Dual-graph deconvolution of enzyme fitness: an interpretable framework to identify and explain catalytic modulators

Authors

Garcia Busto, S., Eschbach, E., Hopf, T. A., Sander, C., Bacallado, S., Marks, D. S.

Abstract

Models trained on protein sequences can predict mutational effects, but their outputs represent a convolution of different phenotypes. Isolating stability-independent activity effects is essential for rational enzyme design, allosteric drug design and functional annotation, yet existing pLM-based deconvolution methods lack interpretability. Here, we introduce a hybrid framework that pairs stability predictors with probabilistic graphical models of coevolution. Benchmarked on all enzymes with available multidimensional deep mutational scanning data, our framework isolates stability-independent activity effects with a fidelity that matches analogous pLM-based approaches. Crucially, we reveal biophysically plausible communication pathways that align with previous knowledge and that we validate with experimental data across a diverse set of targets. This hybrid approach merges statistical and geometric deep learning into a zero-shot, explainable tool for targeted engineering and mechanistic discovery.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 16 Oct 2026.

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