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CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites

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Abbasi, Wajid Arshad, Asiff, Amina, Andleeb, Saiqa and Minhas, Fayyaz ul Amir Afsar (2017) CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites. Proteins: Structure, Function, and Bioinformatics, 85 (9). pp. 1724-1740. doi:10.1002/prot.25330 ISSN 0887-3585.

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Official URL: http://dx.doi.org/10.1002/prot.25330

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Abstract

Due to Ca2+‐dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet‐lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet‐lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large‐margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM‐binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome‐wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif‐based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub‐sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QP Physiology
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Calmodulin, Protein binding, Protein binding -- Computer simulation
Journal or Publication Title: Proteins: Structure, Function, and Bioinformatics
Publisher: John Wiley & Sons Ltd.
ISSN: 0887-3585
Official Date: September 2017
Dates:
DateEvent
September 2017Published
9 June 2017Available
7 June 2017Accepted
Volume: 85
Number: 9
Page Range: pp. 1724-1740
DOI: 10.1002/prot.25330
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): "This is the peer reviewed version of the following article: Abbasi, WA, Asif, A, Andleeb, S, Minhas, FAA. CaMELS: In silico prediction of calmodulin binding proteins and their binding sites. Proteins. 2017; 85: 1724– 1740. which has been published in final form at http://dx.doi.org/10.1002/prot.25330. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions."
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 5 November 2019
Date of first compliant Open Access: 6 November 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
5000 Ph.D. fellowship schemeHigher Education Commission, Pakistanhttp://dx.doi.org/10.13039/501100004681

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