In Search of the protein Native State with a Probabilistic Sampling Approach

The three-dimensional structure of a protein is a key determinant of its biological function. Given the cost and time required to acquire this structure through experimental means, computational models are necessary to complement wet-lab efforts. Many computational techniques exist for navigating the high-dimensional protein conformational search space, which is explored for low-energy conformations that comprise a protein's native states. This work proposes two strategies to enhance the sampling of conformations near the native state. An enhanced fragment library with greater structural diversity is used to expand the search space in the context of fragment-based assembly. To manage the increased complexity of the search space, only a representative subset of the sampled conformations is retained to further guide the search towards the native state. Our results make the case that these two strategies greatly enhance the sampling of the conformational space near the native state. A detailed comparative analysis shows that our approach performs as well as state-of-the-art ab initio structure prediction protocols.

[1]  P. Bradley,et al.  Toward High-Resolution de Novo Structure Prediction for Small Proteins , 2005, Science.

[2]  Jooyoung Lee,et al.  New optimization method for conformational energy calculations on polypeptides: Conformational space annealing , 1997, J. Comput. Chem..

[3]  K. Dill,et al.  The Protein Folding Problem , 1993 .

[4]  Cecilia Clementi,et al.  Coarse-grained models of protein folding: toy models or predictive tools? , 2008, Current opinion in structural biology.

[5]  M. Levitt,et al.  Small libraries of protein fragments model native protein structures accurately. , 2002, Journal of molecular biology.

[6]  Cecilia Clementi,et al.  Unfolding the fold of cyclic cysteine‐rich peptides , 2008, Protein science : a publication of the Protein Society.

[7]  James E. Fitzgerald,et al.  Mimicking the folding pathway to improve homology-free protein structure prediction , 2009, Proceedings of the National Academy of Sciences.

[8]  David Hsu,et al.  Workspace-Based Connectivity Oracle: An Adaptive Sampling Strategy for PRM Planning , 2006, WAFR.

[9]  Lydia E Kavraki,et al.  From coarse‐grain to all‐atom: Toward multiscale analysis of protein landscapes , 2007, Proteins.

[10]  Lydia E. Kavraki,et al.  Discrete Search Leading Continuous Exploration for Kinodynamic Motion Planning , 2007, Robotics: Science and Systems.

[11]  Oliver Brock,et al.  Guiding conformation space search with an all‐atom energy potential , 2008, Proteins.

[12]  W. Graham Richards,et al.  Ultrafast shape recognition to search compound databases for similar molecular shapes , 2007, J. Comput. Chem..

[13]  Shuangye Yin,et al.  Eris: an automated estimator of protein stability , 2007, Nature Methods.

[14]  Tanja Kortemme,et al.  Computational design of protein-protein interactions. , 2004, Current opinion in chemical biology.

[15]  Amarda Shehu,et al.  Guiding the Search for Native-like Protein Conformations with an Ab-initio Tree-based Exploration , 2010, Int. J. Robotics Res..

[16]  Ruth Nussinov,et al.  fragment folding and assembly Reducing the computational complexity of protein folding via , 2002 .

[17]  K. Dill,et al.  From Levinthal to pathways to funnels , 1997, Nature Structural Biology.

[18]  Amarda Shehu,et al.  An Ab-initio tree-based exploration to enhance sampling of low-energy protein conformations , 2009, Robotics: Science and Systems.

[19]  C. Anfinsen.  Principles that govern the folding of protein chains. , 1973, Science.

[20]  Adrian A Canutescu,et al.  Access the most recent version at doi: 10.1110/ps.03154503 References , 2003 .

[21]  Oliver Brock,et al.  Efficient Motion Planning Based on Disassembly , 2005, Robotics: Science and Systems.

[22]  Lydia E. Kavraki,et al.  Motion Planning in the Presence of Drift, Underactuation and Discrete System Changes , 2005, Robotics: Science and Systems.

[23]  A. R. Fresht.  Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding , 1999 .

[24]  Wolfram Burgard,et al.  Robotics: Science and Systems XV , 2010 .

[25]  L. Kavraki,et al.  Multiscale characterization of protein conformational ensembles , 2009, Proteins.

[26]  D T Jones,et al.  Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.

[27]  Howie Choset,et al.  Principles of Robot Motion: Theory, Algorithms, and Implementation ERRATA!!!! 1 , 2007 .

[28]  Jean-Claude Latombe,et al.  On Delaying Collision Checking in PRM Planning: Application to Multi-Robot Coordination , 2002, Int. J. Robotics Res..

[29]  Richard Bonneau,et al.  De novo prediction of three-dimensional structures for major protein families. , 2002, Journal of molecular biology.

[30]  Gaetano T. Montelione,et al.  3.11 News & Views 031 CDS , 2005 .

[31]  Mark H. Overmars,et al.  Using Workspace Information as a Guide to Non-uniform Sampling in Probabilistic Roadmap Planners , 2005, Int. J. Robotics Res..

[32]  Nancy M. Amato,et al.  RESAMPL: A Region-Sensitive Adaptive Motion Planner , 2008, WAFR.

[33]  D. Baker,et al.  Coupled prediction of protein secondary and tertiary structure , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Amarda Shehu,et al.  Enhancing Sampling of the Conformational Space Near the Protein Native State , 2010, BIONETICS.

[35]  James J. Kuffner,et al.  Planning Among Movable Obstacles with Artificial Constraints , 2008, Int. J. Robotics Res..

[36]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

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

[38]  Guoli Wang,et al.  PISCES: a protein sequence culling server , 2003, Bioinform..