Text Mining for Protein Docking

Abstract

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground ( http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate.

Author Summary

Protein interactions are central for many cellular processes. Physical characterization of these interactions is essential for understanding of life processes and applications in biology and medicine. Because of the inherent limitations of experimental techniques and rapid development of computational power and methodology, computer modeling is a tool of choice in many studies. Publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for modeling of proteins and protein complexes. A major paradigm shift in modeling of protein complexes is emerging due to the rapidly expanding amount of such information, which can be used as modeling constraints. Text mining has been widely used in recreating networks of protein interactions, as well as in detecting small molecule binding sites on proteins. Combining and expanding these two well-developed areas of research, we applied the text mining to physical modeling of protein complexes (protein docking). Our procedure retrieves published abstracts on a protein-protein interaction and extracts the relevant information. The results show that correct information on binding can be obtained for about half of protein complexes. The extracted constraints were incorporated in a modeling procedure, significantly improving its performance.

Contributors

Nir Ben-Tal: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Comput Biol

Journal ID (iso-abbrev): PLoS Comput. Biol

Journal ID (publisher-id): plos

Journal ID (pmc): ploscomp

Title: PLoS Computational Biology

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1553-734X

ISSN (Electronic): 1553-7358

Publication date (Electronic): 9 December 2015

Publication date Collection: December 2015

Volume: 11

Issue: 12

Affiliations

[1 ]Center for Computational Biology, The University of Kansas, Lawrence, Kansas, United States of America

[2 ]Department of Molecular Biosciences, The University of Kansas, Lawrence, Kansas, United States of America

Tel Aviv University, ISRAEL

Author notes

The authors have declared that no competing interests exist.

Conceived and designed the experiments: VDB PJK IAV. Performed the experiments: VDB. Analyzed the data: VDB PJK IAV. Contributed reagents/materials/analysis tools: VDB PJK. Wrote the paper: VDB PJK IAV.

* E-mail: vakser@ 123456ku.edu (IAV); pkundro@ 123456ku.edu (PJK)

Article

Publisher ID: PCOMPBIOL-D-15-00921

DOI: 10.1371/journal.pcbi.1004630

PMC ID: 4674139

PubMed ID: 26650466

Copyright statement: © 2015 Badal et al

License:

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

Figures: 6, Tables: 4, Pages: 21

Product

Funding

This study was supported by NIH grant R01GM074255 and NSF grant DBI1262621. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Categories

Subject: Research Article

Custom metadata

Data Availability The paper contains a description of how to generate the raw data. The raw data is also available on request from the corresponding author ( vakser@ 123456ku.edu ).