Kandathil, SM;
Greener, JG;
Jones, DT;
(2019)
Recent Developments in Deep Learning Applied to Protein Structure Prediction.
Proteins
10.1002/prot.25824.
(In press).
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Abstract
Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs have had a significant impact in recent CASP experiments, notably in CASP12 and especially CASP13. In this article, we offer a brief introduction to some of the key principles and properties of DNN models and discuss why they are naturally suited to certain problems in structural bioinformatics. We also briefly discuss methodological improvements that have enabled these successes. Using the contact prediction task as an example, we also speculate why DNN models are able to produce reasonably accurate predictions even in the absence of many homologues for a given target sequence, a result which can at first glance appear surprising given the lack of input information. We end on some thoughts about how and why these types of models can be so effective, as well as a discussion on potential pitfalls. This article is protected by copyright. All rights reserved.
Type: | Article |
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Title: | Recent Developments in Deep Learning Applied to Protein Structure Prediction |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1002/prot.25824 |
Publisher version: | https://doi.org/10.1002/prot.25824 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Deep Learning, protein structure prediction |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10083544 |
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