to be moved is first opened along a direction chosen in the tangent space to the manifold, and then closed in the orthogonal space. As a consequence, in Monte Carlo simulations detailed balance is easily enforced without the need of using Jacobian reweighting. Moreover, the relative fluctuations of the degrees of freedom involved in the move can be easily tuned. We show different applications: the manifold of possible configurations is explored in a very efficient way for a protein fragment and for a cyclic molecule; the “local backbone volume”, related to the volume spanned by the manifold, reproduces the mobility profile of all-α helical proteins; the refinement of small protein fragments with different secondary structures is addressed. The presented results suggest our methodology as a valuable exploration and sampling tool in the context of bio-molecular simulations.

[1]  J. Erickson Elementary Analysis , 2013 .

[2]  Chaok Seok,et al.  A kinematic view of loop closure , 2004, J. Comput. Chem..

[3]  D. Theodorou,et al.  A concerted rotation algorithm for atomistic Monte Carlo simulation of polymer melts and glasses , 1993 .

[4]  Ernst-Walter Knapp,et al.  Polypeptide folding with off-lattice Monte Carlo dynamics: the method , 1996, European Biophysics Journal.

[5]  Jack Snoeyink,et al.  Probik: Protein Backbone Motion by Inverse Kinematics , 2005, WAFR.

[6]  William L. Jorgensen,et al.  Monte Carlo backbone sampling for polypeptides with variable bond angles and dihedral angles using concerted rotations and a Gaussian bias , 2003 .

[7]  E. Coutsias,et al.  Sub-angstrom accuracy in protein loop reconstruction by robotics-inspired conformational sampling , 2009, Nature Methods.

[8]  Aaron R. Dinner,et al.  Local deformations of polymers with nonplanar rigid main-chain internal coordinates , 2000, J. Comput. Chem..

[9]  M. Betancourt,et al.  Efficient Monte Carlo trial moves for polypeptide simulations. , 2005, The Journal of chemical physics.

[10]  A. Laio,et al.  Escaping free-energy minima , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[11]  W. Rudin Principles of mathematical analysis , 1964 .

[12]  A. Liwo,et al.  Computational techniques for efficient conformational sampling of proteins. , 2008, Current opinion in structural biology.

[13]  Alessandro Laio,et al.  Clustering by fast search and find of density peaks , 2014, Science.

[14]  D. Landau,et al.  Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  J. Denavit,et al.  A kinematic notation for lower pair mechanisms based on matrices , 1955 .

[16]  Ian W. Davis,et al.  Structure validation by Cα geometry: ϕ,ψ and Cβ deviation , 2003, Proteins.

[17]  Rongfang Bie,et al.  Clustering by fast search and find of density peaks via heat diffusion , 2016, Neurocomputing.

[18]  William L. Jorgensen,et al.  Monte Carlo Backbone Sampling for Nucleic Acids Using Concerted Rotations Including Variable Bond Angles , 2004 .

[19]  Ferdinand Freudenstein,et al.  Kinematic Synthesis of Linkages , 1965 .

[20]  G. Favrin,et al.  Monte Carlo update for chain molecules: Biased Gaussian steps in torsional space , 2001, cond-mat/0103580.

[21]  Robert E. Bruccoleri,et al.  Chain closure with bond angle variations , 1985 .

[22]  Dinesh Manocha,et al.  Conformational analysis of molecular chains using nano-kinematics , 1995, Comput. Appl. Biosci..

[23]  K. Dill,et al.  Resultants and Loop Closure , 2006 .

[24]  Amelie Stein,et al.  Improvements to Robotics-Inspired Conformational Sampling in Rosetta , 2013, PloS one.

[25]  Wouter Boomsma,et al.  Subtle Monte Carlo Updates in Dense Molecular Systems. , 2012, Journal of chemical theory and computation.

[26]  R J Read,et al.  Crystallography & NMR system: A new software suite for macromolecular structure determination. , 1998, Acta crystallographica. Section D, Biological crystallography.

[27]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[28]  Kresten Lindorff-Larsen,et al.  PHAISTOS: A framework for Markov chain Monte Carlo simulation and inference of protein structure , 2013, J. Comput. Chem..

[29]  N. Go,et al.  Ring Closure and Local Conformational Deformations of Chain Molecules , 1970 .

[30]  Wen Miin Hwang,et al.  Computer-aided structural synthesis of planar kinematic chains with simple joints , 1992 .

[31]  Abhinandan Jain,et al.  A fast recursive algorithm for molecular dynamics simulation , 1993 .

[32]  K. Wüthrich,et al.  Torsion angle dynamics for NMR structure calculation with the new program DYANA. , 1997, Journal of molecular biology.