AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2

Gazizov, A.; Lian, A.; Goverde, C. A.; Ovchinnikov, S.; Polizzi, N. F.

doi:10.1101/2023.10.15.562410

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bioRxiv · 10.1101/2023.10.15.562410

AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2

Gazizov, A.·Lian, A.·Goverde, C. A.·Ovchinnikov, S.·Polizzi, N. F.

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Abstract

Predicting ligand-binding sites, particularly in the absence of previously resolved homologous structures, presents a significant challenge in structural biology. Here, we leverage the internal pairwise representation of AlphaFold2 (AF2) to train a model, AF2BIND, to accurately predict small-molecule-binding residues given only a target protein. AF2BIND uses 20 "bait" amino acids to optimally extract the binding signal in the absence of a small-molecule ligand. We find that the AF2 pair representation outperforms other neural-network representations for binding-site prediction. Moreover, unique combinations of the 20 bait amino acids are correlated with chemical properties of the ligand.

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Gazizov, A., Lian, A., Goverde, C. A., Ovchinnikov, S., Polizzi, N. F.. 2023-10-18. AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2. https://doi.org/10.1101/2023.10.15.562410

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