Hidden Markov Models for Protein Domain Homology Identification and Analysis

Methods Mol Biol. 2017:1555:47-58. doi: 10.1007/978-1-4939-6762-9_3.

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.

Keywords: Hidden Markov model; Neighbor-joining phylogenetic tree; SH2 domains; Sequence alignment.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence
  • Computational Biology / methods
  • Databases, Protein
  • Humans
  • Markov Chains*
  • Phylogeny
  • Protein Domains*
  • Protein Interaction Domains and Motifs*
  • Proteins / chemistry*
  • Proteins / classification
  • Proteins / genetics
  • Proteins / metabolism*
  • Sequence Alignment
  • Sequence Homology, Amino Acid*
  • Software
  • src Homology Domains

Substances

  • Proteins