Chen, Brian

Haspel, Nurit

He, Jing

Shehu, Amarda

English

Old Dominion University

Old Dominion UniversityODU Digital CommonsComputer Science Faculty Publications Computer Science2013The 6th Computational Structural BioinformaticsWorkshop IntroductionJing HeOld Dominion UniversityAmarda ShehuNurit HaspelBrian ChenFollow this and additional works at: https://digitalcommons.odu.edu/computerscience_fac_pubsPart of the Computer Sciences CommonsThis Article is brought to you for free and open access by the Computer Science at ODU Digital Commons. It has been accepted for inclusion inComputer Science Faculty Publications by an authorized administrator of ODU Digital Commons. For more information, please contactdigitalcommons@odu.edu.Repository CitationHe, Jing; Shehu, Amarda; Haspel, Nurit; and Chen, Brian, "The 6th Computational Structural Bioinformatics Workshop Introduction"(2013). Computer Science Faculty Publications. 37.https://digitalcommons.odu.edu/computerscience_fac_pubs/37Original Publication CitationHe, J., Shehu, A., Haspel, N., & Chen, B. (2013). The 6th Computational Structural Bioinformatics Workshop Introduction. BMCStructural Biology, 13. doi: 10.1186/1472-6807-13-s1-iINTRODUCTION Open AccessThe 6th Computational Structural BioinformaticsWorkshopJing He1*, Amarda Shehu2, Nurit Haspel3, Brian Chen4From Computational Structural Bioinformatics Workshop 2012Philadelphia, PA, USA. 4 October 2012The rapid accumulation of macromolecular structurespresents a unique set of challenges and opportunities inthe analysis, comparison, modeling, and prediction ofmacromolecular structures and interactions. The 6thComputational Structural Bioinformatics Workshop(CSBW) was held in Philadelphia on October 4, 2012.This issue includes eleven papers selected from the workpresented at the CSBW of 2012. In “Four-body atomicpotential for modeling protein-ligand binding affinity:application to enzyme-inhibitor binding energy predic-tion”, a predictive model of free-energy was built to eval-uate the binding between a protein and a ligand. In“Unbiased, scalable sampling of protein loop conforma-tions from probabilistic priors”, a new Markov chainMonte Carlo algorithm was proposed to generateunbiased conformations of closed protein loops fromprobabilistic priors. In “Enhancement of accuracy andefficiency for RNA secondary structure prediction bysequence segmentation and MapReduce”, authorsdemonstrate an enhanced method by cutting a long RNAsequence into smaller chunks at strategically selectedpoints, and distributing the tasks to multiple processors.In “A population-based evolutionary search approach tothe multiple minima problem in de novo protein struc-ture prediction”, the authors present an evolutionarysearch algorithm to obtain a discrete representation ofthe protein energy surface in terms of an ensemble ofconformations representing local energy minima. In“Estimating loop length from Cryo-EM images at med-ium resolutions”, the authors developed a computationalgeometry method to simplify the points along the skele-ton to measure loop length in 3D images. The paper“A conservation and rigidity based method for detectingcritical protein residues” presents a method thatcombines the rigidity and the evolutionary conservationin detection of the critical residues. In “A conservationand biophysics guided stochastic approach to refiningdocked multimeric proteins”, the authors introduce arefinement method that accepts complexes consisting ofany number of monomeric units. In “Elucidating theensemble of functionally-relevant transitions in proteinsystems with a robotics-inspired method,” the authorspresent a robotics-inspired tree-based method to sampleenergetically-credible conformational pathways connect-ing diverse functional states in multimodal proteins.The paper “An aggregate analysis of many predictedstructures to reduce errors in protein structure compari-son caused by conformational flexibility” applies proteinstructure prediction algorithms to enhance the classifica-tion of homologous proteins according to their bindingpreferences. In “DINC: A new AutoDock-based protocolfor docking large ligands”, an enhanced method wasdemonstrated for docking large ligands. The paper“Modeling protein conformational transitions by acombination of coarse-grained normal mode analysisand robotics-inspired methods” presents an efficientapproach involving robotics concepts.Competing interestsThe authors declare that they have no competing interests.AcknowledgementsWe would like to thank all the members in the workshop steeringcommittee and the program committee for their continuing support. Wethank the reviewers for their helpful comments.This article has been published as part of BMC Structural Biology Volume 13Supplement 1, 2013: Selected articles from the Computational StructuralBioinformatics Workshop 2012. The full contents of the supplement areavailable online at http://www.biomedcentral.com/bmcstructbiol/supplements/13/S1.Authors’ details1Department of Computer Science, Old Dominion University, Norfolk, VA23529, USA. 2Department of Computer Science, George Mason University,Fairfax, VA, 22030, USA. 3Department of Computer Science, University of1Department of Computer Science, Old Dominion University, Norfolk, VA23529, USAFull list of author information is available at the end of the articleHe et al. BMC Structural Biology 2013, 13(Suppl 1):I1http://www.biomedcentral.com/1472-6807/13/S1/I1© 2013 He et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Massachusetts, Boston, MA, 02125, USA. 4Department of Computer Scienceand Engineering, Lehigh University, Bethlehem, PA, 18015, USA.Published: 8 November 2013doi:10.1186/1472-6807-13-S1-I1Cite this article as: He et al.: The 6th Computational StructuralBioinformatics Workshop. BMC Structural Biology 2013 13(Suppl 1):I1.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submitHe et al. BMC Structural Biology 2013, 13(Suppl 1):I1http://www.biomedcentral.com/1472-6807/13/S1/I1Page 2 of 2

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