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dc.contributor.authorBadal, Varsha D.
dc.contributor.authorKundrotas, Petras J.
dc.contributor.authorVakser, Ilya A.
dc.date.accessioned2018-06-13T17:47:34Z
dc.date.available2018-06-13T17:47:34Z
dc.date.issued2018-03-05
dc.identifier.citationBadal, V. D., Kundrotas, P. J., & Vakser, I. A. (2018). Natural language processing in text mining for structural modeling of protein complexes. BMC Bioinformatics, 19, 84. http://doi.org/10.1186/s12859-018-2079-4en_US
dc.identifier.urihttp://hdl.handle.net/1808/26505
dc.description.abstractBACKGROUND: Structural modeling of protein-protein interactions produces a large number of putative configurations of the protein complexes. Identification of the near-native models among them is a serious challenge. Publicly available results of biomedical research may provide constraints on the binding mode, which can be essential for the docking. Our text-mining (TM) tool, which extracts binding site residues from the PubMed abstracts, was successfully applied to protein docking (Badal et al., PLoS Comput Biol, 2015; 11: e1004630). Still, many extracted residues were not relevant to the docking. RESULTS: We present an extension of the TM tool, which utilizes natural language processing (NLP) for analyzing the context of the residue occurrence. The procedure was tested using generic and specialized dictionaries. The results showed that the keyword dictionaries designed for identification of protein interactions are not adequate for the TM prediction of the binding mode. However, our dictionary designed to distinguish keywords relevant to the protein binding sites led to considerable improvement in the TM performance. We investigated the utility of several methods of context analysis, based on dissection of the sentence parse trees. The machine learning-based NLP filtered the pool of the mined residues significantly more efficiently than the rule-based NLP. Constraints generated by NLP were tested in docking of unbound proteins from the DOCKGROUND X-ray benchmark set 4. The output of the global low-resolution docking scan was post-processed, separately, by constraints from the basic TM, constraints re-ranked by NLP, and the reference constraints. The quality of a match was assessed by the interface root-mean-square deviation. The results showed significant improvement of the docking output when using the constraints generated by the advanced TM with NLP. CONCLUSIONS: The basic TM procedure for extracting protein-protein binding site residues from the PubMed abstracts was significantly advanced by the deep parsing (NLP techniques for contextual analysis) in purging of the initial pool of the extracted residues. Benchmarking showed a substantial increase of the docking success rate based on the constraints generated by the advanced TM with NLPen_US
dc.publisherBioMed Centralen_US
dc.rightsCopyright © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectProtein interactionsen_US
dc.subjectBinding site predictionen_US
dc.subjectProtein dockingen_US
dc.subjectDependency parseren_US
dc.subjectRule-based systemen_US
dc.subjectSupervised learningen_US
dc.titleNatural language processing in text mining for structural modeling of protein complexesen_US
dc.typeArticleen_US
kusw.kuauthorBadal, Varsha D.
kusw.kuauthorKundrotas, Petras J.
kusw.kuauthorVakser, Ilya A.
kusw.kudepartmentCenter for Computational Biologyen_US
kusw.oanotesPer SHERPA/RoMEO 6/13/2018: Author's Pre-print: green tick author can archive pre-print (ie pre-refereeing) Author's Post-print: green tick author can archive post-print (ie final draft post-refereeing) Publisher's Version/PDF: green tick author can archive publisher's version/PDF General Conditions:

On any website Publisher's version/PDF may be used Eligible UK authors may deposit in OpenDepot Creative Commons Attribution License Copy of License must accompany any deposit.
en_US
dc.identifier.doi10.1186/s12859-018-2079-4en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.rights.accessrightsopenAccessen_US


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Copyright © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
Except where otherwise noted, this item's license is described as: Copyright © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.