Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server.
KAUST Grant NumberKUK-I1-012-43
Online Publication Date2013-06-26
Print Publication Date2013-09-15
Permanent link to this recordhttp://hdl.handle.net/10754/596812
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AbstractMOTIVATION: Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode. RESULTS: In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen. AVAILABILITY: http://www.biocomputing.it/proABC. CONTACT: email@example.com or firstname.lastname@example.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
CitationOlimpieri PP, Chailyan A, Tramontano A, Marcatili P (2013) Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server. Bioinformatics 29: 2285–2291. Available: http://dx.doi.org/10.1093/bioinformatics/btt369.
SponsorsKAUST Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST), FIRB RBIN06E9Z8_005, PRIN 20108XYHJS and the Epigenomics Flagship Project -EPIGEN.
PublisherOxford University Press (OUP)
PubMed Central IDPMC3753563
CollectionsPublications Acknowledging KAUST Support
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