Deep learning methods in protein structure prediction
|Title:||Deep learning methods in protein structure prediction||Authors:||Torrisi, Mirko; Pollastri, Gianluca; Le, Quan||Permanent link:||http://hdl.handle.net/10197/11583||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 learning; Protein structure prediction; Machine learning; Structural bioinformatics||DOI:||10.1016/j.csbj.2019.12.011||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.