Recent developments in deep learning applied to protein structure prediction
By Shaun M. Kandathil, Joe G. Greener, David T. Jones. 2019
53 citations recorded by the source corpus.
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 that 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.
Subfields: Computational Theory and Mathematics, Molecular Biology
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In Computational Theory and Mathematics
In Molecular Biology
- Quan Le
- Mirko Torrisi
- John A. Capra
- Mark Gerstein
- Gianluca Pollastri
- Yucheng T. Yang
- Bian Li
- Pierre Baldi
Citations in the corpus
- Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks (2020, background): “They have recently proven to perform well in residue contact prediction (57585 60) and protein tertiary structure prediction (61-64).”
- Deep learning methods in protein structure prediction (2020, background): “Progress in this field over the last few years has been substantial, even dramatic especially in the prediction of contact and distance maps [127,136], but the essential role of structural, evolutionary, and co-evolutionary information in this progress cannot be understated, with ab initio predictio”