Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions

Koehl, Antoine; Jagota, Milind; Erdmann-Pham, Dan D.; Fung, Alexander; Song, Yun S.

doi:10.1142/9789811250477_0003

Cited by 2 publications

(2 citation statements)

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“…Although vdMs are derived from statistics of sidechain and mainchain interactions with one another in the PDB, they can be used to identify binding-site residues capable of forming hydrogen bonds and aromatic interactions with diverse small molecules in much the same way that natural proteins bind a wide variety of small molecules by using a set of 20 amino acids. Although the energetics of these interactions can vary depending on the specific small molecule bound, the fundamental geometries required to achieve binding often remain relatively constant ( 19 ). Thus, a common set of vdMs should serve to bind a wide range of compounds.…”

Section: De Novo Design Of High-affinity Drug-binding Proteinsmentioning

confidence: 99%

De novo design of drug-binding proteins with predictable binding energy and specificity

Lu,

Gou,

Tan

et al. 2024

Science

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The de novo design of small molecule–binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase–1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule–binding proteins with tuned interaction energies is feasible entirely from computation.

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Section: De Novo Design Of High-affinity Drug-binding Proteinsmentioning

confidence: 99%

De novo design of drug-binding proteins with predictable binding energy and specificity

Lu,

Gou,

Tan

et al. 2024

Science

View full text Add to dashboard Cite

show abstract

“…Because protein intramolecular contacts can closely approximate protein-ligand contacts [28] [22], we reasoned that individual “bait” amino acids could be supplied to AF2, in conjunction with the template of a protein target (either from the protein data bank (PDB) or predicted), to tease out the small-molecule-binding signal. The “bait” might allow AF2 to alleviate regions of frustration in the target template (“finish folding”), and binding-site prediction would then proceed by tracking what AF2 does with this bait.…”

Section: Introductionmentioning

confidence: 99%

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

Gazizov,

Lian,

Goverde

et al. 2023

Preprint

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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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Transferability of Geometric Patterns from Protein Self-Interactions to Protein-Ligand Interactions

Cited by 2 publications

References 0 publications

De novo design of drug-binding proteins with predictable binding energy and specificity

De novo design of drug-binding proteins with predictable binding energy and specificity

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

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