ArticleComputational and structural biotechnology journal2026
Machine learning models for prediction of (Pro)cathepsin-glycosaminoglycan binding free energies based on molecular structure.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
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3 authors.
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Abstract
Cathepsins are papain-like proteolytic enzymes localized in lysosomes and the extracellular matrix, where they participate in diverse physiological and pathological processes. They are synthesized as inactive precursors-procathepsins-containing a propeptide domain that blocks access to the active site. The activity of (pro)cathepsins can be modulated by glycosaminoglycans (GAGs), which are negatively charged, sulfated polysaccharides. This study aimed to develop machine learning (ML) models to predict MM-GBSA binding free energies in (pro)cathepsin-GAG complexes. Molecular dynamics simulations were performed using the ff14SB/GLYCAM06j force field for six (pro)cathepsins and six GAGs, representing four periodic states and six binding poses. Structural and energetic descriptors derived from these simulations were used as input features for eight ML algorithms: ElasticNet, Linear Regression, LinearSVR (with RBFSampler), LightGBM, Histogram Gradient Boosting, Fully Connected Neural Network (FCNN), and Random Forest. The FCNN yielded the most accurate predictions (
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