Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Smith, Zachary; Strobel, Michael; Vani, Bodhi P.; Tiwary, Pratyush

doi:10.1021/acs.jcim.3c01698

Cited by 6 publications

(1 citation statement)

References 58 publications

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“…A variety of intelligent frameworks were introduced for predicting the structures of blood-brain barrier-penetrating peptide, antimicrobial peptide, and intrinsically disordered protein. − Elia Venanzi et al applied a machine learning method that integrated protein structure, sequence, and dynamics to predict the enzymatic activity of bovine intestinal kinase variants. Zachary Smith et al identified druggable binding sites of protein target using graph neural networks with attention. ( b ) virtual screening and drug design .…”

mentioning

confidence: 99%

Editorial: Machine Learning in Bio-cheminformatics

Merz,

Wei,

Zhu

2024

J. Chem. Inf. Model.

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confidence: 99%

Editorial: Machine Learning in Bio-cheminformatics

Merz,

Wei,

Zhu

2024

J. Chem. Inf. Model.

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Comparative evaluation of methods for the prediction of protein–ligand binding sites

Utgés,

Barton

2024

J Cheminform

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The accurate identification of protein–ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank, PRANK and fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of our new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein–ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein–ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein–ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by PRANK and DeepPocket display the highest recall (60%) whilst IF-SitePred presents the lowest recall (39%). We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Finally, we propose top-N+2 recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark.Scientific contributionsThis study conducts the largest benchmark of ligand binding site prediction methods to date, comparing 13 original methods and 15 variants using 10 informative metrics. The LIGYSIS dataset is introduced, which aggregates biologically relevant protein–ligand interfaces across multiple structures of the same protein. The study highlights the detrimental effect of redundant binding site prediction and demonstrates significant improvement in recall and precision through stronger scoring schemes. Finally, top-N+2 recall is proposed as a universal benchmark metric for ligand binding site prediction, with a recommendation for open-source sharing of both methods and benchmarks.

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Comparative evaluation of methods for the prediction of protein-ligand binding sites

Utgés,

Barton

2024

Preprint

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The accurate identification of protein-ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed since the early 1990s. Over this time, the paradigm has changed from geometry-based to machine learning. In this work, we collate 11 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank or fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of the new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein-ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein-ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein-ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by DeepPocket and PRANK display the highest recall (60%) whilst VN-EGNN (46%) and IF-SitePred (39%) present the lowest recall. We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Methods predicting few pockets per protein, e.g., GrASP and PUResNet are very precise (> 90%) but are limited in recall. Finally, we propose recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark.

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Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention

Cited by 6 publications

References 58 publications

Editorial: Machine Learning in Bio-cheminformatics

Editorial: Machine Learning in Bio-cheminformatics

Comparative evaluation of methods for the prediction of protein–ligand binding sites

Comparative evaluation of methods for the prediction of protein-ligand binding sites

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