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

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ID: 319195
2026
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Abstract
Abstract Motivation Structure-based virtual screening (SBVS) is limited by the rigid-receptor assumption, which is particularly problematic for kinases that adopt multiple active-site conformations but are experimentally biased toward a single state. Although ensemble screening can address this limitation, it remains computationally expensive. Results We introduce KASSPer (Kinase Active Site Structure Predictor), a framework that predicts kinase active-site conformational states using protein and compound language models. Given a kinase amino acid sequence and a ligand SMILES string, KASSPer enables ligand-specific conformer selection prior to SBVS, potentially reducing the computational cost associated with exhaustive ensemble screening. Benchmarking on the DUD-E kinase subset demonstrates that KASSPer-guided screening outperforms the tested ensemble-based approach across the evaluation metrics. Availability and Implementation The implementation for model loading and inference is available at the GitHub repository https://github.com/kucm-lsbi/KASSPer Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7166810907 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wonkyeong Jang, Woong‐Hee Shin
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag481
URL
https://doi.org/10.1093/bioinformatics/btag481
Keywords Keywords not found

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