REME: an integrated platform for reaction enzyme mining and evaluation.

A key challenge in pathway design is finding proper enzymes that can be engineered to catalyze a non-natural reaction. Although existing tools can identify potential enzymes based on similar reactions, these tools encounter several issues. Firstly, the calculated similar reactions may not even have the same reaction type. Secondly, the associated enzymes are often numerous and identifying the most promising candidate enzymes is difficult due to the lack of data for evaluation. Thirdly, existing web tools do not provide interactive functions that enable users to fine-tune results based on their expertise. Here, we present REME (https://reme.biodesign.ac.cn/), the first integrated web platform for reaction enzyme mining and evaluation. Combining atom-to-atom mapping, atom type change identification, and reaction similarity calculation enables quick ranking and visualization of reactions similar to an objective non-natural reaction. Additional functionality enables users to filter similar reactions by their specified functional groups and candidate enzymes can be further filtered (e.g. by organisms) or expanded by Enzyme Commission number (EC) or sequence homology. Afterward, enzyme attributes (such as kcat, Km, optimal temperature and pH) can be assessed with deep learning-based methods, facilitating the swift identification of potential enzymes that can catalyze the non-natural reaction.

CAVE: a cloud-based platform for analysis and visualization of metabolic pathways.

Flux balance analysis (FBA) is an important method for calculating optimal pathways to produce industrially important chemicals in genome-scale metabolic models (GEMs). However, for biologists, the requirement of coding skills poses a significant obstacle to using FBA for pathway analysis and engineering target identification. Additionally, a time-consuming manual drawing process is often needed to illustrate the mass flow in an FBA-calculated pathway, making it challenging to detect errors or discover interesting metabolic features. To solve this problem, we developed CAVE, a cloud-based platform for the integrated calculation, visualization, examination and correction of metabolic pathways. CAVE can analyze and visualize pathways for over 100 published GEMs or user-uploaded GEMs, allowing for quicker examination and identification of special metabolic features in a particular GEM. Additionally, CAVE offers model modification functions, such as gene/reaction removal or addition, making it easy for users to correct errors found in pathway analysis and obtain more reliable pathways. With a focus on the design and analysis of optimal pathways for biochemicals, CAVE complements existing visualization tools based on manually drawn global maps and can be applied to a broader range of organisms for rational metabolic engineering. CAVE is available at https://cave.biodesign.ac.cn/.

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based kcat data.

BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have emerged as a valuable advancement, providing more accurate predictions and unveiling new engineering targets compared to models lacking enzyme constraints. In 2022, a stoichiometric GEM, iDL1450, was reconstructed for the industrially significant fungus Myceliophthora thermophila. To enhance the GEM's performance, an ecGEM was developed for M. thermophila in this study. RESULTS: Initially, the model iDL1450 underwent refinement and updates, resulting in a new version named iYW1475. These updates included adjustments to biomass components, correction of gene-protein-reaction (GPR) rules, and a consensus on metabolites. Subsequently, the first ecGEM for M. thermophila was constructed using machine learning-based kcat data predicted by TurNuP within the ECMpy framework. During the construction, three versions of ecGEMs were developed based on three distinct kcat collection methods, namely AutoPACMEN, DLKcat and TurNuP. After comparison, the ecGEM constructed using TurNuP-predicted kcat values performed better in several aspects and was selected as the definitive version of ecGEM for M. thermophila (ecMTM). Comparing ecMTM to iYW1475, the solution space was reduced and the growth simulation results more closely resembled realistic cellular phenotypes. Metabolic adjustment simulated by ecMTM revealed a trade-off between biomass yield and enzyme usage efficiency at varying glucose uptake rates. Notably, hierarchical utilization of five carbon sources derived from plant biomass hydrolysis was accurately captured and explained by ecMTM. Furthermore, based on enzyme cost considerations, ecMTM successfully predicted reported targets for metabolic engineering modification and introduced some new potential targets for chemicals produced in M. thermophila. CONCLUSIONS: In this study, the incorporation of enzyme constraint to iYW1475 not only improved prediction accuracy but also broadened the model's applicability. This research demonstrates the effectiveness of integrating of machine learning-based kcat data in the construction of ecGEMs especially in situations where there is limited measured enzyme kinetic parameters for a specific organism.

ERMer: a serverless platform for navigating, analyzing, and visualizing Escherichia coli regulatory landscape through graph database.

Cellular regulation is inherently complex, and one particular cellular function is often controlled by a cascade of different types of regulatory interactions. For example, the activity of a transcription factor (TF), which regulates the expression level of downstream genes through transcriptional regulation, can be regulated by small molecules through compound-protein interactions. To identify such complex regulatory cascades, traditional relational databases require ineffective additional operations and are computationally expensive. In contrast, graph databases are purposefully developed to execute such deep searches efficiently. Here, we present ERMer (E. coli Regulation Miner), the first cloud platform for mining the regulatory landscape of Escherichia coli based on graph databases. Combining the AWS Neptune graph database, AWS lambda function, and G6 graph visualization engine enables quick search and visualization of complex regulatory cascades/patterns. Users can also interactively navigate the E. coli regulatory landscape through ERMer. Furthermore, a Q&A module is included to showcase the power of graph databases in answering complex biological questions through simple queries. The backend graph model can be easily extended as new data become available. In addition, the framework implemented in ERMer can be easily migrated to other applications or organisms. ERMer is available at https://ermer.biodesign.ac.cn/.

AutoESD: a web tool for automatic editing sequence design for genetic manipulation of microorganisms.

Advances in genetic manipulation and genome engineering techniques have enabled on-demand targeted deletion, insertion, and substitution of DNA sequences. One important step in these techniques is the design of editing sequences (e.g. primers, homologous arms) to precisely target and manipulate DNA sequences of interest. Experimental biologists can employ multiple tools in a stepwise manner to assist editing sequence design (ESD), but this requires various software involving non-standardized data exchange and input/output formats. Moreover, necessary quality control steps might be overlooked by non-expert users. This approach is low-throughput and can be error-prone, which illustrates the need for an automated ESD system. In this paper, we introduce AutoESD (https://autoesd.biodesign.ac.cn/), which designs editing sequences for all steps of genetic manipulation of many common homologous-recombination techniques based on screening-markers. Notably, multiple types of manipulations for different targets (CDS or intergenic region) can be processed in one submission. Moreover, AutoESD has an entirely cloud-based serverless architecture, offering high reliability, robustness and scalability which is capable of parallelly processing hundreds of design tasks each having thousands of targets in minutes. To our knowledge, AutoESD is the first cloud platform enabling precise, automated, and high-throughput ESD across species, at any genomic locus for all manipulation types.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

DeepSub: Utilizing Deep Learning for Predicting the Number of Subunits in Homo-Oligomeric Protein Complexes.

The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from Corynebacterium glutamicum, Matrilin-4 from Mus musculus and Homo sapiens, and the Multimerins protein family from M. musculus and H. sapiens. The predicted results align closely with the reported findings in the literature, underscoring the reliability and utility of DeepSub.

Insight into de-regulation of amino acid feedback inhibition: a focus on structure analysis method.

Regulation of amino acid's biosynthetic pathway is of significant importance to maintain homeostasis and cell functions. Amino acids regulate their biosynthetic pathway by end-product feedback inhibition of enzymes catalyzing committed steps of a pathway. Discovery of new feedback resistant enzyme variants to enhance industrial production of amino acids is a key objective in industrial biotechnology. Deregulation of feedback inhibition has been achieved for various enzymes using in vitro and in silico mutagenesis techniques. As enzyme's function, its substrate binding capacity, catalysis activity, regulation and stability are dependent on its structural characteristics, here, we provide detailed structural analysis of all feedback sensitive enzyme targets in amino acid biosynthetic pathways. Current review summarizes information regarding structural characteristics of various enzyme targets and effect of mutations on their structures and functions especially in terms of deregulation of feedback inhibition. Furthermore, applicability of various experimental as well as computational mutagenesis techniques to accomplish feedback resistance has also been discussed in detail to have an insight into various aspects of research work reported in this particular field of study.

Complete Depolymerization of PET Wastes by an Evolved PET Hydrolase from Directed Evolution.

PETase displays great potential in PET depolymerization. Directed evolution has been limited to engineer PETase due to the lack of high-throughput screening assay. In this study, a novel fluorescence-based high-throughput screening assay employing a newly designed substrate, bis (2-hydroxyethyl) 2-hydroxyterephthalate (termed BHET-OH), was developed for PET hydrolases. The best variant DepoPETase produced 1407-fold more products towards amorphous PET film at 50 °C and showed a 23.3 °C higher Tm value than the PETase WT. DepoPETase enabled complete depolymerization of seven untreated PET wastes and 19.1 g PET waste (0.4 % Wenzyme /WPET ) in liter-scale reactor, suggesting that it is a potential candidate for industrial PET depolymerization processes. The molecular dynamic simulations revealed that the distal substitutions stabilized the loops around the active sites and transmitted the stabilization effect to the active sites through enhancing inter-loop interactions network.

Global Cellular Metabolic Rewiring Adapts Corynebacterium glutamicum to Efficient Nonnatural Xylose Utilization.

Xylose, the major component of lignocellulosic biomass, cannot be naturally or efficiently utilized by most microorganisms. Xylose (co)utilization is considered a cornerstone of efficient lignocellulose-based biomanufacturing. We evolved a rapidly xylose-utilizing strain, Cev2-18-5, which showed the highest reported specific growth rate (0.357 h-1) on xylose among plasmid-free Corynebacterium glutamicum strains. A genetically clear chassis strain, CGS15, was correspondingly reconstructed with an efficient glucose-xylose coutilization performance based on comparative genomic analysis and mutation reconstruction. With the introduction of a succinate-producing plasmid, the resulting strain, CGS15-SA1, can efficiently produce 97.1 g/L of succinate with an average productivity of 8.09 g/L/h by simultaneously utilizing glucose and xylose from corn stalk hydrolysate. We further revealed a novel xylose regulatory mechanism mediated by the endogenous transcription factor IpsA with global regulatory effects on C. glutamicum. A synergistic effect on carbon metabolism and energy supply, motivated by three genomic mutations (Psod(C131T)-xylAB, Ptuf(Δ21)-araE, and ipsAC331T), was found to endow C. glutamicum with the efficient xylose utilization and rapid growth phenotype. Overall, this work not only provides promising C. glutamicum chassis strains for a lignocellulosic biorefinery but also enriches the understanding of the xylose regulatory mechanism. IMPORTANCE A novel xylose regulatory mechanism mediated by the transcription factor IpsA was revealed. A synergistic effect on carbon metabolism and energy supply was found to endow C. glutamicum with the efficient xylose utilization and rapid growth phenotype. The new xylose regulatory mechanism enriches the understanding of nonnatural substrate metabolism and encourages exploration new engineering targets for rapid xylose utilization. This work also provides a paradigm to understand and engineer the metabolism of nonnatural renewable substrates for sustainable biomanufacturing.

Reconstruction and analysis of genome-scale metabolic model for thermophilic fungus Myceliophthora thermophila.

Myceliophthora thermophila, a thermophilic fungus that can degrade and utilize all major polysaccharides in plant biomass, has great potential in biotechnological industries. Here, the first manually curated genome-scale metabolic model iDL1450 for M. thermophila was reconstructed using an autogenerating pipeline with thorough manual curation. The model contains 1450 genes, 2592 reactions, and 1784 unique metabolites. High accuracy was shown in predictions related to carbon and nitrogen source utilization based on data obtained from Biolog experiments. Besides, metabolism profiles were analyzed using iDL1450 integrated with transcriptomics data of M. thermophila at various growth temperatures. The refined model provides new insights into thermophilic fungi metabolism and sheds light on model-driven strain design to improve biotechnological applications of this thermophilic lignocellulosic fungus.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models.

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.

Global connectivity in genome-scale metabolic networks revealed by comprehensive FBA-based pathway analysis.

BACKGROUND: Graph-based analysis (GBA) of genome-scale metabolic networks has revealed system-level structures such as the bow-tie connectivity that describes the overall mass flow in a network. However, many pathways obtained by GBA are biologically impossible, making it difficult to study how the global structures affect the biological functions of a network. New method that can calculate the biologically relevant pathways is desirable for structural analysis of metabolic networks. RESULTS: Here, we present a new method to determine the bow-tie connectivity structure by calculating possible pathways between any pairs of metabolites in the metabolic network using a flux balance analysis (FBA) approach to ensure that the obtained pathways are biologically relevant. We tested this method with 15 selected high-quality genome-scale metabolic models from BiGG database. The results confirmed the key roles of central metabolites in network connectivity, locating in the core part of the bow-tie structure, the giant strongly connected component (GSC). However, the sizes of GSCs revealed by GBA are significantly larger than those by FBA approach. A great number of metabolites in the GSC from GBA actually cannot be produced from or converted to other metabolites through a mass balanced pathway and thus should not be in GSC but in other subsets of the bow-tie structure. In contrast, the bow-tie structural classification of metabolites obtained by FBA is more biologically relevant and suitable for the study of the structure-function relationships of genome scale metabolic networks. CONCLUSIONS: The FBA based pathway calculation improve the biologically relevant classification of metabolites in the bow-tie connectivity structure of the metabolic network, taking us one step further toward understanding how such system-level structures impact the biological functions of an organism.

Analyzing the genetic characteristics of a tryptophan-overproducing Escherichia coli.

L-tryptophan (L-trp) production in Escherichia coli has been developed by employing random mutagenesis and selection for a long time, but this approach produces an unclear genetic background. Here, we generated the L-trp overproducer TPD5 by combining an intracellular L-trp biosensor and fluorescence-activated cell sorting (FACS) in E. coli, and succeeded in elucidating the genetic basis for L-trp overproduction. The most significant identified positive mutations affected TnaA (deletion), AroG (S211F), TrpE (A63V), and RpoS (nonsense mutation Q33*). The underlying structure-function relationships of the feedback-resistant AroG (S211F) and TrpE (A63V) mutants were uncovered based on protein structure modeling and molecular dynamics simulations, respectively. According to transcriptomic analysis, the global regulator RpoS not only has a great influence on cell growth and morphology, but also on carbon utilization and the direction of carbon flow. Finally, by balancing the concentrations of the L-trp precursors' serine and glutamine based on the above analysis, we further increased the titer of L-trp to 3.18 g/L with a yield of 0.18 g/g. The analysis of the genetic characteristics of an L-trp overproducing E. coli provides valuable information on L-trp synthesis and elucidates the phenotype and complex cellular properties in a high-yielding strain, which opens the possibility to transfer beneficial mutations and reconstruct an overproducer with a clean genetic background.

Advances in biotechnological production of ß-alanine.

ß-Alanine (3-aminopropionic acid) is the only naturally occurring ß-type amino acid. Although it is not incorporated into proteins, it has important physiological functions in the metabolism of animals, plants and microorganisms. Furthermore, it has attracted great interest due to its wide usage as a precursor of many significant industrial chemicals for medicine, feed, food, environmental applications and other fields. With the depletion of fossil fuels and concerns regarding environmental issues, biological production of ß-alanine has attracted more attention relative to chemical methods. In this review, we first summarize the pathways through which natural microorganisms synthesize ß-alanine. Then, the current research progress in the biological synthesis of ß-alanine is also elaborated. Finally, we discuss the main problems and challenges in optimizing the biological pathways, offering perspectives on promising new biological approaches.

Engineering central pathways for industrial-level (3R)-acetoin biosynthesis in Corynebacterium glutamicum.

BACKGROUND: Acetoin, especially the optically pure (3S)- or (3R)-enantiomer, is a high-value-added bio-based platform chemical and important potential pharmaceutical intermediate. Over the past decades, intense efforts have been devoted to the production of acetoin through green biotechniques. However, efficient and economical methods for the production of optically pure acetoin enantiomers are rarely reported. Previously, we systematically engineered the GRAS microorganism Corynebacterium glutamicum to efficiently produce (3R)-acetoin from glucose. Nevertheless, its yield and average productivity were still unsatisfactory for industrial bioprocesses. RESULTS: In this study, cellular carbon fluxes in the acetoin producer CGR6 were further redirected toward acetoin synthesis using several metabolic engineering strategies, including blocking anaplerotic pathways, attenuating key genes of the TCA cycle and integrating additional copies of the alsSD operon into the genome. Among them, the combination of attenuation of citrate synthase and inactivation of phosphoenolpyruvate carboxylase showed a significant synergistic effect on acetoin production. Finally, the optimal engineered strain CGS11 produced a titer of 102.45 g/L acetoin with a yield of 0.419 g/g glucose at a rate of 1.86 g/L/h in a 5 L fermenter. The optical purity of the resulting (3R)-acetoin surpassed 95%. CONCLUSION: To the best of our knowledge, this is the highest titer of highly enantiomerically enriched (3R)-acetoin, together with a competitive product yield and productivity, achieved in a simple, green processes without expensive additives or substrates. This process therefore opens the possibility to achieve easy, efficient, economical and environmentally-friendly production of (3R)-acetoin via microbial fermentation in the near future.

Recent progress in metabolic engineering of microbial formate assimilation.

Formate can be efficiently produced via electrochemical or photochemical catalytic conversion of CO2, and it can be directly used as an organic carbon source by microorganisms. In theory, formate can be used as the sole carbon source for the microbial production of high-value-added chemicals. Consequently, the construction of efficient formate-assimilation pathways in microorganisms is essential for the utilization of cheap, renewable one-carbon compounds. This paper summarizes new methods of formate synthesis, as well as the natural formate utilization pathways of microorganisms with their advantages and disadvantages. Furthermore, it reviews recent progress in the design of utilization pathways for formate in microbial cells through metabolic engineering and synthetic biology. Besides, we also use the pathway-prediction algorithm comb-FBA to rationally design completely new one-carbon compounds utilization pathways. The pathway with the highest efficiency, named GAA, was corroborated by the in vitro experiments showing a carbon molar yield up to 88%. Finally, it discusses the main problems and challenges presently existing in the pathway design and strain improvement for microbial utilization of formate. KEY POINTS: • Natural and artificial design pathways of formate-assimilation was summarized. • Recent progresses in different hosts and approaches of using one-carbon compounds was reviewed. • Metabolic engineering and synthetic biology methods to improve formate utilization were discussed.

Systematic design and in vitro validation of novel one-carbon assimilation pathways.

The utilization of one-carbon (C1) assimilation pathways to produce chemicals and fuels from low-cost C1 compounds could greatly reduce the substrate-related production costs, and would also alleviate the pressure of the resource supply for bio-manufacturing. However, the natural C1 assimilation pathways normally involve ATP consumption or the loss of carbon resources as CO2, resulting in low product yields, making the design of novel pathways highly pertinent. Here we present several new ATP-independent and carbon-conserving C1 assimilation cycles with 100% theoretical carbon yield, which were discovered by computational analysis of metabolic reaction set with 6578 natural reactions from MetaCyc database and 73 computationally predicted aldolase reactions from ATLAS database. Then, kinetic evaluation of these cycles was conducted and the cycles without kinetic traps were chosen for further experimental verification. Finally, we used the two engineered enzymes Gals and TalBF178Y for the artificial reactions to construct a novel C1 assimilation pathway in vitro and optimized the pathway to achieve 88% carbon yield. These results demonstrate the usefulness of computational design in finding novel metabolic pathways for the efficient utilization of C1 compounds and shedding light on other promising pathways.

Expanding targeting scope, editing window, and base transition capability of base editing in Corynebacterium glutamicum.

CRISPR/Cas9-guided cytidine deaminase enables C:G to T:A base editing in bacterial genome without introduction of lethal double-stranded DNA break, supplement of foreign DNA template, or dependence on inefficient homologous recombination. However, limited by genome-targeting scope, editing window, and base transition capability, the application of base editing in metabolic engineering has not been explored. Herein, four Cas9 variants accepting different protospacer adjacent motif (PAM) sequences were used to increase the genome-targeting scope of bacterial base editing. After a comprehensive evaluation, we demonstrated that PAM requirement of bacterial base editing can be relaxed from NGG to NG using the Cas9 variants, providing 3.9-fold more target loci for gene inactivation in Corynebacterium glutamicum. Truncated or extended guide RNAs were employed to expand the canonical 5-bp editing window to 7-bp. Bacterial adenine base editing was also achieved with Cas9 fused to adenosine deaminase. With these updates, base editing can serve as an enabling tool for fast metabolic engineering. To demonstrate its potential, base editing was used to deregulate feedback inhibition of aspartokinase via amino acid substitution for lysine overproduction. Finally, a user-friendly online tool named gBIG was provided for designing guide RNAs for base editing-mediated inactivation of given genes in any given sequenced genome (www.ibiodesign.net/gBIG).

iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis.

BACKGROUND: Constraint-based metabolic network models have been widely used in pheno-typic prediction and metabolic engineering design. In recent years, researchers have attempted to im-prove prediction accuracy by integrating regulatory information and multiple types of "omics" data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed. METHODS AND RESULTS: We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction bounda-ries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods. CONCLUSION: Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise pre-dictions of metabolic fluxes by using expression values normalized based on GO.