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- W4308134792 abstract "Abstract Background Ligand–protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets. Results In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods. Conclusions MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa ." @default.
- W4308134792 created "2022-11-08" @default.
- W4308134792 creator A5005618066 @default.
- W4308134792 creator A5016680184 @default.
- W4308134792 creator A5029695887 @default.
- W4308134792 date "2022-11-02" @default.
- W4308134792 modified "2023-10-06" @default.
- W4308134792 title "A multilayer dynamic perturbation analysis method for predicting ligand–protein interactions" @default.
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- W4308134792 cites W1948887231 @default.
- W4308134792 cites W1963687491 @default.
- W4308134792 cites W1975416940 @default.
- W4308134792 cites W1977032620 @default.
- W4308134792 cites W1980969309 @default.
- W4308134792 cites W1981625739 @default.
- W4308134792 cites W1983194490 @default.
- W4308134792 cites W1986191025 @default.
- W4308134792 cites W2001470965 @default.
- W4308134792 cites W2008954354 @default.
- W4308134792 cites W2010286440 @default.
- W4308134792 cites W2018661561 @default.
- W4308134792 cites W2019450130 @default.
- W4308134792 cites W2022109867 @default.
- W4308134792 cites W2024631466 @default.
- W4308134792 cites W2029181178 @default.
- W4308134792 cites W2031194866 @default.
- W4308134792 cites W2037298322 @default.
- W4308134792 cites W2037750709 @default.
- W4308134792 cites W2041877730 @default.
- W4308134792 cites W2046407284 @default.
- W4308134792 cites W2050456292 @default.
- W4308134792 cites W2051893115 @default.
- W4308134792 cites W2053884159 @default.
- W4308134792 cites W2058463742 @default.
- W4308134792 cites W2065807927 @default.
- W4308134792 cites W2069777230 @default.
- W4308134792 cites W2075999310 @default.
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- W4308134792 cites W2105668062 @default.
- W4308134792 cites W2111762125 @default.
- W4308134792 cites W2113994268 @default.
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- W4308134792 cites W2140538197 @default.
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- W4308134792 cites W2319117079 @default.
- W4308134792 cites W2430084315 @default.
- W4308134792 cites W2468361502 @default.
- W4308134792 cites W2560058357 @default.
- W4308134792 cites W2560290304 @default.
- W4308134792 cites W2588882032 @default.
- W4308134792 cites W2608351113 @default.
- W4308134792 cites W2617750324 @default.
- W4308134792 cites W2734338281 @default.
- W4308134792 cites W2791384094 @default.
- W4308134792 cites W2882976371 @default.
- W4308134792 cites W2887981960 @default.
- W4308134792 cites W2914877211 @default.
- W4308134792 cites W2944973540 @default.
- W4308134792 cites W3005364306 @default.
- W4308134792 cites W3007743886 @default.
- W4308134792 cites W3011701533 @default.
- W4308134792 cites W3015066937 @default.
- W4308134792 cites W3047270904 @default.
- W4308134792 cites W3113154436 @default.
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- W4308134792 cites W3165603781 @default.
- W4308134792 cites W3168436232 @default.
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- W4308134792 doi "https://doi.org/10.1186/s12859-022-04995-2" @default.
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- W4308134792 hasPublicationYear "2022" @default.
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