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• W4220681784 abstract "Abstract Motivation With the breakthrough of AlphaFold2, the protein structure prediction problem has made a remarkable progress through end-to-end deep learning techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of full-chain model can be further improved by domain assembly assisted by deep learning. Results In this article, we developed a structural analogue-based protein structure domain assembly method assisted by deep learning, named SADA. In SADA, a multi-domain protein structure database (MPDB) was constructed for the full-chain analogue detection using individual domain models. Starting from the initial model constructed from the analogue, the domain assembly simulation was performed to generate the full-chain model through a two-stage differential evolution algorithm guided by the physics-based force field and an inter-residue distance potential predicted by deep learning. SADA was compared with the state-of-the-art domain assembly methods on 356 benchmark proteins, and the average TM-score of SADA models is 8.1% and 27.0% higher than that of DEMO and AIDA, respectively. We also assembled full-chain models of 20 human multi-domain proteins using individual domain models independently predicted by AlphaFold2, where the SADA full-chain models obtained a 4.8% higher average TM-score than full-chain models directly predicted by AlphaFold2 and fewer computing resources were required. In addition, we also find that the domains often interact in the similar way in the quaternary orientations if the domains have similar tertiary structures. Furthermore, homologous templates and structural analogues are complementary for multi-domain protein full-chain modelling. Availability The SADA web server are freely available at http://zhanglab-bioinf.com/SADA ." @default.
• W4220681784 created "2022-04-03" @default.
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• W4220681784 date "2022-03-08" @default.
• W4220681784 modified "2023-10-14" @default.
• W4220681784 title "Structural analogue-based protein structure domain assembly assisted by deep learning" @default.
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• W4220681784 doi "https://doi.org/10.1101/2022.03.07.483151" @default.
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