Predicting Alpha Helical Transmembrane Proteins Using HMMs

Methods Mol Biol. 2017;1552:63-82. doi: 10.1007/978-1-4939-6753-7_5.

Abstract

Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for alpha helical transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane alpha helices in proteins and discriminating them from globular proteins.

Keywords: Algorithms; Alpha helical; Hidden Markov model; Membrane; Prediction; Protein; Transmembrane.

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Computer Simulation*
  • Databases, Protein
  • Humans
  • Markov Chains*
  • Membrane Proteins / chemistry*
  • Models, Molecular
  • Protein Conformation

Substances

  • Membrane Proteins