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.

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.

Omics data analysis reveals the system-level constraint on cellular amino acid composition.

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.

Trojan horselike T6SS effector TepC mediates both interference competition and exploitative competition.

The type VI secretion system (T6SS) is a bacterial weapon capable of delivering antibacterial effectors to kill competing cells for interference competition, as well as secreting metal ion scavenging effectors to acquire essential micronutrients for exploitation competition. However, no T6SS effectors that can mediate both interference competition and exploitation competition have been reported. In this study, we identified a unique T6SS-1 effector in Yersinia pseudotuberculosis named TepC, which plays versatile roles in microbial communities. First, secreted TepC acts as a proteinaceous siderophore that binds to iron and mediates exploitative competition. Additionally, we discovered that TepC has DNase activity, which gives it both contact-dependent and contact-independent interference competition abilities. In conditions where iron is limited, the iron-loaded TepC is taken up by target cells expressing the outer membrane receptor TdsR. For kin cells encoding the cognate immunity protein TipC, TepC facilitates iron acquisition, and its toxic effects are neutralized. On the other hand, nonkin cells lacking TipC are enticed to uptake TepC and are killed by its DNase activity. Therefore, we have uncovered a T6SS effector, TepC, that functions like a "Trojan horse" by binding to iron ions to provide a valuable resource to kin cells, whereas punishing cheaters that do not produce public goods. This lure-to-kill mechanism, mediated by a bifunctional T6SS effector, may offer new insights into the molecular mechanisms that maintain stability in microbial communities.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501.

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Enzyme Commission Number Prediction and Benchmarking with Hierarchical Dual-core Multitask Learning Framework.

Enzyme commission (EC) numbers, which associate a protein sequence with the biochemical reactions it catalyzes, are essential for the accurate understanding of enzyme functions and cellular metabolism. Many ab initio computational approaches were proposed to predict EC numbers for given input protein sequences. However, the prediction performance (accuracy, recall, and precision), usability, and efficiency of existing methods decreased seriously when dealing with recently discovered proteins, thus still having much room to be improved. Here, we report HDMLF, a hierarchical dual-core multitask learning framework for accurately predicting EC numbers based on novel deep learning techniques. HDMLF is composed of an embedding core and a learning core; the embedding core adopts the latest protein language model for protein sequence embedding, and the learning core conducts the EC number prediction. Specifically, HDMLF is designed on the basis of a gated recurrent unit framework to perform EC number prediction in the multi-objective hierarchy, multitasking manner. Additionally, we introduced an attention layer to optimize the EC prediction and employed a greedy strategy to integrate and fine-tune the final model. Comparative analyses against 4 representative methods demonstrate that HDMLF stably delivers the highest performance, which improves accuracy and F1 score by 60% and 40% over the state of the art, respectively. An additional case study of tyrB predicted to compensate for the loss of aspartate aminotransferase aspC, as reported in a previous experimental study, shows that our model can also be used to uncover the enzyme promiscuity. Finally, we established a web platform, namely, ECRECer (https://ecrecer.biodesign.ac.cn), using an entirely could-based serverless architecture and provided an offline bundle to improve usability.

Construction and Analysis of an Enzyme-Constrained Metabolic Model of Corynebacterium glutamicum.

The genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only considers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experimentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineering targets. Here, we present the first genome-scale enzyme-constrained model (ecCGL1) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using a ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of phenotypes and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used the ecCGL1 to identify several gene modification targets for l-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

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.

Salmonella Induces the cGAS-STING-Dependent Type I Interferon Response in Murine Macrophages by Triggering mtDNA Release.

Salmonella enterica serovar Typhimurium (S. Typhimurium) elicited strong innate immune responses in macrophages. To activate innate immunity, pattern recognition receptors (PRRs) in host cells can recognize highly conserved pathogen-associated molecular patterns (PAMPs). Here, we showed that S. Typhimurium induced a robust type I interferon (IFN) response in murine macrophages. Exposure of macrophages to S. Typhimurium activated a Toll-like receptor 4 (TLR4)-dependent type I IFN response. Next, we showed that type I IFN and IFN-stimulated genes (ISGs) were elicited in a TBK1-IFN-dependent manner. Furthermore, cytosolic DNA sensor cyclic GMP-AMP synthase (cGAS) and immune adaptor protein stimulator of interferon genes (STING) were also required for the induction of type I IFN response during infection. Intriguingly, S. Typhimurium infection triggered mitochondrial DNA (mtDNA) release into the cytosol to activate the type I IFN response. In addition, we also showed that bacterial DNA was enriched in cGAS during infection, which may contribute to cGAS activation. Finally, we showed that cGAS and STING deficient mice and cells were more susceptible to S. Typhimurium infection, signifying the critical role of the cGAS-STING pathway in host defense against S. Typhimurium infection. In conclusion, in addition to TLR4-dependent innate immune response, we demonstrated that S. Typhimurium induced the type I IFN response in a cGAS-STING-dependent manner and the S. Typhimurium-induced mtDNA release was important for the induction of type I IFN. This study elucidated a new mechanism by which bacterial pathogen activated the cGAS-STING pathway and also characterized the important role of cGAS-STING during S. Typhimurium infection. IMPORTANCE As one of the most common foodborne transmitted zoonotic pathogens, S. Typhimurium infection causes diarrheal disease in humans and animals. S. Typhimurium infection has been implicated as an inducer for the type I interferon (IFN) response in macrophages, but the mechanisms are not fully understood. In this study, we reported that in addition to TLR4-dependent response, the cytosolic surveillance pathway (CSP) cGAS-STING is also required for the activation of type I IFN response during S. Typhimurium infection. We further showed that the infection of S. Typhimurium triggered mtDNA release into the cytosol, which induces the type I IFN response. In addition, physical interactions between cGAS and S. Typhimurium DNA have been identified in the context of infection. Importantly, we also provided convincing in vivo and in vitro evidence that the cGAS-STING pathway was potently implicated in the host defense against S. Typhimurium infection. Together, we uncovered a mechanism by which type I IFN response is elicited during S. Typhimurium infection in murine macrophages in an mtDNA-cGAS-STING-dependent manner.

Data-Driven Synthetic Cell Factories Development for Industrial Biomanufacturing.

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing.

Small protein Cgl2215 enhances phenolic tolerance by promoting MytA activity in Corynebacterium glutamicum.

Corynebacterium glutamicum is a promising chassis microorganism for the bioconversion of lignocellulosic biomass owing to its good tolerance and degradation of the inhibitors generated in lignocellulosic pretreatments. Among the identified proteins encoded by genes within the C. glutamicum genome, nearly 400 are still functionally unknown. Based on previous transcriptome analysis, we found that the hypothetical protein gene cgl2215 was highly upregulated in response to phenol, ferulic acid, and vanillin stress. The cgl2215 deletion mutant was shown to be more sensitive than the parental strain to phenolic compounds as well as other environmental factors such as heat, ethanol, and oxidative stresses. Cgl2215 interacts with C. glutamicum mycoloyltransferase A (MytA) and enhances its in vitro esterase activity. Sensitivity assays of the ΔmytA and Δcgl2215ΔmytA mutants in response to phenolic stress established that the role of Cgl2215 in phenolic tolerance was mediated by MytA. Furthermore, transmission electron microscopy (TEM) results showed that cgl2215 and mytA deletion both led to defects in the cell envelope structure of C. glutamicum, especially in the outer layer (OL) and electron-transparent layer (ETL). Collectively, these results indicate that Cgl2215 can enhance MytA activity and affect the cell envelope structure by directly interacting with MytA, thus playing an important role in resisting phenolic and other environmental stresses.