Using Computational Intelligence Models for Additional Insight into Protein Structure

  • Maria Iuliana Bocicor Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
  • Alessandro Pandini Department of Computer Science, Brunel University, London, England
  • Gabriela Czibula Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
  • Silvana Albert Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania
  • Mihai Teletin Department of Computer Science, Faculty of Mathematics and Computer Science, Babeș-Bolyai University, Cluj-Napoca, Romania

Abstract

Proteins are large, complex molecules with crucial roles in the functioning of living organisms. Understanding the underlying mechanisms by which proteins achieve their structures and substructures, as well as those involved in the conformational transitions may contribute to a deeper comprehension of the involved biological processes. This paper investigates a new machine learning perspective upon analyzing protein conformational transitions and introduces a new formalization for the problem, with the more general goal of uncovering interesting patterns in protein conformational transitions. This study represents the starting point of a research which is being conducted in order to obtain a better comprehension of proteins’ structures and, implicitly, functions, by investigating computational intelligence methods for analyzing and deducing proteins conformational transitions.

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Published
2017-05-30
How to Cite
BOCICOR, Maria Iuliana et al. Using Computational Intelligence Models for Additional Insight into Protein Structure. Studia Universitatis Babeș-Bolyai Informatica, [S.l.], v. 62, n. 1, p. 107-120, may 2017. ISSN 2065-9601. Available at: <http://www.cs.ubbcluj.ro/~studia-i/journal/journal/article/view/9>. Date accessed: 29 nov. 2020. doi: https://doi.org/10.24193/subbi.2017.1.08.
Section
Articles