Publikationsdatum:
2012-08-08
Beschreibung:
Motivation: Determination of the binding affinity of a protein–ligand complex is important to quantitatively specify whether a particular small molecule will bind to the target protein. Besides, collection of comprehensive datasets for protein–ligand complexes and their corresponding binding affinities is crucial in developing accurate scoring functions for the prediction of the binding affinities of previously unknown protein–ligand complexes. In the past decades, several databases of protein–ligand-binding affinities have been created via visual extraction from literature. However, such approaches are time-consuming and most of these databases are updated only a few times per year. Hence, there is an immediate demand for an automatic extraction method with high precision for binding affinity collection. Result: We have created a new database of protein–ligand-binding affinity data, AutoBind, based on automatic information retrieval. We first compiled a collection of 1586 articles where the binding affinities have been marked manually. Based on this annotated collection, we designed four sentence patterns that are used to scan full-text articles as well as a scoring function to rank the sentences that match our patterns. The proposed sentence patterns can effectively identify the binding affinities in full-text articles. Our assessment shows that AutoBind achieved 84.22% precision and 79.07% recall on the testing corpus. Currently, 13 616 protein–ligand complexes and the corresponding binding affinities have been deposited in AutoBind from 17 221 articles. Availability: AutoBind is automatically updated on a monthly basis, and it is freely available at http://autobind.csie.ncku.edu.tw/ and http://autobind.mc.ntu.edu.tw/ . All of the deposited binding affinities have been refined and approved manually before being released. Contact: jchiang@mail.ncku.edu.tw Supplementary Information: Supplementary data are available at Bioinformatics online.
Print ISSN:
1367-4803
Digitale ISSN:
1460-2059
Thema:
Biologie
,
Informatik
,
Medizin
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