Deep learning methods in protein structure prediction

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Title: Deep learning methods in protein structure prediction
Authors: Torrisi, MirkoPollastri, GianlucaLe, Quan
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Date: 1-Jan-2020
Online since: 2020-09-22T14:29:07Z
Abstract: Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the ’60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions. We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.
Funding Details: Irish Research Council
University College Dublin
Type of material: Journal Article
Publisher: Elsevier
Journal: Computational and Structural Biotechnology Journal
Volume: 18
Start page: 1301
End page: 1310
Copyright (published version): 2020 the Authors
Keywords: Deep learningProtein structure predictionMachine learningStructural bioinformatics
DOI: 10.1016/j.csbj.2019.12.011
Language: en
Status of Item: Peer reviewed
Appears in Collections:Computer Science Research Collection

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