New publication in ChemPhysChem

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Sequeiros-Borja C, Škoda P, Brezovsky J, 2026: Toward Automatic Derivation of Geometry-Based Descriptors as Surrogates for Complex Computational Approaches in Enzyme–Substrate Prediction. ChemPhysChem 27: e202500883. full text dataset

Accurate prediction of enzyme–substrate (ES) interactions remains a fundamental challenge in biocatalysis and drug discovery. While machine learning (ML) approaches have shown promise, they require extensive training data and often lack mechanistic interpretability. Here, we present a novel methodology that automatically derives geometry-based descriptors from ES complex structures to predict substrate specificity. Our approach simplifies complex catalytic mechanisms into interpretable geometric filters comprising critical inter-atomic distances and accessibility of atomic pairs parameters. We validated this methodology using two mechanistically distinct enzyme families with minimal training data: haloalkane dehalogenases (HLDs; 9 enzymes and 53 substrates) and aldehyde reductases (AldRs; 9 enzymes and 36 substrates). The filters demonstrated robust performance across chemically diverse substrates. On testing datasets, the derived filters achieved average accuracy of 77% and sensitivity of
94% for HLDs and average 57% recall of true substrates for AldRs, exceeding state-of-the-art ML methods for substrate predictions on these datasets. Crucially, the geometric descriptors directly correspond to catalytic requirements, providing mechanistic insights into substrate recognition. This interpretable, mechanism-based approach requires minimal training data and can be readily applied to newly characterized enzymes, offering a powerful tool for enzyme engineering and substrate screening applications.

New publication in Journal of Medicinal Chemistry

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Prochazkova J, Carbain B, Marini V, Nikulenkov F, Havel S, Akavaram N, Khirsariya P, Sisakova A, Cibulka J, Boudova M, Zacpalova M, Kalovska M, Rodrigues J, Daniel L, Brezovsky J, Bartunek P, Azzalin C, Paruch K, Krejci L 2026: Discovery of Two Structurally Distinct Classes of Inhibitors Targeting the Nuclease MUS81 and Enhancing Efficacy of Chemotherapy in Cancer Cells. Journal of Medicinal Chemistry 69: 5350–5369. full text

Nucleases are promising pharmacological targets due to their essential role in maintaining genomic stability. They are crucial for regulation of cell viability, and their modulation is exploitable in disease prevention and treatment, including cancer. The conserved structure-specific endonuclease MUS81 resolves branched DNA intermediates during replication, repair, and recombination. Aberrant MUS81 activity causes DNA damage, chromosomal abnormalities, and genome instability, contributing to oncogenesis. Thus, pharmacological targeting of MUS81 is an attractive yet underexplored therapeutic strategy. We describe the discovery of two chemically distinct small-molecule classes of MUS81 inhibitors, exemplified by compounds MU262 and MU876. Both compounds effectively inhibit MUS81 in vitro and in cells, sensitizing cancer cells to DNA-damaging agents by impairing DNA repair. These inhibitors can also serve as chemical biology tools for a deeper study of MUS81 function and as leads for drug discovery aimed at therapies exploiting DNA repair vulnerabilities in cancer treatment.

 

New publication in Journal of Chemical Theory and Computation

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Mandal N, Stevens JA, Poma AB, Surpeta B, Sequeiros-Borja C, Thirunavukarasu AS, Marrink SJ, Brezovsky J, 2026: Unlocking high-throughput investigation of transport tunnels in enzymes using coarse-grained simulation methods. Journal of Chemical Theory and Computation 22: 135-150. full text dataset

Transport tunnels in enzymes with buried active sites are critical gatekeepers of enzymatic function, controlling substrate access, product release, and catalytic efficiency. Despite their importance, the transient nature of these tunnels makes them difficult to study using conventional simulation methods. In this study, we systematically evaluate three coarse-grained (CG) molecular dynamics approaches─Martini with Elastic network restraints, Martini with Go̅-model restraints, and SIRAH─for their ability to characterize tunnel structure and dynamics across diverse enzyme classes. Using haloalkane dehalogenase LinB and its engineered variants as model systems, we show that CG methods accurately reproduce the geometry of tunnel ensembles observed in all-atom (AA) simulations while providing notable computational speedups. The Martini-Go̅ model performed particularly well, capturing subtle mutation-induced changes in tunnel dynamics, such as the closure of a main tunnel and the de novo opening of a transient auxiliary tunnel in LinB variants. In contrast, Martini with Elastic network restraints was limited in capturing tunnel dynamics due to the structural bias introduced by the restraints. We further validated these findings across nine enzymes from the oxidoreductase, transferase, and hydrolase classes with diverse structural folds. Although all CG methods reliably identified functionally relevant tunnels and provided fairly accurate estimates of their ensemble geometry and key bottleneck residues, they differed in their ability to replicate tunnel dynamics, with tunnel occurrences and ranking showing moderate to good correspondence with AA results. This comprehensive evaluation highlights the strengths and weaknesses of CG simulations, establishing them as powerful tools for high-throughput analysis of enzyme tunnels, which enables more efficient enzyme engineering and drug design efforts targeting these critical structural features.