Transmembrane Epitope Delivery by Passive Protein Threading through the Pores of the OmpF Porin Trimer

Sejeong Lee; Nicholas G. Housden; Sandra A. Ionescu; Matthew H. Zimmer; Renata Kaminska; Colin Kleanthous; Hagan Bayley

doi:10.1021/jacs.0c02362

Transmembrane Epitope Delivery by Passive Protein Threading through the Pores of the OmpF Porin Trimer

Journal of the American Chemical Society ◽

10.1021/jacs.0c02362 ◽

2020 ◽

Vol 142 (28) ◽

pp. 12157-12166

Author(s):

Sejeong Lee ◽

Nicholas G. Housden ◽

Sandra A. Ionescu ◽

Matthew H. Zimmer ◽

Renata Kaminska ◽

...

Keyword(s):

Ompf Porin ◽

Protein Threading

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Mutations that alter the pore function of the ompF porin of Escherichia coli K12

Journal of Molecular Biology ◽

10.1016/0022-2836(88)90121-0 ◽

1988 ◽

Vol 203 (4) ◽

pp. 961-970 ◽

Cited By ~ 84

Author(s):

S.A. Benson ◽

J.L.L. Occi ◽

B.A. Sampson

Keyword(s):

Escherichia Coli ◽

Ompf Porin ◽

Escherichia Coli K12

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Linear programming optimization and a double statistical filter for protein threading protocols

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.1145 ◽

2001 ◽

Vol 45 (3) ◽

pp. 241-261 ◽

Cited By ~ 97

Author(s):

Jaroslaw Meller ◽

Ron Elber

Keyword(s):

Linear Programming ◽

Protein Threading

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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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