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CryptoBench: cryptic protein-ligand binding sites dataset and benchmark.

Vít Škrhák1, Marian Novotný2, Christos P Feidakis2

  • 1Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, 118 00 Prague, Czech Republic.

Bioinformatics (Oxford, England)
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

A new benchmark dataset, CryptoBench, enables better prediction of cryptic binding sites (CBSs) in proteins. Sequence-based methods show superior performance over structure-based approaches for CBS detection, establishing a new baseline.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Drug discovery

Background:

  • Protein-ligand binding site prediction is vital for research and medicine.
  • Existing methods often use ligand-bound (holo) protein structures, which is problematic for cryptic binding sites (CBSs).
  • This reliance on holo states leads to unrealistic performance expectations for CBS detection.

Purpose of the Study:

  • To introduce CryptoBench, a comprehensive benchmark dataset for training and evaluating novel CBS prediction methods.
  • To establish a performance baseline for existing CBS prediction methodologies using CryptoBench.
  • To compare the efficacy of sequence-based versus structure-based methods for CBS detection.

Main Methods:

  • CryptoBench was constructed using apo-holo protein pairs with significant structural changes in binding sites.
  • The dataset includes 1107 structures with predefined cross-validation splits.
  • Sequence-based methods utilized protein language model embeddings, while structure-based methods included PocketMiner and P2Rank.

Main Results:

  • The developed sequence-based approach outperformed PocketMiner and P2Rank in predicting CBS residues.
  • Key metrics such as AUC, AUCPR, MCC, and F1 scores demonstrated the superiority of the sequence-based method.
  • CryptoBench serves as the most extensive dataset for CBS prediction to date.

Conclusions:

  • The sequence-based method provides a strong baseline for future CBS prediction research.
  • CryptoBench is a foundational resource for advancing the field of cryptic binding site detection.
  • The dataset and code are publicly available to facilitate further research and development.