Antibody structure prediction using interpretable deep learning

Antibody engineering is becoming increasingly popular in the medical field for the development of diagnostics and immunotherapies. Antibody function relies largely on the recognition and binding of antigenic epitopes via the loops in the complementarity determining regions. Hence, accurate high-resolution modeling of these loops is essential for effective antibody engineering and design. Deep learning methods have previously been shown to effectively predict antibody backbone structures described as a set of inter-residue distances and orientations. However, antigen binding is also dependent on the specific conformations of surface side chains. To address this shortcoming, we created DeepSCAb: a deep learning method that predicts inter-residue geometries as well as side chain dihedrals of the antibody variable fragment. The network requires only sequence as input, rendering our method particularly useful for antibodies without any known backbone conformations. Rotamer predictions use an interpretable self-attention layer, which learns to identify structurally conserved anchor positions across several species. We evaluate the performance of our model for discriminating near-native structures from sets of decoys and find that DeepSCAb outperforms similar methods lacking side chain context. When compared to alternative rotamer repacking methods, which require an input backbone structure, DeepSCAb predicts side chain conformations competitively. Our findings suggest that DeepSCAb improves antibody structure prediction with accurate side chain modeling and is adaptable to applications in docking of antibody-antigen complexes and design of new therapeutic antibody sequences.

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Crystal Structure Prediction via Deep Learning

Journal of the American Chemical Society ◽

10.1021/jacs.8b03913 ◽

2018 ◽

Vol 140 (32) ◽

pp. 10158-10168 ◽

Cited By ~ 86

Author(s):

Kevin Ryan ◽

Jeff Lengyel ◽

Michael Shatruk

Keyword(s):

Crystal Structure ◽

Deep Learning ◽

Structure Prediction ◽

Crystal Structure Prediction

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Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.26194 ◽

2021 ◽

Author(s):

Ivan Anishchenko ◽

Minkyung Baek ◽

Hahnbeom Park ◽

Naozumi Hiranuma ◽

David E. Kim ◽

...

Keyword(s):

Deep Learning ◽

Structure Prediction ◽

Tertiary Structure ◽

Tertiary Structure Prediction ◽

Protein Tertiary Structure ◽

Protein Tertiary Structure Prediction

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Deep learning techniques have significantly impacted protein structure prediction and protein design

Current Opinion in Structural Biology ◽

10.1016/j.sbi.2021.01.007 ◽

2021 ◽

Vol 68 ◽

pp. 194-207

Author(s):

Robin Pearce ◽

Yang Zhang

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Protein Design ◽

Structure Prediction ◽

Learning Techniques

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Template-based prediction of protein structure with deep learning

BMC Genomics ◽

10.1186/s12864-020-07249-8 ◽

2020 ◽

Vol 21 (S11) ◽

Author(s):

Haicang Zhang ◽

Yufeng Shen

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Structure Prediction ◽

Tertiary Structure ◽

Query Sequence ◽

Dynamic Programming Algorithm ◽

Tertiary Structure Prediction ◽

Protein Tertiary Structure ◽

Protein Threading ◽

Protein Tertiary Structure Prediction

Abstract Background Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. Results We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively. Conclusions These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

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Improved protein structure prediction by deep learning irrespective of co-evolution information

Nature Machine Intelligence ◽

10.1038/s42256-021-00348-5 ◽

2021 ◽

Author(s):

Jinbo Xu ◽

Matthew McPartlon ◽

Jin Li

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction

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RNA secondary structure prediction using deep learning with thermodynamic integration

10.1101/2020.08.10.244442 ◽

2020 ◽

Author(s):

Kengo Sato ◽

Manato Akiyama ◽

Yasubumi Sakakibara

Keyword(s):

Deep Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Secondary Structure Prediction ◽

Secondary Structures ◽

Thermodynamic Integration ◽

Rna Secondary Structure Prediction ◽

Rna Secondary Structures ◽

Non Coding Rnas

RNA secondary structure prediction is one of the key technologies for revealing the essential roles of functional non-coding RNAs. Although machine learning-based rich-parametrized models have achieved extremely high performance in terms of prediction accuracy, the risk of overfitting for such models has been reported. In this work, we propose a new algorithm for predicting RNA secondary structures that uses deep learning with thermodynamic integration, thereby enabling robust predictions. Similar to our previous work, the folding scores, which are computed by a deep neural network, are integrated with traditional thermodynamic parameters to enable robust predictions. We also propose thermodynamic regularization for training our model without overfitting it to the training data. Our algorithm (MXfold2) achieved the most robust and accurate predictions in computational experiments designed for newly discovered non-coding RNAs, with significant 2–10 % improvements over our previous algorithm (MXfold) and standard algorithms for predicting RNA secondary structures in terms of F-value.

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