Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

By Sheng Wang, Siqi Sun, Z. Li, Renyu Zhang, Jinbo Xu. 2016

873 citations recorded by the source corpus.

Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not extremely useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can model contact occurring patterns and very complex sequence-structure relationship and thus, obtain high-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained by only non-membrane proteins, our deep learning method works very well on membrane protein contact prediction. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 5 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues and one α protein of 217 residues and one α/β of 260 residues. Availability: http://raptorx.uchicago.edu/ContactMap/ Author Summary Protein contact prediction and contact-assisted folding has made good progress due to direct evolutionary coupling analysis (DCA). However, DCA is effective on only some proteins with a very large number of sequence homologs. To further improve contact prediction, we borrow ideas from deep learning, which has recently revolutionized object recognition, speech recognition and the GO game. Our deep learning method can model complex sequence-structure relationship and high-order correlation (i.e., contact occurring patterns) and thus, improve contact prediction accuracy greatly. Our test results show that our method greatly outperforms the state-of-the-art methods regardless how many sequence homologs are available for a protein in question. Ab initio folding guided by our predicted contacts may fold many more test proteins than the other contact predictors. Our contact-assisted 3D models also have much better quality than homology models built from the training proteins, especially for membrane proteins. One interesting finding is that even trained with only soluble proteins, our method performs very well on membrane proteins. Recent blind test in CAMEO confirms that our method can fold large proteins with a new fold and only a small number of sequence homologs.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “This z 1 ∈ R 2 d 2 × L × L is then concatenated in the ﬁrst (feature map or channel) dimension, with the pairwise features z 2 ∈ R 6 × L × L i.e. the pairwise RaptorX features described in previous subsection and/or the msa embedding covariance features ( z 3 ∈ R 256 × L × L ) described in the next ”
Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[8] Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, and Jinbo Xu.”
High-resolution de novo structure prediction from primary sequence (2022, background): “…models, which has been demonstrated to be able to extract biologically meaningful representations for various tasks such as protein contact prediction ( 11–14 ), protein function prediction ( 15,16 ), zero-shot mutation effect prediction ( 17 ), and viral evolution and escape prediction ( 18, 19 ).”
Improved protein structure prediction using predicted interresidue orientations (2019, methodology): “Following RaptorX-Contact (4, 12) and AlphaFold (7), we learn probability distributions over distances, and extend this to orientation features.”
Generating functional protein variants with variational autoencoders (2020, background): “61 While there have recently been several successes in applying deep learning techniques to mod62 elling protein sequences in tasks including contact prediction [19], secondary structure prediction 63 [20, 21], and prediction of the fitness effects of mutations [22], the possibility of applying gene”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, background): “Rational design is oftentimes assisted by quantitative approaches to evaluate sequences that fold into a specific structure, including biophysical modeling [3,4] , force field approximations [5–7] , structure prediction [8–10] , and molecular dynamics simulations [11] .”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIII (2019, methodology): “These methods were already being tested in CASP12 and promising benchmarking has since been published.(16) But as is often”
DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, methodology): “We believe that our findings when combined with techniques such as using multiple distance thresholds [9,15], ensembling [3,9], predicting actual distances using binning [17], etc. used in other methods can significantly improve the overall contact prediction precision.”
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, background): “Since then, deep learning techniques have affected the application of machine learning in biology expanding the ability of prediction tools to produce more accurate results on complex datasets (11-18).”
DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, methodology): “lief networks [22] were developed to predict protein residue-residue contacts. Recently a deep residual convolutional neural network was designed to further improve the accuracy of contact prediction [23]. Deep learning methods have also been applied to predict protein secondary structures [24, 25] ”
End-to-end differentiable learning of protein structure (2018, background): “In contrast, use of deep learning for structure prediction has so far been restricted to individual components within a larger pipeline (Aydin et al., 2012; Gao et al., 2017; Li et al., 2017; Lyons et al., 2014), for example prediction of contact maps (Liu et al., 2017; Wang et al., 2016).”