Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of chemical information and modeling
Unveiling Large-Scale Kinase-Centric Protein-Protein Interactions through a Knowledge-Informed Workflow
Protein phosphorylation regulates signaling, yet atomic-level substrate specificity remains elusive due to sparse structural data and phosphorylation-site-insensitive deep-learning predictors. Here we present a pipeline reformulating kinase-substrate modeling as a Bayesian inference problem. By integrating curated data sets and literature evidence parsed by Large Language Models, we converted diverse biological knowledge into structural restraints for the restraint-guided deep-learning model GRASP. For EGFR, BRAF and JNK1, we obtained 336 new phosphorylation-site-specific structure candidates refined by molecular dynamics. These models recapitulate known features, such as JNK1's hydrophobic docking groove, and enabled a Virtual Position Scanning Peptide Array (V-PSPA) to map recognition patches and derive sequence preferences. Cross-referencing predicted interfaces with AlphaMissense pathogenicity scores reveal that the interaction types and distances to the catalytic pocket significantly influence pathogenicity scores. A comparison with clinical mutation data sets further connects pathogenic mutations to the kinase-substrate interface. This high-resolution, high-throughput pipeline can be broadly applicable to kinase specificity studies and general drug discovery.
28 July 2026
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SSPSPredictor: A Sequence- and Structure-Based Deep Learning Model for Predicting Phase-Separating Proteins
Liquid-liquid phase separation (LLPS) underlies the formation of biomolecular liquid condensates (also termed membraneless organelles, MLOs), which are essential for spatially organizing various biochemical processes within cells. Proteins that play a key role in driving condensate formation are termed phase-separating proteins (PSPs). Given that experimental identification of PSPs remains labor-intensive and time-consuming, multiple computational tools have been developed based on empirical features or deep learning. In this study, we propose SSPSPredictor, a novel multimodal predictive model for PSPs with folded or intrinsically disordered structures, leveraging the fusion of sequence information from a protein language model ESM-2 and structural insights from a graph neural network GVP. Compared with existing tools, SSPSPredictor achieves a balanced performance in identifying PSPs, predicting relative LLPS propensities, and recognizing key regions that drive LLPS. More importantly, SSPSPredictor exhibits good interpretability in identifying driving regions along protein sequences, although no relevant supervision was provided during training. Further predictive analysis of the human proteome using SSPSPredictor reveals that the proportion of intrinsically disordered proteins (IDPs) undergoing LLPS is significantly higher than that of the folded proteins. In addition, pathogenic variants, especially those located in disordered regions, exhibit a higher LLPS propensity than other mutations, uncovering a link between LLPS and diseases at the amino acid level. An online Web server for SSPSPredictor (https://bio-comp.ucas.ac.cn/SSPSPredictor/) and its source code (https://github.com/ucas-biocomp/SSPSPredictor) are available.
23 June 2026
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