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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

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Related Experiment Video

Updated: Jun 13, 2026

Modeling Ligands into Maps Derived from Electron Cryomicroscopy
09:30

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Published on: July 19, 2024

Pocket-Surface Discrete Differential Geometry as a Leakage-Robust Feature Class for Protein-Ligand Binding Affinity

Mehmet Ali Balcı1, Erbil Çetin2, Gizem Calibasi-Kocal3

  • 1Department of Mathematics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey.

Molecules (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

Discrete differential geometry descriptors improve protein-ligand binding affinity prediction. This method offers a robust and interpretable feature class for virtual screening, outperforming traditional approaches in leakage-robust benchmarks.

Keywords:
Laplace–Beltrami operatordiscrete differential geometryheat-kernel signatureleakage-protected splitsmolecular surfaceprotein–ligand binding affinitystructure-based drug discovery

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Related Experiment Videos

Last Updated: Jun 13, 2026

Modeling Ligands into Maps Derived from Electron Cryomicroscopy
09:30

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Published on: July 19, 2024

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Protein-ligand binding affinity prediction is crucial for structure-based drug discovery.
  • Public benchmarks often overestimate model generalization due to data leakage.
  • The utility of explicit pocket geometry descriptors versus graph neural networks is unclear.

Purpose of the Study:

  • To develop and evaluate a novel discrete differential geometry descriptor for protein-ligand binding affinity prediction.
  • To assess the descriptor's performance under strict data splitting regimes and against leakage-proof benchmarks.
  • To compare the descriptor's effectiveness against atom-level graph neural networks.

Main Methods:

  • Computed a 59-dimensional discrete differential geometry descriptor on the ligand-aware solvent-excluded surface of 3285 PDBBind v2020 complexes.
  • Combined curvature distributions, Laplace-Beltrami eigenvalues, and heat-kernel signatures.
  • Evaluated the descriptor in gradient-boosted tree pipelines and a SchNet-style graph neural network across various benchmarks and split strategies.

Main Results:

  • The descriptor significantly improved prediction accuracy (Pearson correlations) on cluster-disjoint testing (+0.111), LP-PDBBind DataSAIL S2 (+0.258), and CASF-2016 (+0.365).
  • In isolation, the descriptor achieved performance comparable to established methods like X-Score and AutoDock Vina on external benchmarks (0.456–0.594).
  • Graph neural network injection strategies showed no significant performance lift, suggesting atomic coordinate-based methods capture geometric information effectively.

Conclusions:

  • Pocket-surface discrete differential geometry provides an interpretable, leakage-robust, and lightweight feature class for early-stage virtual screening.
  • This approach enhances binding affinity prediction accuracy, particularly in challenging, leakage-controlled scenarios.
  • The findings motivate the development of hybrid mesh-to-atom architectures for improved drug discovery models.