Protocol for protein structure modelling

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Abstract

In silico protein structure prediction methods are assisting the field of structural biology by complementing expensive and laborious experimental methods. During the past few decades, improvements in computational techniques have achieved significant developments in protein structure prediction methods, of which template-based methods have the highest accuracy. This article presents a comprehensive protocol for modelling 3-dimensional (3D) structures of proteins from identified structural templates. The 3D model generated is also screened for functional motifs and patterns so that the selected structural model represents a functional protein. The article concludes by presenting the suite of available structure modelling tools and automated webservers for protein structure prediction to assist readers in elucidating the most appropriate choice for their purpose according to the problem scenario.
Original languageEnglish
Title of host publicationEncyclopedia of bioinformatics and computational biology
Subtitle of host publicationABC of Bioinformatics
EditorsMario Cannataro, Bruno Gaeta, Mohammad Asif Khan
Place of PublicationAmsterdam; Oxford; Cambridge
PublisherElsevier
Pages252-272
Number of pages21
Volume3
ISBN (Electronic)9780128114322
ISBN (Print)9780128114148
DOIs
Publication statusPublished - 2019

Bibliographical note

Editors-in-chief: Shoba Ranganathan, Kenta Nakai, Christian Schönbach, Michael Gribskov.

Keywords

  • Bioinformatics tools
  • Homology modelling
  • Motifs and patterns
  • Profile alignment
  • Protein structure predictions
  • Protein structure validation
  • Sequence analysis
  • Sequence structure alignment
  • Tertiary structure
  • Transmembrane helices

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    Jabeen, A., Mohamedali, A., & Ranganathan, S. (2019). Protocol for protein structure modelling. In M. Cannataro, B. Gaeta, & M. Asif Khan (Eds.), Encyclopedia of bioinformatics and computational biology: ABC of Bioinformatics (Vol. 3, pp. 252-272). Amsterdam; Oxford; Cambridge: Elsevier. https://doi.org/10.1016/B978-0-12-809633-8.20477-9