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Related Concept Videos

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.
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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...
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 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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KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to

Wonkyeong Jang1, Woong-Hee Shin1,2

  • 1Department of Biomedical Informatics, Korea University College of Medicine, Seoul, 02708, Republic of Korea.

Bioinformatics (Oxford, England)
|July 1, 2026
PubMed
Summary

Kinase Active Site Structure Predictor (KASSPer) identifies kinase active site conformations, improving structure-based virtual screening (SBVS) efficiency. KASSPer-guided screening outperforms ensemble methods, reducing computational costs for drug discovery.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Drug discovery

Background:

  • Structure-based virtual screening (SBVS) is hindered by the rigid-receptor assumption, especially for kinases with multiple active-site conformations.
  • Existing ensemble screening methods are computationally expensive and may not fully capture dynamic receptor states.

Purpose of the Study:

  • To introduce KASSPer (Kinase Active Site Structure Predictor), a novel framework for predicting kinase active-site conformational states.
  • To enable ligand-specific conformer selection for structure-based virtual screening, reducing computational costs.

Main Methods:

  • Utilized protein and compound language models to predict kinase active-site conformational states.
  • Developed a framework that takes kinase amino acid sequence and ligand SMILES string as input.
  • Integrated KASSPer for ligand-specific conformer selection prior to structure-based virtual screening.

Main Results:

  • KASSPer successfully predicts kinase active-site conformational states.
  • KASSPer-guided screening demonstrated superior performance compared to ensemble-based approaches on the DUD-E kinase subset.
  • The framework offers a computationally efficient alternative to exhaustive ensemble screening.

Conclusions:

  • KASSPer enhances structure-based virtual screening by accurately predicting relevant kinase conformations.
  • The developed framework offers a significant advancement in computationally efficient drug discovery for kinases.
  • KASSPer-guided screening provides a more effective approach for identifying potential kinase inhibitors.