How noise in force fields can affect the structural refinement of protein models?

Structural refinement of predicted models of biological macromolecules using atomistic or coarse‐grained molecular force fields having various degree of error is investigated. The goal of this analysis is to estimate what is the probability for designing an effective structural refinement based on computations of conformational energies using force field, and starting from a structure predicted from the sequence (using template‐based or template‐free modeling), and refining it to bring the structure into closer proximity to the native state. It is widely believed that it should be possible to develop such a successful structure refinement algorithm by applying an iterative procedure with stochastic sampling and appropriate energy function, which assesses the quality (correctness) of protein decoys. Here, an analysis of noise in an artificially introduced scoring function is investigated for a model of an ideal sampling scheme, where the underlying distribution of RMSDs is assumed to be Gaussian. Sampling of the conformational space is performed by random generation of RMSD values. We demonstrate that whenever the random noise in a force field exceeds some level, it is impossible to obtain reliable structural refinement. The magnitude of the noise, above which a structural refinement, on average is impossible, depends strongly on the quality of sampling scheme and a size of the protein. Finally, possible strategies to overcome the intrinsic limitations in the force fields for impacting the development of successful refinement algorithms are discussed. Proteins 2012. © 2011 Wiley Periodicals, Inc.

[1]  Alexei V. Finkelstein,et al.  3D Protein Folds: Homologs Against Errors-a Simple Estimate Based on the Random Energy Model , 1998 .

[2]  Adam Zemla,et al.  Critical assessment of methods of protein structure prediction (CASP)‐round V , 2005, Proteins.

[3]  P. Wolynes,et al.  Spin glasses and the statistical mechanics of protein folding. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

[4]  B. Rost.  Twilight zone of protein sequence alignments. , 1999, Protein engineering.

[5]  Torsten Schwede,et al.  Assessment of CASP7 predictions for template‐based modeling targets , 2007, Proteins.

[6]  David Baker,et al.  Prediction of the structure of symmetrical protein assemblies , 2007, Proceedings of the National Academy of Sciences.

[7]  Yang Zhang,et al.  I-TASSER server for protein 3D structure prediction , 2008, BMC Bioinformatics.

[8]  Yang Zhang,et al.  I-TASSER: a unified platform for automated protein structure and function prediction , 2010, Nature Protocols.

[9]  W. Kabsch.  A solution for the best rotation to relate two sets of vectors , 1976 .

[10]  Jens Meiler,et al.  Rosetta predictions in CASP5: Successes, failures, and prospects for complete automation , 2003, Proteins.

[11]  A. Tramontano,et al.  Critical assessment of methods of protein structure prediction (CASP)—round IX , 2011, Proteins.

[12]  Michal Brylinski,et al.  FINDSITE: a combined evolution/structure-based approach to protein function prediction , 2009, Briefings Bioinform..

[13]  Michael Feig,et al.  Sampling of near‐native protein conformations during protein structure refinement using a coarse‐grained model, normal modes, and molecular dynamics simulations , 2007, Proteins.

[14]  Michael Feig,et al.  A correlation‐based method for the enhancement of scoring functions on funnel‐shaped energy landscapes , 2006, Proteins.

[15]  Michal Brylinski,et al.  FINDSITELHM: A Threading-Based Approach to Ligand Homology Modeling , 2009, PLoS Comput. Biol..

[16]  J. Skolnick,et al.  What is the probability of a chance prediction of a protein structure with an rmsd of 6 A? , 1998, Folding & design.

[17]  K. Dill,et al.  Assessment of the protein‐structure refinement category in CASP8 , 2009, Proteins.

[18]  P. Wolynes,et al.  The energy landscapes and motions of proteins. , 1991, Science.

[19]  J. Skolnick,et al.  Assembly of protein structure from sparse experimental data: An efficient Monte Carlo model , 1998, Proteins.

[20]  C. Kiel,et al.  Structures in systems biology. , 2007, Current opinion in structural biology.

[21]  A. Liwo,et al.  Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[22]  A. Sali,et al.  Modeling of loops in protein structures , 2000, Protein science : a publication of the Protein Society.

[23]  A. Kolinski.  Protein modeling and structure prediction with a reduced representation. , 2004, Acta biochimica Polonica.