Biology subjects

Broadbelt, L. J.

Publications and source records attributed to Broadbelt, L. J..

5 recordsLinked to original sources

bioRxiv2026

Mechanism-informed rules tunably balance novelty and feasibility of predicted enzymatic reactions

Enzymes catalyze reactions with remarkable specificity and can unlock recalcitrant feedstocks that are dilute, complex, and variable in their constituent molecules. While characterized enzymatic reactions cover a wide range of chemistries, there are an undetermined number of cryptic activities for every known one. These cryptic activities can be elicited through rational design, adaptive laboratory evolution, and increasingly, generative models of proteins. However, prior to tuning a catalyst one must efficiently predict viable novel reactions. In this work we leverage the growing amount of mechanistic enzyme information, specifically the Mechanism and Catalytic Site Atlas, to construct a set of reaction rules that can meet this demand. By explicitly utilizing mechanistic information, the rule sets developed here more accurately identify molecular structures required for catalysis compared to existing curated and heuristically constructed rules. The 899 Distilled rules are constructed directly from characterized mechanisms and cover 62.5% of reactions from Rhea. The Learned rule set is generated from a classifier trained on mechanistic data, allowing full coverage of Rhea and precise identification of mechanism-required atoms (ROC-AUC = 0.98). Additionally, our Learned rules exhibit a more favorable tradeoff between novelty and feasibility and provide users with fine-grained control over this tradeoff. The rules are compatible with all SMARTS-based reaction network expansion and retrosynthesis software.

synthetic biology↗

bioRxiv2025

RC-GNN: A predictive model of enzyme-reaction pairs

Uncharacterized functions of enzymes represent untapped opportunity to develop therapeutics, unlock the sustainable synthesis of materials, and understand the evolution of life-sustaining metabolic networks. Uncharacterized enzymes and reactions, generated by protein language models and computer-aided synthesis tools, respectively, make up a large part of this opportunity. Given the technical complexity of high-throughput enzymatic activity screens, predictive models are needed that can pre-screen enzyme-reaction pairs in silico. We present Reaction-Center Graph Neural Network, (RC-GNN) a model capable of predicting whether an enzyme, represented by an amino acid sequence, can significantly catalyze a given reaction, represented by its full set of reactants and products. We explicitly evaluated RC-GNNs generalization to queries highly dissimilar from those present in the training dataset. In the most difficult conditions tested, our models achieve 0.88 and 0.84 ROC-AUC on classification tasks featuring globally selected and synthetic negatives, respectively. On a time-based split an RC-GNN achieved 0.91 ROC-AUC. The ability to successfully make predictions on enzymes and reactions distinct from those used during training makes RC-GNN especially useful for both metabolic engineers and evolutionary biologists who need to reason about uncharacterized enzymatic reactions.

bioinformatics↗

bioRxiv2024

Biosynth Pipeline: An integrated platform for merging the computational design of chimeric type I polyketide synthases with enzymatic pathways for chemical biosynthesis

Synthetic biology offers the promise of manufacturing chemicals more sustainably than petrochemistry. Yet, both the rate at which biomanufacturing can synthesize these molecules and the net chemical accessible space are limited by existing pathway discovery methods which can often rely on arduous literature searches. Here, we present an automated retrobiosynthesis tool, BioPKS Pipeline, that simultaneously tackles both problems by integrating multifunctional type I polyketide synthases (PKSs) with monofunctional enzymes to propose the synthesis of desired target chemicals via two new tools: DORAnet and RetroTide. While monofunctional enzymes are valuable for carefully decorating a substrates carbon backbone, they typically cannot expand the backbone itself. PKSs can, instead, predictably do this through their unique ability to catalyze carbon-carbon bond formation reactions iteratively. We have evaluated the performance of BioPKS Pipeline against a previously published set of 155 molecules of interest for biomanufacturing, and report that BioPKS Pipeline could produce exact designs for 93 of them, as well as pipelines to a chemically similar product for most of the remaining molecules. Furthermore, BioPKS Pipeline successfully proposes biosynthetic routes for complex therapeutic natural products (cryptofolione and basidalin) for which no known biosynthetic pathway currently exists.

bioinformatics↗

bioRxiv2022

Dynamic Kinetic Models Capture Cell-Free Metabolism for Improved Butanol Production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a novel combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 hours. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

synthetic biology↗

bioRxiv2018

Bayesian inference of metabolic kinetics from genome-scale multiomics data

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, integrating these data sources to generate predictions of steady-state metabolism typically requires a kinetic description of the enzymatic reactions that occur within a cell. Parameterizing these kinetic models from biological data can be computationally difficult, especially as the amount of data increases. Robust methods must also be able to quantify the uncertainty in model parameters as a function of the available data, which can be particularly computationally intensive. The field of Bayesian inference offers a wide range of methods for estimating distributions in parameter uncertainty. However, these techniques are poorly suited to kinetic metabolic modeling due to the complex kinetic rate laws typically employed and the resulting dynamic system that must be solved. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and variational inference methods. We demonstrate that detailed information on the posterior distribution of kinetic model parameters can be obtained efficiently at a variety of different problem scales, including large-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and developing new, efficient strain designs.

systems biology↗