Biology subjects

Garcia Busto, S.

Publications and source records attributed to Garcia Busto, S..

3 recordsLinked to original sources

bioRxiv2026

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

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.

bioinformatics↗

bioRxiv2026

MIMIQ: Fast mutual information calculation and significance testing for single-cell RNA sequencing analysis

Mutual information is a fundamental quantity in information theory that describes the non-linear dependency between two variables, and has numerous applications within bioinformatics and beyond. However, its exploitation is hampered by a trade-off between computational intensity and accuracy. Here we present an adaptive binning approach to computing the pairwise mutual information, optimized for small integer counts such as those observed in single-cell RNA sequencing. By assuming a sampling distribution such as the negative binomial, a {chi}2 test statistic for hypothesis testing can be computed simultaneously via a copula transformation. Using these quantities, we show how gene rewiring of CD4+ naive T-cells during SARS-CoV-2 infection can be studied using a single-cell sequencing dataset of healthy and COVID-19 donors.

bioinformatics↗

bioRxiv2026

evedesign: accessible biosequence design with a unified framework

Machine learning methods for protein engineering are rarely interoperable, require bespoke workflows, and remain inaccessible to non-experts. Yet the design problems that matter most - conditional design subject to real-world constraints, multi-objective optimization, and iterative lab-in-the-loop workflows where experimental data continuously refines successive design rounds - demand exactly the kind of flexible, composable infrastructure that no single tool provides. We present evedesign, a unified open-source framework that formalizes conditional biosequence design in a method-agnostic way, enabling complex multiobjective workflows combining supervised and unsupervised models from standardized specifications, and built from the outset to support iterative experimental integration. An interactive web interface facilitates end-to-end design for a broad scientific audience at https://evedesign.bio. We demonstrate evedesigns utility in antibody engineering, enzyme design, and natural enzyme discovery, and invite open-source community contributions.

bioinformatics↗