Biology subjects

Kurgan, L.

Publications and source records attributed to Kurgan, L..

3 recordsLinked to original sources

bioRxiv2025

KcatNet: advancing genome-wide enzyme turnover number prediction through structural enzymatic characterization

Enzyme turnover numbers (Kcat) are fundamental kinetic constants that quantify enzymatic efficiency. Systematic studies of Kcat are essential for characterizing the mechanisms underlying proteomic composition and cellular metabolism. However, experimental measurements of Kcat remain limited and prone to noise. To address this, we present KcatNet, a geometric deep learning model designed for high-throughput prediction of Kcat in metabolic enzymes across all organisms, leveraging paired enzyme sequence and substrate representations. KcatNet consistently outperforms existing predictors, particularly for enzymes with high catalytic efficiency, and demonstrates strong generalization to enzymes that are dissimilar to those in the training set. Furthermore, KcatNet uncovers structural mechanisms and interaction patterns within enzyme-substrate complexes, providing insights into architectural principles that are inaccessible with existing methods by harnessing the representational power of large-scale protein language models. We applied KcatNet to genome-scale Kcat prediction across diverse yeast species, improving proteome allocation predictions by integrating its outputs into metabolic models. Experimental validation further confirmed the models ability to identify enzyme mutants with enhanced activity. By bridging the gap between sequence, structure, and function, KcatNet establishes a robust foundation for advancing understanding of molecular-level mechanisms and accelerating enzyme engineering efforts.

bioinformatics↗

bioRxiv2025

PROBind: A Web Server for Prediction, Analysis and Visualization of Protein-Protein and Protein-Nucleic Acid Binding Residues

Protein-protein and protein-nucleic acids interactions are fundamental to numerous cellular functions, yet only a small fraction have been experimentally characterized. Although modern computational methods have been developed for predicting interacting residues in proteins, they are challenging to use due to individual installation and execution requirements, lack of a standardized input or output format, and absence of support for result analysis. Moreover, methods trained using structures of complexes or intrinsically disordered regions, may not perform well on other types. To overcome these challenges, we develop PROBind, a web server for predicting, analyzing, and interactively visualizing protein, DNA and RNA binding residues from both protein sequences and structures. PROBind integrates 12 predictors trained on structural or disordered proteins, and supports the upload of results from external predictors. By normalizing and averaging predictions from multiple predictors targeting the same ligand type, PROBind generates meta-predictions that balance discrepancies among different methods. Furthermore, it provides interactive graphical tools for result analysis and contextualization. Overall, PROBind accommodates diverse ligand types and supports predictions and analysis based on both structure and sequence data, overcoming the limitations of existing tools. PROBind is freely accessible at https://www.csuligroup.com/PROBind.

bioinformatics↗

bioRxiv2024

SCREEN: a graph-based contrastive learning tool to infer catalytic residues and assess mutation tolerance in enzymes

The accurate identification of catalytic residues contributes to our understanding of enzyme functions in biological processes and pathways. The increasing number of protein sequences necessitates computational tools for the automated prediction of catalytic residues in enzymes. Here, we introduce SCREEN, a graph neural network for the high-throughput prediction of catalytic residues via the integration of enzyme functional and structural information. SCREEN constructs residue representations based on spatial arrangements and incorporates enzyme function priors into such representations through contrastive learning. We demonstrate that SCREEN (i) consistently outperforms currently-available predictors; (ii) provides accurate results when applied to inferred enzyme structures; and (iii) generalizes well to enzymes dissimilar from those in the training set. We also show that the putative catalytic residues predicted by SCREEN mimic key structural and biophysical characteristics of native catalytic residues. Moreover, using experimental data sets, we show that SCREENs predictions can be used to distinguish residues with a high mutation tolerance from those likely to cause functional loss when mutated, indicating that this tool might be used to infer disease-associated mutations.

bioinformatics↗