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      A Novel Side-Chain Orientation Dependent Potential Derived from Random-Walk Reference State for Protein Fold Selection and Structure Prediction

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      PLoS ONE
      Public Library of Science

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          Abstract

          Background

          An accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, however, which ignored the inherent sequence connectivity and entropic elasticity of proteins.

          Methodology

          We developed a new pair-wise distance-dependent, atomic statistical potential function (RW), using an ideal random-walk chain as reference state, which was optimized on CASP models and then benchmarked on nine structural decoy sets. Second, we incorporated a new side-chain orientation-dependent energy term into RW (RWplus) and found that the side-chain packing orientation specificity can further improve the decoy recognition ability of the statistical potential.

          Significance

          RW and RWplus demonstrate a significantly better ability than the best performing pair-wise distance-dependent atomic potential functions in both native and near-native model selections. It has higher energy-RMSD and energy-TM-score correlations compared with other potentials of the same type in real-life structure assembly decoys. When benchmarked with a comprehensive list of publicly available potentials, RW and RWplus shows comparable performance to the state-of-the-art scoring functions, including those combining terms from multiple resources. These data demonstrate the usefulness of random-walk chain as reference states which correctly account for sequence connectivity and entropic elasticity of proteins. It shows potential usefulness in structure recognition and protein folding simulations. The RW and RWplus potentials, as well as the newly generated I-TASSER decoys, are freely available in http://zhanglab.ccmb.med.umich.edu/RW.

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          Most cited references32

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          Principles that govern the folding of protein chains.

          C ANFINSEN (1973)
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            Reversible unfolding of individual titin immunoglobulin domains by AFM.

            Single-molecule atomic force microscopy (AFM) was used to investigate the mechanical properties of titin, the giant sarcomeric protein of striated muscle. Individual titin molecules were repeatedly stretched, and the applied force was recorded as a function of the elongation. At large extensions, the restoring force exhibited a sawtoothlike pattern, with a periodicity that varied between 25 and 28 nanometers. Measurements of recombinant titin immunoglobulin segments of two different lengths exhibited the same pattern and allowed attribution of the discontinuities to the unfolding of individual immunoglobulin domains. The forces required to unfold individual domains ranged from 150 to 300 piconewtons and depended on the pulling speed. Upon relaxation, refolding of immunoglobulin domains was observed.
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              Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.

              We explore the ability of a simple simulated annealing procedure to assemble native-like structures from fragments of unrelated protein structures with similar local sequences using Bayesian scoring functions. Environment and residue pair specific contributions to the scoring functions appear as the first two terms in a series expansion for the residue probability distributions in the protein database; the decoupling of the distance and environment dependencies of the distributions resolves the major problems with current database-derived scoring functions noted by Thomas and Dill. The simulated annealing procedure rapidly and frequently generates native-like structures for small helical proteins and better than random structures for small beta sheet containing proteins. Most of the simulated structures have native-like solvent accessibility and secondary structure patterns, and thus ensembles of these structures provide a particularly challenging set of decoys for evaluating scoring functions. We investigate the effects of multiple sequence information and different types of conformational constraints on the overall performance of the method, and the ability of a variety of recently developed scoring functions to recognize the native-like conformations in the ensembles of simulated structures.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2010
                27 October 2010
                : 5
                : 10
                Affiliations
                [1]Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
                Leeds Institute of Molecular Medicine, United Kingdom
                Author notes

                Conceived and designed the experiments: JZ YZ. Performed the experiments: JZ. Analyzed the data: JZ. Wrote the paper: JZ YZ.

                Article
                PONE-D-10-00260
                10.1371/journal.pone.0015386
                2965178
                21060880
                a7d9b266-cceb-4f61-b352-86bc762eb1d9
                Zhang and Zhang. 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.
                Page count
                Pages: 13
                Categories
                Research Article
                Biology
                Biochemistry
                Proteins
                Protein Structure
                Biophysics
                Protein Folding
                Computational Biology
                Genomics
                Structure Prediction
                Biophysic Al Simulations
                Computer Science
                Algorithms

                Uncategorized
                Uncategorized

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