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Assessment of AI-Based Protein Structure Prediction for the NLRP3 Target.

ABSTRACT: The recent successes of AlphaFold and RoseTTAFold have demonstrated the value of AI methods in highly accurate protein structure prediction. Despite these advances, the role of these methods in the context of small-molecule drug discovery still needs to be thoroughly explored. In this study, we evaluated whether the AI-based models can reliably reproduce the three-dimensional structures of protein-ligand complexes. The structure we chose was NLRP3, a challenging protein target in terms of obtaining a three-dimensional model both experimentally and computationally. The conformation of the binding pockets generated by the AI models was carefully characterized and compared with experimental structures. Further molecular docking results indicated that AI-predicted protein structures combined with molecular dynamics simulations offers a promising approach in small-molecule drug discovery.

SUBMITTER: Yin J

PROVIDER: S-EPMC9505260 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Assessment of AI-Based Protein Structure Prediction for the NLRP3 Target.

Yin Jian J Lei Junkun J Yu Jialin J Cui Weiren W Satz Alexander L AL Zhou Yifan Y Feng Hua H Deng Jason J Su Wenji W Kuai Letian L

Molecules (Basel, Switzerland) 20220907 18

The recent successes of AlphaFold and RoseTTAFold have demonstrated the value of AI methods in highly accurate protein structure prediction. Despite these advances, the role of these methods in the context of small-molecule drug discovery still needs to be thoroughly explored. In this study, we evaluated whether the AI-based models can reliably reproduce the three-dimensional structures of protein-ligand complexes. The structure we chose was NLRP3, a challenging protein target in terms of obtain ...[more]

PMID: 36144532

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