Zorman, M. | Search bioRxiv

bioRxiv2025

Tunable nucleofugality in carbamoyl-bearing covalent cholinesterase inhibitors

A handful of carbamate warheads is utilised in chemical biology to target serine hydrolases. The following case study on cholinesterases is the first comprehensive structure-reactivity exploration of the carbamoyl warhead, rather than one-target-oriented structure-activity study, with in-depth profiling of diverse halogen, chalcogen, and nitrogen-based leaving groups (nucleofuges) that can tune warhead reactivity. With computational tools we correlated the experimentally observed reactivities with steric and electronic factors of the investigated warheads. QM/MM simulations considering the enzymatic environment explained how substitution of carbon for nitrogen in the leaving groups of compounds 26 and 28 through resonance stabilisation, inductive bond polarization, and acidity amplification lowered the reaction barrier and increased the reaction rate >360 million times, making compound 28 a covalent inhibitor. Our findings underline the complexity of covalent inhibition and demonstrate that multiple complementary methods are required to interpret and predict covalent behaviour. Additionally, even though carbamates typically act as slow substrates, we were able to slow down decarbamoylation to a point where inhibition became de facto irreversible. The most interesting O-isoxazol-3-yl carbamate warhead was further profiled against the wider human proteome and showed low off-target reactivity, making it useful in further drug discovery. By establishing structure-reactivity principles for carbamoyl warhead, this study provides a generalisable framework for the development of selective covalent inhibitors and activity-based probes across diverse targets.

pharmacology and toxicology↗

bioRxiv2024

A learnable transition from low temperature to high temperature proteins with neural machine translation

This work presents Neural Optimization for Melting-temperature Enabled by Leveraging Translation (NOMELT), a novel approach for designing and ranking high-temperature stable proteins using neural machine translation. The model, trained on over 4 million protein homologous pairs from organisms adapted to different temperatures, demonstrates promising capability in targeting thermal stability. A designed variant of the Drosophila melanogaster Engrailed Homeodomain shows increased stability at high temperatures, as validated by estimators and molecular dynamics simulations. Furthermore, NOMELT achieves zero-shot predictive capabilities in ranking experimental melting and half-activation temperatures across two protein families. It achieves this without requiring extensive homology data or massive training datasets as do existing zero-shot predictors by specifically learning thermophilicity, as opposed to all natural variation. These findings underscore the potential of leveraging organismal growth temperatures in context-dependent design of proteins for enhanced thermal stability.

bioinformatics↗