Item – Thèses Canada

Numéro d'OCLC
1013747522
Lien(s) vers le texte intégral
Exemplaire de BAC
Auteur
Becerra Romero, David,
Titre
Predicting protein folding pathways using ensemble modeling and sequence information
Diplôme
Ph. D. -- McGill University, 2017
Éditeur
[Montreal] : McGill University Libraries, [2017]
Description
1 online resource
Notes
Thesis supervisor: Jérôme Waldispuhl (Supervisor).
Includes bibliographical references.
Résumé
"The protein folding problem aims to predict the complete physical and dynamical process that transforms an unfolded sequence into a functional 3D protein structure. This problem consists of two (open) sub-problems: i) the protein structure prediction problem and ii) the protein pathway prediction problem. Computational techniques to face these two sub-problems have been based on the theory of evolution and laws of physics. To-date, classical approaches to obtaining detailed information about protein folding rely on time-consuming methods that are primarily limited to relatively small proteins (i.e., less than 50 amino acids). The overall objective of this thesis is to explore algorithms that conciliate: i) the prediction of protein structures and pathways, ii) physical-based predictions (i.e., low free-energy models) & evolutionary based predictions (i.e., sequence variation methods), and iii) computational costs and granularity level requirements of protein folding simulations. We propose an algorithmic framework for predicting protein folding that offers a better trade-off between resolution and efficiency. This framework computes accurate coarse-grained representations of the conformational landscape for large proteins through the combination of ensemble modeling techniques and evolutionary based sequence information. The resulting conformational energy landscape is then used to predict dominant folding pathways. Given that the proposed framework in this thesis makes use of sequence information, we also explore a crowdsourcing and multiobjective evolutionary strategy to investigate the accuracy of evolutionary information encoded by multiple sequence alignments. Finally, to present our results to the wider biology and computer science communities, we develop an easy-to-use interactive molecular visualizer."--
Autre lien(s)
digitool.Library.McGill.CA
escholarship.mcgill.ca
escholarship.mcgill.ca
Sujet
Computer Science