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Review
. 2020 Jan 22:18:1301-1310.
doi: 10.1016/j.csbj.2019.12.011. eCollection 2020.

Deep learning methods in protein structure prediction

Mirko Torrisi  1 , Gianluca Pollastri  1 , Quan Le  2
Affiliations
Review

Deep learning methods in protein structure prediction

Mirko Torrisi et al. Comput Struct Biotechnol J. .

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.

Keywords: Deep learning; Machine learning; Protein structure prediction.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
A generic pipeline for ab initio Protein Structure Prediction, in which evolutionary information in the form of alignments, 1D and 2D PSA are intermediate steps.
Fig. 2
Fig. 2
Growth of known structures in the Protein Data Bank (left) and known sequences in Uniprot (right). The y-axis is shown in logarithmic scale for the Uniprot.
Fig. 3
Fig. 3
Performances of secondary structure predictors over the years. “stat” are predictors based on statistical methods other than Neural Networks. “ML” are predictors based on shallow Neural Networks or Support Vector Machines. “DL-CNN” are Deep Learning methods based on Convolutional Neural Networks. “DL-RNN” are Deep Learning methods based on Recurrent Neural Networks. Data extracted from accompanying publications of predictors referenced in this article.
Fig. 4
Fig. 4
Improvements in quality of 3D predictions for free modelling (ab initio) targets between CASP9 and CASP13.
See this image and copyright information in PMC

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