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SH2 Domains pp 47–58Cite as

Hidden Markov Models for Protein Domain Homology Identification and Analysis

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1555))

Abstract

Protein domain identification and analysis are cornerstones of modern proteomics. The tools available to protein domain researchers avail a variety of approaches to understanding large protein domain families. Hidden Markov Models (HMM) form the basis for identifying and categorizing evolutionarily linked protein domains. Here I describe the use of HMM models for predicting and identifying Src Homology 2 (SH2) domains within the proteome.

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Acknowledgments

The knowledge amassed to write this chapter was based on work supported by the University of Chicago Cancer Research Foundation Women’s Board and Piers Nash’s laboratory at the University of Chicago Ben May Department for Cancer Research.

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Authors and Affiliations

  1. Division of Emergency Medicine, Department of Medicine, University of Washington, 325 9th Ave., Seattle, WA, USA

    Karl Jablonowski

  2. Division of Biomedical and Health Informatics, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA

    Karl Jablonowski

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  1. Karl Jablonowski
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Correspondence to Karl Jablonowski .

Editor information

Editors and Affiliations

  1. Department of Genetics and Genome Sciences, University of Connecticut School of Medicine, Farmington, Connecticut, USA

    Kazuya Machida

  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Bernard A. Liu

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Jablonowski, K. (2017). Hidden Markov Models for Protein Domain Homology Identification and Analysis. In: Machida, K., Liu, B. (eds) SH2 Domains. Methods in Molecular Biology, vol 1555. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6762-9_3

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  • DOI: https://doi.org/10.1007/978-1-4939-6762-9_3

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6760-5

  • Online ISBN: 978-1-4939-6762-9

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