The temperature intervals with global exchange of replicas empirical accelerated sampling method: Parameter sensitivity and extension to a complex molecular system

The recently developed “temperature intervals with global exchange of replicas” (TIGER2) algorithm is an efficient replica‐exchange sampling algorithm that provides the freedom to specify the number of replicas and temperature levels independently of the size of the system and temperature range to be spanned, thus making it particularly well suited for sampling molecular systems that are considered to be too large to be sampled using conventional replica exchange methods. Although the TIGER2 method is empirical in nature, when appropriately applied it is able to provide sampling that satisfies the balance condition and closely approximates a Boltzmann‐weighted ensemble of states. In this work, we evaluated the influence of factors such as temperature range, temperature spacing, replica number, and sampling cycle design on the accuracy of a TIGER2 simulation based on molecular dynamics simulations of alanine dipeptide in implicit solvent. The influence of these factors is further examined by calculating the properties of a complex system composed of the B1 immunoglobulin‐binding domain of streptococcal protein G (protein G) in aqueous solution. The accuracy of a TIGER2 simulation is particularly sensitive to the maximum temperature level selected for the simulation. A method to determine the appropriate maximum temperature level to be used in a TIGER2 simulation is presented. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011

[1]  H. Berendsen,et al.  ALGORITHMS FOR MACROMOLECULAR DYNAMICS AND CONSTRAINT DYNAMICS , 1977 .

[2]  Robert A Latour,et al.  TIGER2: an improved algorithm for temperature intervals with global exchange of replicas. , 2009, The Journal of chemical physics.

[3]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[4]  Joan-Emma Shea,et al.  Folding Landscapes of the Alzheimer Amyloid-β(12-28) Peptide , 2006 .

[5]  M. Karplus,et al.  Understanding beta-hairpin formation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[6]  B. Berne,et al.  The free energy landscape for β hairpin folding in explicit water , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Alexander D. MacKerell,et al.  Extending the treatment of backbone energetics in protein force fields: Limitations of gas‐phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations , 2004, J. Comput. Chem..

[8]  C. Brooks.  Computer simulation of liquids , 1989 .

[9]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[10]  M. Karplus,et al.  CHARMM: A program for macromolecular energy, minimization, and dynamics calculations , 1983 .

[11]  Michael W. Deem,et al.  Strict detailed balance is unnecessary in Monte Carlo simulation , 1999 .

[12]  Y. Sugita,et al.  Replica-exchange molecular dynamics method for protein folding , 1999 .

[13]  F. Young.  Biochemistry , 1955, The Indian Medical Gazette.

[14]  Nadeem A. Vellore,et al.  An improved replica-exchange sampling method: temperature intervals with global energy reassignment. , 2007, The Journal of chemical physics.

[15]  K. Sanbonmatsu,et al.  Exploring the energy landscape of a β hairpin in explicit solvent , 2001 .

[16]  Sergio A Hassan,et al.  Long dynamics simulations of proteins using atomistic force fields and a continuum representation of solvent effects: Calculation of structural and dynamic properties , 2005, Proteins.

[17]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[18]  Ruhong Zhou,et al.  Hydrophobic aided replica exchange: an efficient algorithm for protein folding in explicit solvent. , 2006, The journal of physical chemistry. B.

[19]  S. Hassan,et al.  A critical analysis of continuum electrostatics: The screened Coulomb potential–implicit solvent model and the study of the alanine dipeptide and discrimination of misfolded structures of proteins , 2002, Proteins.

[20]  A. Gronenborn,et al.  A novel, highly stable fold of the immunoglobulin binding domain of streptococcal protein G. , 1993, Science.

[21]  R. Levy,et al.  Protein folding pathways from replica exchange simulations and a kinetic network model. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[22]  R Nussinov,et al.  Molecular dynamics simulations of a beta-hairpin fragment of protein G: balance between side-chain and backbone forces. , 2000, Journal of molecular biology.

[23]  V S Pande,et al.  Molecular dynamics simulations of unfolding and refolding of a beta-hairpin fragment of protein G. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[24]  S. Hassan,et al.  A General Treatment of Solvent Effects Based on Screened Coulomb Potentials , 2000 .

[25]  B. Berne,et al.  Replica exchange with solute tempering: a method for sampling biological systems in explicit water. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  G L Gilliland,et al.  Two crystal structures of the B1 immunoglobulin-binding domain of streptococcal protein G and comparison with NMR. , 1994, Biochemistry.