Biology subjects

B, A.

Publications and source records attributed to B, A..

3 recordsLinked to original sources

bioRxiv2026

Structural and Metabolic Characterization of Ni(I)-inhibitors Provide a Robust Anti-Methanogenicity Scoring System

Atmospheric methane (CH4) acts as a key contributor to global warming and a short-lived climate forcer. CH4 mitigation represents the most promising means to address short-term climate change. Ruminant enteric CH4 produced by methanogenic archaea represents 27.2% of global CH4 emissions. Only a few of the direct methanogenesis inhibitors identified bear high mitigation potential hence it is important to investigate their underlying modes of action. Here, we elucidated biophysical and thermodynamic interplay between known inhibitors and cofactor F430, to determine their stoichiometric ratios and binding affinities. We leverage this prior in a robust contrastive learning approach to functionally cluster known sixteen inhibitors and 53,959 bovine-linked metabolites. We demonstrate a multi-factor optimization protocol to identify putative inhibitors with: (i) high bacterial membrane permeability, (ii) no adverse effect to ruminal fermentation, (iii) known degradation pathway, and (iv) direct commercial availability. Subsequent in vitro assays and community metabolic modeling with a first set of eight treatment molecules revealed structo-metabolic priors that tie thermodynamic signatures of inhibition to metabolic flux shifts. We established a multi-scale workflow that transforms ostensibly negative compounds into mechanistic insight, linking rumen metabolic flux shifts to MCR-F430-Ni(I) inhibition chemistry as a foundation for rational methane-mitigation design. COVER ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/708075v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1d38d21org.highwire.dtl.DTLVardef@1d681d9org.highwire.dtl.DTLVardef@1e70c0corg.highwire.dtl.DTLVardef@1c8223f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Robust Prediction of Enzyme Variant Kinetics with RealKcat

Predicting enzyme kinetics directly from sequence remains a central challenge in computational biology, particularly in resolving the effects of mutations at catalytically essential residues. Existing models frequently overlook the functional consequences of such perturbations, often defaulting to wild-type predictions even in cases of substantial activity loss, thereby limiting their reliability for enzyme design and mechanistic inference. Here, we introduce RealKcat, a machine learning framework trained on KinHub-27k, a rigorously curated dataset of 27,176 experimentally reported enzyme-substrate entries consolidated from BRENDA, SABIO-RK, and UniProt and verified across 2,158 primary sources. To ensure biochemical realism, kinetic parameters were collapsed into order-of-magnitude bins, enabling predictions that are tolerant to experimental noise yet sensitive to functional shifts. RealKcat integrates ESM embeddings for enzyme sequences with ChemBERTa embeddings of affiliated substrate, producing a unified feature space of the chemical conversion that supports robust multi-class classification of both catalytic turnover (kCat) and substrate affinity (KM). Across cross-validation, hold-out, out-of-distribution, and few-shot evaluations--including a dense mutational landscape of alkaline phosphatase (PafA)--RealKcat consistently capturead the direction and magnitude of mutation-induced changes, while preserving discrimination in both wild-type and mutant contexts. Importantly, structural descriptors were deliberately excluded, as naive integration of structural features has been shown to impair model generalization, underscoring the primacy of rigorous dataset curation, biologically informed task formulation, and balanced evaluation metrics. RealKcat establishes a scalable and mutation-sensitive framework for enzyme kinetics prediction, offering a biologically grounded platform for enzyme engineering, metabolic modeling, and therapeutic design. Significance StatementEnzymes catalyze biochemical reactions that sustain life, and accurate measurement of their efficiency--expressed through turnover number (kCat) and substrate affinity (KM)--is fundamental to biotechnology, synthetic biology, and even pharmaceutical innovation. Yet experimental assays remain prohibitive, time-intensive, and sensitive to conditions such as pH, temperature, and ionic strength of the assay buffer, while existing computational approaches often lack sensitivity to catalytic-site mutations and are constrained by inconsistencies in public databases. RealKcat addresses these gaps by introducing a rigorously curated dataset (KinHub-27k) derived from manual review of 2,158 articles and augmented with 5,278 synthetic catalytic variants generated through alanine substitution at annotated catalytic residues. Leveraging protein and substrate embeddings and a classification scheme based on order-of-magnitude kinetic bins, RealKcat achieves state-of-the-art functional e-accuracy and, critically, demonstrates sensitivity to catalytic perturbations. By adopting e-accuracy--a performance metric that evaluates predictions within {+/-}1 order of magnitude, aligning with the practical utility of enzyme kinetics--RealKcat provides biologically meaningful assessments that conventional metrics often obscure. This work establishes a robust, mutation-aware predictive platform that advances computational enzyme design and extends applicability to biomanufacturing, metabolic engineering, and precision medicine.

systems biology↗

bioRxiv2024

Exploring putative enteric methanogenesis inhibitors using molecular simulations and a graph neural network

Atmospheric methane (CH4) acts as a key contributor to global warming. As CH4 is a short-lived climate forcer (12 years atmospheric lifespan), its mitigation represents the most promising means to address climate change in the short term. Enteric CH4 (the biosynthesized CH4 from the rumen of ruminants) represents 5.1% of total global greenhouse gas (GHG) emissions, 23% of emissions from agriculture, and 27.2% of global CH4 emissions. Therefore, it is imperative to investigate methanogenesis inhibitors and their underlying modes of action. We hereby elucidate the detailed biophysical and thermodynamic interplay between anti-methanogenic molecules and cofactor F430 of methyl coenzyme M reductase and interpret the stoichiometric ratios and binding affinities of sixteen inhibitor molecules. We leverage this as prior in a graph neural network to first functionally cluster these sixteen known inhibitors among [~]54,000 bovine metabolites. We subsequently demonstrate a protocol to identify precursors to and putative inhibitors for methanogenesis, based on Tanimoto chemical similarity and membrane permeability predictions. This work lays the foundation for computational and de novo design of inhibitor molecules that retain/ reject one or more biochemical properties of known inhibitors discussed in this study. COVER ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=146 SRC="FIGDIR/small/613350v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1fd4518org.highwire.dtl.DTLVardef@c34d87org.highwire.dtl.DTLVardef@16eef9org.highwire.dtl.DTLVardef@1a33512_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗