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      Rapid Sampling of Molecular Motions with Prior Information Constraints

      1 , 2 , 2 , 1 , * , 2 , *

      PLoS Computational Biology

      Public Library of Science

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          Abstract

          Proteins are active, flexible machines that perform a range of different functions. Innovative experimental approaches may now provide limited partial information about conformational changes along motion pathways of proteins. There is therefore a need for computational approaches that can efficiently incorporate prior information into motion prediction schemes. In this paper, we present PathRover, a general setup designed for the integration of prior information into the motion planning algorithm of rapidly exploring random trees (RRT). Each suggested motion pathway comprises a sequence of low-energy clash-free conformations that satisfy an arbitrary number of prior information constraints. These constraints can be derived from experimental data or from expert intuition about the motion. The incorporation of prior information is very straightforward and significantly narrows down the vast search in the typically high-dimensional conformational space, leading to dramatic reduction in running time. To allow the use of state-of-the-art energy functions and conformational sampling, we have integrated this framework into Rosetta, an accurate protocol for diverse types of structural modeling. The suggested framework can serve as an effective complementary tool for molecular dynamics, Normal Mode Analysis, and other prevalent techniques for predicting motion in proteins. We applied our framework to three different model systems. We show that a limited set of experimentally motivated constraints may effectively bias the simulations toward diverse predicates in an outright fashion, from distance constraints to enforcement of loop closure. In particular, our analysis sheds light on mechanisms of protein domain swapping and on the role of different residues in the motion.

          Author Summary

          Incorporating external knowledge into computational frameworks is a challenge of prime importance in many fields of biological research. In this study, we show how computational power can be harnessed to make use of limited external information and to more effectively simulate the molecular motion of proteins. While experimentally solved protein structures restrict our knowledge to static molecular “snapshots”, a vast number of proteins are flexible entities that constantly change shape. Protein motion is therefore intrinsically related to protein function. State-of-the-art experimental approaches are still limited in the information that they provide about protein motion. Therefore, we suggest here a very general computational framework that can take into account diverse external constraints and include experimental information or expert intuition. We explore in detail several biological systems of prime interest, including domain swapping and substrate binding, and show how limited partial information enhances the accuracy of predictions. Suggested motion pathways form detailed lab-testable hypotheses and can be of great interest to both experimentalists and theoreticians.

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          Most cited references 107

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          The Protein Data Bank.

          The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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            Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features.

             C. Sander,  W Kabsch (1983)
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              The Amber biomolecular simulation programs.

              We describe the development, current features, and some directions for future development of the Amber package of computer programs. This package evolved from a program that was constructed in the late 1970s to do Assisted Model Building with Energy Refinement, and now contains a group of programs embodying a number of powerful tools of modern computational chemistry, focused on molecular dynamics and free energy calculations of proteins, nucleic acids, and carbohydrates. (c) 2005 Wiley Periodicals, Inc.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                February 2009
                February 2009
                27 February 2009
                : 5
                : 2
                08-PLCB-RA-0446R2
                10.1371/journal.pcbi.1000295
                2637990
                19247429
                Raveh et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Counts
                Pages: 17
                Categories
                Research Article
                Computational Biology/Macromolecular Structure Analysis
                Computational Biology/Protein Structure Prediction
                Computer Science/Applications

                Quantitative & Systems biology

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