skip to main content
10.1145/2147805.2147855acmconferencesArticle/Chapter ViewAbstractPublication PagesbcbConference Proceedingsconference-collections
short-paper

Kernel methods for Calmodulin binding and binding site prediction

Published: 01 August 2011 Publication History

Abstract

Calmodulin (CaM) is a calcium-binding protein that is involved in a variety of cellular processes, interacting with many proteins. Since many CaM interactions are calcium-dependent, they are difficult to detect using high-throughput methods like yeast-two-hybrid. Furthermore, detection of CaM binding sites requires a significant experimental effort. Using a collection of CaM binding sites extracted from the Calmodulin Target Database we trained SVM-based classifiers to detect CaM binding sites using a variety of sequence features; our best classifier achieved an area under the ROC curve of 0.89 for detecting binding site locations at the amino acid level. We apply our classifiers to the problem of detecting CaM binding proteins in Arabidopsis; at a false-positive level of 0.05 we detected 638 novel putative CaM binding proteins. These proteins share overrepresented Gene Ontology terms associated with the functions of known CaM binders.

References

[1]
S. F. Altschul et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research, 25(17):3389--3402, September 1997.
[2]
A. Ben-Hur. PyML - machine learning in Python, 2009. Software available at http://pyml.sourceforge.net/http://pyml.sourceforge.net/.
[3]
A. Ben-Hur and W. Noble. Kernel methods for predicting protein--protein interactions. Bioinformatics, 21(suppl 1):i38, 2005.
[4]
A. Ben-Hur, C. Ong, S. Sonnenburg, B. Schölkopf, and G. Rätsch. Support vector machines and kernels for computational biology. PLoS Computational Biology, 4(10), 2008.
[5]
N. Bouche, A. Yellin, W. Snedden, and H. Fromm. Plant-specific calmodulin-binding proteins. Annual Review of Plant Biology, 56(1):435--466, 2005.
[6]
C. Cortes and V. Vapnik. Support-vector networks. In Machine Learning, pages 273--297, 1995.
[7]
T. G. Dietterich. Machine learning for sequential data: A review. In Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, pages 15--30, London, UK, 2002. Springer-Verlag.
[8]
C.-S. Goh et al. Co-evolution of proteins with their interaction partners. Journal of Molecular Biology, 299(2):283--293, 2000.
[9]
M. A. Hibbs, C. L. Myers, C. Huttenhower, D. C. Hess, K. Li, A. A. Caudy, and O. G. Troyanskaya. Directing experimental biology: A case study in mitochondrial biogenesis. PLoS Comput Biol, 5(3):e1000322, 03 2009.
[10]
S. Kawashima, H. Ogata, and M. Kanehisa. AAindex: Amino acid index database. Nucleic Acids Research, 27(1):368--369, 1999.
[11]
C. S. Leslie et al. The spectrum kernel: a string kernel for SVM protein classification. In Pacific Symposium on Biocomputing, pages 566--575, 2002.
[12]
Y. Ofran and B. Rost. Isis: interaction sites identified from sequence. Bioinformatics, 23(2):e13--e16, 2007.
[13]
K. T. O'Neil and W. F. DeGrado. How calmodulin binds its targets: sequence independent recognition of amphiphilic alpha-helices. Trends in Biochemical Sciences, 15(2):59--64, 1990.
[14]
S. C. Popescu et al. Differential binding of calmodulin-related proteins to their targets revealed through high-density Arabidopsis protein microarrays. PNAS, 104(11):4730--4735, March 2007.
[15]
P. Radivojac et al. Calmodulin signaling: analysis and prediction of a disorder-dependent molecular recognition. Proteins: Structure, Function, and Bioinformatics, 63(2):398--410, 2006.
[16]
A. Reddy. Calcium: silver bullet in signaling. Plant Sci, 160(3):381--404, 2001.
[17]
A. Reddy, A. Ben-Hur, and I. S. Day. Experimental and computational approaches for the study of calmodulin interactions. Phytochemistry, 2011.
[18]
A. R. Rhoads and F. Friedberg. Sequence motifs for calmodulin recognition. The FASEB Journal: Official Publication of the Federation of American Societies for Experimental Biology, 11(5):331--340, April 1997.
[19]
B. E. Suzek et al. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics, 23(10):1282--1288, 2007.
[20]
H. Wang et al. Insite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale. Genome Biology, 8(9):R192, 2007.
[21]
K. L. Yap et al. Calmodulin target database. Journal of Structural and Functional Genomics, 1(1):8--14, March 2000.
[22]
H. Zhou and S. Qin. Interaction-site prediction for protein complexes: a critical assessment. Bioinformatics, 23(17):2203--2209, June 2007.

Cited By

View all
  • (2019)Flexible k-mers with variable-length indels for identifying binding sequences of protein dimersBriefings in Bioinformatics10.1093/bib/bbz101Online publication date: 5-Nov-2019
  • (2012)Multiple instance learning of Calmodulin binding sitesBioinformatics10.1093/bioinformatics/bts41628:18(i416-i422)Online publication date: 3-Sep-2012

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
BCB '11: Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
August 2011
688 pages
ISBN:9781450307963
DOI:10.1145/2147805
  • General Chairs:
  • Robert Grossman,
  • Andrey Rzhetsky,
  • Program Chairs:
  • Sun Kim,
  • Wei Wang
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 August 2011

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Short-paper

Conference

BCB' 11
Sponsor:

Acceptance Rates

Overall Acceptance Rate 254 of 885 submissions, 29%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Flexible k-mers with variable-length indels for identifying binding sequences of protein dimersBriefings in Bioinformatics10.1093/bib/bbz101Online publication date: 5-Nov-2019
  • (2012)Multiple instance learning of Calmodulin binding sitesBioinformatics10.1093/bioinformatics/bts41628:18(i416-i422)Online publication date: 3-Sep-2012

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media