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Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

  • Christopher J. Oldfield
  • Ke Chen
  • Lukasz KurganEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1958)

Abstract

Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as β hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.

Key words

Secondary structure prediction Supersecondary structure prediction Beta hairpins Coiled coils Helix-turn-helix Greek key Multiple sequence alignment 

Notes

Acknowledgments

This work was supported by the Qimonda Endowment funds to L.K.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Christopher J. Oldfield
    • 1
  • Ke Chen
    • 2
  • Lukasz Kurgan
    • 1
    Email author
  1. 1.Department of Computer Science, College of EngineeringVirginia Commonwealth UniversityRichmondUSA
  2. 2.School of Computer Science and Software EngineeringTianjin Polytechnic UniversityTianjinPeople’s Republic of China

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