Author

E. D. Akten

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Aug 2026

Systematic identification of species-specific allosteric sites in bacterial glycolytic enzymes: Hexokinase, phosphoglucose isomerase, phosphoglycerate kinase, and enolase.

In this study, we developed and applied an integrative computational workflow for the systematic identification and prioritization of candidate allosteric pockets across all four glycolytic enzymes: three from Staphylococcus aureus-phosphoglucose isomerase (PGI), phosphoglycerate kinase (PGK), and enolase- and one representative hexokinase from Plasmodium vivax, included due to the absence of an experimentally determined three-dimensional structure for the S. aureus ortholog. Solvent mapping using FTMap and FTMove across oligomeric ensembles revealed multiple high-confidence cavities predominantly located at subunit interfaces, in addition to canonical catalytic sites. Independent evaluation with CavityPlus supported the presence and druggability of these pockets. Hexokinase presented 12 interface-associated pockets that emerged only upon oligomer formation and remained stable across 300 FTMove-derived conformers. PGI and PGK displayed interface- and hinge-associated cavities linked to known global motions, while the octameric enolase showed prominent central and peripheral inter-dimer pockets. Candidate pockets were subsequently evaluated using CorrSite, ESSA, PASSer, and AlloSigMA to assess features associated with allosteric communication, energetic coupling, and protein dynamics. High-confidence candidate sites were prioritized based on consensus across these complementary computational approaches. Across all four enzymes, interface-localized pockets consistently emerged as promising candidate regulatory regions, suggesting that protein-protein interfaces may represent valuable targets for allosteric modulation. Several predicted pockets, particularly in PGI and enolase, exhibited low sequence and structural similarity to the corresponding human homologs, indicating their potential for selective inhibitor design. Overall, this integrated computational framework provides a systematic strategy for identifying and prioritizing candidate allosteric pockets for future structural, biochemical, and structure-based drug discovery.

Defne Alnıgeniş, Florihana Brina, Ilknur Kocal et al. · Biochimica et Biophysica Act... · 1 citation

Open access Jun 2026

Structural feature-based machine learning benchmarking for protein interface prediction.

Accurate prediction of protein-protein interaction interfaces is critical for understanding molecular recognition and guiding therapeutic design. This study presents a comprehensive machine learning pipeline for predicting interface residues in permanent homodimeric protein complexes. Using a curated dataset of 1311 homodimers, we benchmarked six widely used machine learning algorithms and identified multilayer perceptron and XGBoost as top performers, achieving Matthews correlation coefficients (MCC) exceeding 0.93. To enhance interpretability and efficiency, we employed recursive feature elimination to derive a minimal set of six biologically meaningful features, including solvent accessibility, surface roughness, planarity, and average protrusion index, that retained high predictive power (MCC > 0.90). Structurally stratified models tailored to α-helical, β-strand, and membrane proteins demonstrated comparable or improved accuracy relative to generalized models, particularly when utilizing the reduced feature subset. As a preliminary demonstration of generalizability, we applied our approach to an external heterodimer complex (PDB ID: 9ETL). While limited to a single case study, the structurally specialized models maintained high accuracy, suggesting potential applicability beyond the training domain. Furthermore, our residue-level feature-driven models demonstrated highly competitive performance when compared against the baseline established by the general-purpose ColabFold pipeline. The results highlight the importance of structural context in interface prediction and demonstrate that compact, structure-aware models can achieve high accuracy while reducing computational complexity. This work provides a scalable, interpretable, and biologically informed approach to protein interface prediction, with implications for large-scale structural descriptor, drug target characterization, and protein engineering applications.

Tayyip Topuz, Z. Erdem, Halil Bisgin et al. · Scientific Reports · 0 citations