Biology subjects

Maranas, C. D.

Publications and source records attributed to Maranas, C. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Accessing Enzyme Kinetic Data and Prediction Methods at Scale

Enzyme kinetic parameters inform metabolic models, yet experimental measurements are sparse. A growing body of work predicts them from protein and substrate features, but software fragmentation hinders adoption, so downstream tools lock into the most accessible method. We present OpenKinetics Predictor (at predictor.openkinetics.org), an open-source platform integrating thirteen methods in isolated environments behind one interface. The platform optionally reports similarity between query proteins and each method's training data to contextualise reliability. A common featurisation-prediction abstraction keeps it extensible, and independent parties, including original authors, contributed many methods. We pair it with a data portal (at data.openkinetics.org) that exposes CatLog, a curated kinetic dataset, with precomputed embeddings, predicted binding sites, and standardised splits. Both offer a web interface and an API, and the GECKO modelling toolbox already calls the predictor API. As a case study, we predict across an E. coli model and find inter-predictor agreement varies with metabolic context and data availability.

systems biology↗

bioRxiv2026

The ModelSEED Biochemistry Database, 2026 update: grading multi-source thermodynamics

The ModelSEED Biochemistry Database (https://modelseed.org) supplies foundational mass- and charge-balanced reaction networks for metabolic reconstructions. Here we present an update on the biochemistry where the database has been expanded to [~] 46,000 compounds, and [~] 56,000 reactions, featuring [~] 37,000 metabolic structures. We have expanded our approach for handling thermodynamic data, enabling multiple sources of data to be derived, integrated, and presented to the wider research community. We now publish predictions of pKa, reaction energy (and respective uncertainties), and estimates of reaction direction from multiple independent sources. Each reaction is graded gold, silver or bronze according to the strength of the evidence behind it, so that users can weigh its reliability directly. We also release reaction directions predicted by an ensemble of large language models. This multi-source approach exposes agreements and discrepancies between sources for [~] 33,000 reactions. Finally, to ensure data integrity, a new conflict-resolution pipeline reconciles structures across sources, documenting input from curators. Our work is publicly available at https://github.com/ModelSEED/ModelSEEDDatabase.

biochemistry↗

bioRxiv2026

Engineered Pseudomonas putida reconfigures metabolic fluxes to support energy demands during muconate bioproduction from lignin-related aromatics

Muconic acid is a versatile platform chemical that can be biologically produced from lignocellulosic substrates, including from lignin-related aromatic compounds. Pseudomonas putida has been previously engineered to convert lignin-related aromatic compounds to muconate at quantitative molar yields. This high atom efficiency requires a supplemental carbon and energy source to support bacterial growth, and central carbon metabolic efficiency and its interaction with aromatic catabolism are underexplored. Here, we applied proteomics, metabolomics, and 13C-fluxomics to quantitatively compare central carbon and energy metabolism in wild-type P. putida KT2440 and a muconate-producing strain, P. putida CJ781. During cultivation on glucose and 4-hydroxybenzoate, CJ781 showed increased glucose uptake, reconfigured central fluxes, and increased extracellular leakage of aliphatic acids relative to wild type. These altered fluxes supported a 3-fold higher ATP pool, in excess of demand. Pyruvate and acetate secretion in CJ781 was mitigated by debottlenecking TCA-cycle entry via citrate synthase overexpression. Furthermore, tuned expression of the catechol dioxygenase and protocatechuate decarboxylase enabled the production of 36.3 g L-1 muconate at 1.1 g L-1 h-1. Overall, this work reveals how P. putida redirects carbon and energy fluxes to support aromatic bioconversion for improved bioproduction from renewable feedstocks.

synthetic biology↗

bioRxiv2025

BindPred: A Framework for Predicting Protein-Protein Binding Affinity from Language Model Embeddings

MotivationReliable predictions of protein-protein binding affinities are essential for molecular biology and therapeutic discovery. However, most computational methods rely on three-dimensional structural models, which are often unavailable for many complexes. ResultsWe introduce BindPred, a structure-agnostic input framework that predicts affinities directly from amino acid sequences by combining embeddings from large protein language models with gradient boosting trees. On the PPB-Affinity benchmark, which comprises 11,919 diverse complexes, BindPred achieves a Pearson correlation coefficient of 0.86 in random split five-fold cross-validation, where the training and test sets share <30% global sequence identity. Ablation analysis indicates that evolutionary embeddings alone capture most of the predictive signals, while augmenting with physics-based energy terms from PyRosetta and BindCraft increases the correlation by only 0.01. A more stringent protein-level split that places entire protein families (wild-type and all mutants) exclusively in either training or testing sets, results in only a modest decline in performance, demonstrating robust generalization to novel interaction pairs. Because BindPred operates exclusively on sequence input, it enables rapid inference (approximately 3 million complexes per GPU (T4) hour), making proteome-scale screening computationally feasible. AvailabilityThe pretrained model and inference pipeline are available in a Google Colab notebook: BindPred Colab notebook. The training dataset, code, and model weights are available on the Hugging Face: BindPred Contactcostas@psu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2025

MechFind: A computational framework for de novo prediction of enzyme mechanisms

Despite the importance of understanding the step-by-step mechanism of enzymatically catalyzed reactions, fewer than one thousand cataloged mechanistic annotations can be found in the open literature and databases. Herein, we introduce MechFind, a computational tool that generates elementally and charge-balanced putative enzyme mechanisms. Unlike previous methods that require structural data or user-supplied active site residues, MechFind uses only the overall reaction stoichiometry as input, abstracting individual reaction steps as the gain or loss of chemical moieties. An optimization framework then identifies the top ten most parsimonious (i.e., fewest steps) mechanistic descriptions for the overall transformation, which are then re-ranked based on their mechanistic similarity to a database of known, curated mechanisms. MechFind recovered the correct mechanism for 72% of the Mechanism and Catalytic Site Atlas (M-CSA) training dataset within the top ten predictions and was independently validated on six enzyme mechanisms absent from the training set. When deployed at scale on 28,412 reactions from the Rhea database, MechFind identified a plausible mechanism for 64% of all entries, generating over 18,000 novel mechanistic hypotheses expanding significantly upon the current state of the art in prediction (i.e., a 20-fold increase). By proposing detailed reaction mechanisms MechFind provides information that can be leveraged by modern ML-based de novo protein design tools, offering a resource for improving functional annotation and accelerating the engineering of novel biocatalysts. All codes, curated datasets, and results are available at https://github.com/maranasgroup/MechFind.

biochemistry↗

bioRxiv2025

Parameterization of cell-free systems with time-series data using KETCHUP

Kinetic models mechanistically link enzyme levels, metabolite concentrations, and allosteric regulation to metabolic reaction fluxes. This coupling allows for the quantitative elucidation of the dynamics of the evolution of metabolite concentrations and metabolic fluxes as a function of time. So far, most large-scale kinetic model parameterizations are carried out using mostly steady-state flux measurements supplemented with metabolomics and/or proteomics data when available. Even though the parameterized kinetic model can trace a temporal evolution of the system, lack of anchoring to temporal data reduces confidence in the dynamics predictions. Notably, the simulation of enzymatic cascade reactions requires the full description of the dynamics of the system as a steady-state is not applicable given that all measured metabolite concentrations vary with time. Here we describe how kinetic parameters fitted to the dynamics of single-enzyme assays remain accurate for the simulation of multi-enzyme cell-free systems. Herein, we demonstrate two extensions for the Kinetic Estimation Tool Capturing Heterogeneous datasets Using Pyomo (KETCHUP) software tool for parameterizing a kinetic model of the cell-free kinetics of formate dehydrogenase (FDH) and 2,3-butanediol dehydrogenase (BDH) through the use of time-course data across various initial conditions. An implemented extension of KETCHUP allowing for the reconciliation of measurement time-lag errors present in datasets was used to parameterize multiple datasets. By combining the kinetic parameters identified by the FDH and BDH assays, accurate simulation of the binary FDH-BDH system was achieved. KETCHUP can be accessed at https://github.com/maranasgroup/KETCHUP. Author SummaryMetabolic engineering of microorganisms offers a sustainable and renewable alternative for industrial scale production of commodity chemicals. Large-scale metabolic stoichiometric models enable means to elucidate an organisms physiological behavior towards both environment and genetic perturbations and predict optimal interventions to maximize production. Kinetic models extend stoichiometric models capabilities in characterizing metabolic phenotypes by mathematically linking enzyme catalyzed reactions in metabolic networks as functions of metabolite concentrations, enzyme levels, and allosteric regulations. Although these kinetic descriptions improve predictive accuracy and strain design, the lack of enzyme information, raw experimental data, and high computational cost preclude the construction and parameterization of detailed large-scale kinetic models. Here, we introduce an extension of KETCHUP, an open-source semi-automatic kinetic construction and parameterization tool that utilizes steady-state data as a pilot for the parameterization of single-enzyme cell-free time-course data. We show that this tool offers comparable performance to existing tools during parameterization of single enzymes and successful recapitulation of metabolite profiles for a two-enzyme system when simulating a kinetic model with parameters from the two single-enzymes. This study showcases a high throughput pipeline that can be integrated into our framework for larger scaled networks.

systems biology↗

bioRxiv2024

novoStoic2.0: An integrated framework for pathway synthesis, thermodynamic evaluation, and enzyme selection

Computational pathway design and retro-biosynthetic approaches can facilitate the development of innovative biochemical production routes, biodegradation strategies, and the funneling of multiple precursors into a single bioproduct. However, effective pathway design necessitates a comprehensive understanding of biochemistries, enzyme activities, and thermodynamic feasibility. Herein, we introduce novoStoic2.0, an integrated platform that combines tools for estimating overall stoichiometry, designing de novo synthesis pathways, assessing thermodynamic feasibility, and selecting enzymes. novoStoic2.0 offers a unified web-based interface as a part of the AlphaSynthesis platform (http://novostoic.platform.moleculemaker.org/) tailored for the synthesis of thermodynamically viable pathways as well as the selection of enzymes for re-engineering required for novel reaction steps. We exemplify the utility of the platform to identify novel pathways for hydroxytyrosol synthesis, which are shorter than the known pathways and require reduced cofactor usage. In summary, novoStoic2.0 aims to streamline the process of pathway design contributing to the development of sustainable biotechnological solutions.

synthetic biology↗

bioRxiv2024

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

Genome-scale metabolic models (GEMs) and other constraint-based models (CBMs) play a pivotal role in understanding biological phenotypes and advancing research in areas like metabolic engineering, human disease modelling, drug discovery, and personalized medicine. Despite their growing application, a significant challenge remains in ensuring the reproducibility of GEMs, primarily due to inconsistent reporting and inadequate model documentation of model results. Addressing this gap, we introduce FROG analysis, a community-driven initiative aimed at standardizing reproducibility assessments of CBMs and GEMs. The FROG framework encompasses four key analyses--Flux variability, Reaction deletion, Objective function, and Gene deletion--to produce standardized, numerically reproducible FROG reports. These reports serve as reference datasets, enabling model evaluators, curators, and independent researchers to verify the reproducibility of GEMs systematically. BioModels, a leading repository of systems biology models, has integrated FROG analysis into its curation workflow, enhancing the reproducibility and reusability of submitted GEMs. In our study evaluating 65 GEM submissions from the community, approximately 40% reproduced without intervention, 28% requiring minor adjustments, and 32% needing input from authors. The standardization introduced by FROG analysis facilitated the detection and resolution of issues, ultimately leading to the successful reproduction of all models. By establishing a standardized and comprehensive approach to evaluating GEM reproducibility, FROG analysis significantly contributes to making CBMs and GEMs more transparent, reusable, and reliable for the broader scientific community.

systems biology↗

bioRxiv2024

CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki

Quantification of enzymatic activities still heavily relies on experimental assays, which can be expensive and time-consuming. Therefore, methods that enable accurate predictions of enzyme activity can serve as effective digital twins. A few recent studies have shown the possibility of training machine learning (ML) models for predicting the enzyme turnover numbers (kcat) and Michaelis constants (Km) using only features derived from enzyme sequences and substrate chemical topologies by training on in vitro measurements. However, several challenges remain such as lack of standardized training datasets, evaluation of predictive performance on out-of-distribution examples, and model uncertainty quantification. Here, we introduce CatPred, a comprehensive framework for ML prediction of in vitro enzyme kinetics. We explored different learning architectures and feature representations for enzymes including those utilizing pretrained protein language model features and pretrained three-dimensional structural features. We systematically evaluate the performance of trained models for predicting kcat, Km, and inhibition constants (Ki) of enzymatic reactions on held-out test sets with a special emphasis on out-of-distribution test samples (corresponding to enzyme sequences dissimilar from those encountered during training). CatPred assumes a probabilistic regression approach offering query-specific standard deviation and mean value predictions. Results on unseen data confirm that accuracy in enzyme parameter predictions made by CatPred positively correlate with lower predicted variances. Incorporating pre-trained language model features is found to be enabling for achieving robust performance on out-of-distribution samples. Test evaluations on both held-out and out-of-distribution test datasets confirm that CatPred performs at least competitively with existing methods while simultaneously offering robust uncertainty quantification. CatPred offers wider scope and larger data coverage ([~]23k, 41k, 12k data-points respectively for kcat, Km and Ki). A web-resource to use the trained models is made available at: https://tiny.cc/catpred

bioinformatics↗

bioRxiv2024

Genome streamlining to improve performance of a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973

Cyanobacteria are photosynthetic organisms that have garnered significant recognition as potential hosts for sustainable bioproduction. However, their complex regulatory networks pose significant challenges to major metabolic engineering efforts, thereby limiting their feasibility as production hosts. Genome streamlining has been demonstrated to be a successful approach for improving productivity and fitness in heterotrophs but is yet to be explored to its full potential in phototrophs. Here we present the systematic reduction of the genome of the cyanobacterium exhibiting the fastest exponential growth, Synechococcus elongatus UTEX 2973. This work, the first of its kind in a photoautotroph, involved an iterative process using state-of-the-art genome-editing technology guided by experimental analysis and computational tools. CRISPR/Cas3 enabled large, progressive deletions of predicted dispensable regions and aided in the identification of essential genes. The large deletions were combined to obtain a strain with 55 kb genome reduction. The strains with streamlined genome showed improvement in growth (up to 23%) and productivity (by 22.7%) as compared to the WT. This streamlining strategy not only has the potential to develop cyanobacterial strains with improved growth and productivity traits but can also facilitate a better understanding of their genome to phenome relationships. ImportanceGenome streamlining is an evolutionary strategy used by natural living systems to dispense unnecessary genes from their genome as a mechanism to adapt and evolve. While this strategy has been successfully borrowed to develop synthetic heterotrophic microbial systems with desired phenotype, it has not been extensively explored in photoautotrophs. Genome streamlining strategy incorporates both computational predictions to identify the dispensable regions and experimental validation using genome editing tool and in this study we have employed a modified strategy with the goal to minimize the genome size to an extent that allows optimal cellular fitness under specified conditions. Our strategy has explored a novel genome-editing tool in photoautotrophs which, unlike other existing tools, enables large, spontaneous optimal deletions from the genome. Our findings demonstrate the effectiveness of this modified strategy in obtaining strains with streamlined genome, exhibiting improved fitness and productivity.

synthetic biology↗

bioRxiv2023

Multi-organ Metabolic Model of Zea mays Connects Temperature Stress with Thermodynamics-Reducing Power-Energy Generation Axis

Global climate change has severely impacted maize productivity. A holistic understanding of metabolic crosstalk among its organs is essential to address this issue. Thus, we reconstructed the first multi-organ maize genome-scale metabolic model, iZMA6517, and contextualized it with heat and cold stress-related transcriptomics data using the novel EXpression disTributed REAction flux Measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed fundamental differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. To tie these signatures, we performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, for global food security, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof of concept demonstrated its potential to alleviate temperature stress. In summary, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.

plant biology↗

bioRxiv2022

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (kapp) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

systems biology↗

bioRxiv2022

Proteome capacity constraints favor respiratory ATP generation

Cells face competing metabolic demands. These include efficient use of both limited substrates and limited proteome capacity, as well as flexibility to deal with different environments. Flexibility requires spare enzyme capacity, which is proteome inefficient. ATP generation can occur via fermentation or respiration. Fermentation is much less substrate-efficient, but often assumed to be more proteome efficient 1-3, thereby favoring fast-growing cells engaging in aerobic glycolysis 4-8. Here, however, we show that mitochondrial respiration is actually more proteome-efficient than aerobic glycolysis. Instead, aerobic glycolysis arises from cells maintaining the flexibility to grow also anaerobically. These conclusions emerged from an unbiased assessment of metabolic regulatory mechanisms, integrating quantitative metabolomics, proteomics, and fluxomics, of two budding yeasts, Saccharomyces cerevisiae and Issatchenkia orientalis, the former more fermentative and the latter respiratory. Their energy pathway usage is largely explained by differences in proteome allocation. Each organisms proteome allocation is remarkably stable across environmental conditions, with metabolic fluxes predominantly regulated at the level of metabolite concentrations. This leaves extensive spare biosynthetic capacity during slow growth and spare capacity of their preferred bioenergetic machinery when it is not essential. The greater proteome-efficiency of respiration is also observed in mammals, with aerobic glycolysis occurring in yeast or mammalian cells that maintain a fermentation-capable proteome conducive to both aerobic and anaerobic growth.

systems biology↗

bioRxiv2022

A CNN model for predicting binding affinity changes between SARS-CoV-2 spike RBD variants and ACE2 homologues

The cellular entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the association of its receptor binding domain (RBD) with human angiotensin converting enzyme 2 (hACE2) as the first crucial step. Efficient and reliable prediction of RBD-hACE2 binding affinity changes upon amino acid substitutions can be valuable for public health surveillance and monitoring potential spillover and adaptation into non-human species. Here, we introduce a convolutional neural network (CNN) model trained on protein sequence and structural features to predict experimental RBD-hACE2 binding affinities of 8,440 variants upon single and multiple amino acid substitutions in the RBD or ACE2. The model achieves a classification accuracy of 83.28% and a Pearson correlation coefficient of 0.85 between predicted and experimentally calculated binding affinities in five-fold cross-validation tests and predicts improved binding affinity for most circulating variants. We pro-actively used the CNN model to exhaustively screen for novel RBD variants with combinations of up to four single amino acid substitutions and suggested candidates with the highest improvements in RBD-ACE2 binding affinity for human and animal ACE2 receptors. We found that the binding affinity of RBD variants against animal ACE2s follows similar trends as those against human ACE2. White-tailed deer ACE2 binds to RBD almost as tightly as human ACE2 while cattle, pig, and chicken ACE2s bind weakly. The model allows testing whether adaptation of the virus for increased binding with other animals would cause concomitant increases in binding with hACE2 or decreased fitness due to adaptation to other hosts.

biophysics↗

bioRxiv2021

Multiple spillovers and onward transmission of SARS-Cov-2 in free-living and captive White-tailed deer (Odocoileus virginianus)

Many animal species are susceptible to SARS-CoV-2 and could potentially act as reservoirs, yet transmission of the virus in non-human free-living animals has not been documented. White-tailed deer (Odocoileus virginianus), the predominant cervid in North America, are susceptible to SARS-CoV-2 infection, and experimentally infected fawns can transmit the virus. To test the hypothesis that SARS-CoV-2 may be circulating in deer, we tested 283 retropharyngeal lymph node (RPLN) samples collected from 151 free-living and 132 captive deer in Iowa from April 2020 through December of 2020 for the presence of SARS-CoV-2 RNA. Ninety-four of the 283 deer (33.2%; 95% CI: 28, 38.9) samples were positive for SARS-CoV-2 RNA as assessed by RT-PCR. Notably, between November 23, 2020 and January 10, 2021, 80 of 97 (82.5%; 95% CI 73.7, 88.8) RPLN samples had detectable SARS-CoV-2 RNA by RT-PCR. Whole genome sequencing of the 94 positive RPLN samples identified 12 SARS-CoV-2 lineages, with B.1.2 (n = 51; 54.5%), and B.1.311 (n = 19; 20%) accounting for ~75% of all samples. The geographic distribution and nesting of clusters of deer and human lineages strongly suggest multiple zooanthroponotic spillover events and deer-to-deer transmission. The discovery of sylvatic and enzootic SARS-CoV-2 transmission in deer has important implications for the ecology and long-term persistence, as well as the potential for spillover to other animals and spillback into humans. These findings highlight an urgent need for a robust and proactive "One Health" approach to obtaining a better understanding of the ecology and evolution of SARS-CoV-2. One-Sentence SummarySARS-CoV-2 was detected in one-third of sampled white-tailed deer in Iowa between September 2020 and January of 2021 that likely resulted from multiple human-to-deer spillover and deer-to-deer transmission events.

microbiology↗

bioRxiv2021

Quantifying the propagation of parametric uncertainty on flux balance analysis

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g/mmol, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

systems biology↗

bioRxiv2021

EnZymClass: Substrate specificity prediction tool of plant acyl-ACP thioesterases based on Ensemble Learning

Classification of proteins into their respective functional categories remains a long-standing key challenge in computational biology. Machine Learning (ML) based discriminative algorithms have been used extensively to address this challenge; however, the presence of small-sized, noisy, unbalanced protein classification datasets where high sequence similarity does not always imply identical functional properties have prevented robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. EnZymClass makes use of 47 alignment-free feature extraction techniques as numerically encoded descriptors of protein sequences to construct a stacked ensemble classification scheme capable of categorizing proteins based on their functional attributes. We used EnZymClass to classify plant acyl-ACP thioesterases (TEs) into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive. EnZymClass achieved high classification metric scores on the TE substrate specificity prediction task (average accuracy score of 0.8, average precision and recall scores of 0.87 and 0.89 respectively on medium-chain TE prediction) producing accuracy scores that are about twice as effective at avoiding misclassifications than existing similarity-based methods of substrate specificity prediction. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two acyl-ACP TE, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts. We incorporated modifications into ClFatB3 established in prior TE engineering studies, resulting in a 4.2-fold overall improvement in observed C10 titers over the wildtype enzyme. EnZymClass can be readily applied to other protein classification challenges and is available at: https://github.com/deeprob/ThioesteraseEnzymeSpecificity Author SummaryThe natural diversity of proteins has been harnessed to serve specialized applications in various fields, including medicine, renewable chemical production, and food and agriculture. Acquiring and characterizing new proteins to meet a given application, however, can be an expensive process, requiring selection from thousands to hundreds of thousands of candidates in a database and subsequent experimental screening. Using amino acid sequence to predict a proteins function has been demonstrated to accelerate this process, however standard approaches require information on previously characterized proteins and their respective sequences. Obtaining the necessary amount of data to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Here, we present EnZymClass, a model that is specifically designed to work with small to medium-sized protein sequence datasets and retain high prediction performance of function. We applied EnZymClass to predict the presence or absence of a desired function among acyl-ACP thioesterases, a key enzyme class used in the production of renewable oleochemicals in microbial hosts. By training EnZymClass on only 115 functionally characterized enzyme sequences, we were able to successfully detect two plant acyl-ACP thioesterases with the desired specialized function among 617 sequences in the ThYme database.

bioinformatics↗

bioRxiv2021

Dissecting the Metabolic Reprogramming of Maize Root under Nitrogen Limiting Stress Condition

The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N-) and nitrogen-deficient (N-) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N-. Overall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes. SummaryO_LIThe growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. C_LIO_LIA genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen-stress condition. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. C_LIO_LITranscriptomics data derived from the roots of hydroponically grown maize plants was used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N-) conditions. Model-predicted result achieved 70% accuracy comparing to the experimental direction change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (e.g., L-methionine, L-asparagine, L-lysine, cholesterol, and L-pipecolate) playing critical regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidyl-choline and phosphatidyl-glycerol metabolites which even though not coupled with biomass production played a key role in the increased biomass production under N-. C_LIO_LIOverall, the omics-integrated-GSM provides a promising tool to facilitate stress-condition analysis for maize root and ultimately engineer better stress-tolerant maize genotypes. C_LI

systems biology↗