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Related Experiment Video

Updated: Jun 9, 2025

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Published on: October 26, 2015

Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases.

Jinung Song¹, Junsu Ha², Juyong Lee^1,2,3

¹College of Pharmacy, Seoul National University, Seoul, Republic of Korea.

Scientific Reports

|October 25, 2024

Summary

This summary is machine-generated.

Multi-state modeling (MSM) for AlphaFold2 (AF2) kinase structures improves drug discovery by overcoming structural bias. This approach enhances virtual screening, identifying diverse kinase inhibitors more effectively.

Keywords:

AlphaFold2 Ensemble screening Kinase Multi-state modeling Protein-ligand docking Structure-based virtual screening

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Last Updated: Jun 9, 2025

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

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Published on: October 26, 2015

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Published on: August 29, 2015

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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Published on: July 16, 2017

Area of Science:

Computational chemistry
Structural biology
Drug discovery

Background:

Structure-based virtual screening (SBVS) is vital in drug discovery but limited by protein structural variations.
Kinase drug discovery faces challenges due to active site conformational changes and a bias towards DFGin state structures.

Purpose of the Study:

To introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures.
To address the structural bias in SBVS by enabling the discovery of diverse inhibitor scaffolds.

Main Methods:

Developed and applied an MSM protocol for AF2 kinase structures using state-specific templates.
Conducted comprehensive benchmarks evaluating model quality, binding pose prediction, and hit compound identification via ensemble SBVS.

Main Results:

MSM models showed comparable or improved structural accuracy over standard AF2 models.
Enhanced binding pose prediction accuracy and better capture of kinase-ligand interactions were observed.
MSM consistently outperformed standard AF2 and AF3 modeling in virtual screening, identifying more diverse hit compounds.

Conclusions:

MSM offers a promising solution to structural bias in kinase-targeted drug discovery.
This protocol can broaden the scope of kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors.