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KinConfBench: A Curated Benchmark for Cofolding Models on Kinase Conformational States.

Kunyang Sun¹, Teresa Head-Gordon^1,2,3

¹Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, CA, 94720 USA.

Biorxiv : the Preprint Server for Biology

|April 17, 2026

Summary

Cofolding models struggle to predict protein kinase conformational changes induced by drugs. New benchmarks reveal these models lack diversity and exhibit "apo-drift," hindering drug discovery efforts.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Biochemistry
Structural Biology
Computational Biology

Background:

Protein kinases are crucial drug targets due to their roles in cellular signaling.
Modulating kinase active and inactive states is key for therapeutic development.
Cofolding models can predict protein structures but their ability to capture ligand-induced conformational changes is unproven.

Purpose of the Study:

To introduce KinConfBench, a benchmark dataset for evaluating cofolding models.
To assess the performance of state-of-the-art cofolding models (Boltz-2, Chai-1, Protenix) in recovering kinase conformational states.
To investigate the correlation between ligand pose accuracy and correct kinase conformation.

Main Methods:

Curated a benchmark of 2,225 human kinase chains (KinConfBench).
Evaluated three cofolding models: Boltz-2, Chai-1, and Protenix.
Assessed model performance using geometric and new dynamical benchmarks for conformational states.

Main Results:

Geometric metrics for ligand pose do not strongly correlate with correct kinase conformational states.
All evaluated cofolding models achieved ~65-75% accuracy in kinase conformational classification.
Models exhibited mode collapse, limited sampling diversity for induced-fit motions, and significant 'apo-drift' towards ligand-free states.

Conclusions:

Current cofolding models have limitations in capturing ligand-induced conformational diversity in protein kinases.
Accurate prediction of conformational dynamics, beyond simple geometric fit, is essential for advancing structure-based drug discovery.
Development of improved cofolding models is needed to better simulate drug-target interactions.