The Library
CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites
Tools
Tools
Tools
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.
|
PDF
WRAP-CaMELS-silico-calmodulin-binding-proteins-Minhas-2017.pdf - Accepted Version - Requires a PDF viewer. Download (1971Kb) | Preview |
Official URL: http://dx.doi.org/10.1002/prot.25330
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: |
|
||||||||
| 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: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
