Modeling reveals a metabolic basis of competition among strains during tandem chloroform and dichloromethane metabolism.

SC05-UT is an anaerobic, heterogenous microbial enrichment culture that reduces chloroform to dichloromethane through reductive dechlorination, which it further mineralizes to carbon dioxide. This dichloromethane mineralization yields electron equivalents that are used to reduce chloroform without the addition of exogenous electron donor. By studying this self-feeding chloroform-amended culture and a dichloromethane-amended enrichment subculture (named DCME), we previously found the genomic potential to perform both biodegradation steps in two distinct strains: SAD and Dehalobacter alkaniphilus DAD.

CPI-Pred: A deep learning framework for predicting functional parameters of compound-protein interactions.

Recent advancements in deep learning have enabled functional annotation of genome sequences, facilitating the discovery of new enzymes and metabolites. However, accurately predicting compound-protein interactions (CPI) from sequences remains challenging due to the complexity of these interactions and the sparsity and heterogeneity of available data, which constrain the generalization of patterns across their solution space. In this work, we introduce CPI-Pred, a versatile deep learning model designed to predict compound-protein interaction function.

Pangenomic insights into evolution and acquisition of functional genes for bioremediation.

is a genus of organohalide-respiring bacteria that is recognized for its fastidious growth using reductive dehalogenases (RDases). In the SC05 culture, however, a population also mineralizes dichloromethane (DCM) produced by chloroform dechlorination using the cassette, just downstream of its active RDase. A closed genome of this DCM-mineralizing lineage has previously evaded assembly.

Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy.

A metabolic theory is presented for predicting maximum growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on the intersection of cell geometry, membrane protein crowding, and metabolism. The importance of cytosolic macromolecular crowding on phenotype has been established in the literature but the importance of surface area has been largely overlooked due to incomplete knowledge of membrane properties. We demonstrate that the capacity of the membrane to host proteins increases with growth rate offsetting decreases in surface area-to-volume ratios (SA:V).

Evaluating the feasibility of medium-chain oleochemical synthesis using microbial chain elongation.

Unlabelled: Chain elongating bacteria are a unique guild of strictly anaerobic bacteria that have garnered interest for sustainable chemical manufacturing from carbon-rich wet and gaseous waste streams. They produce C6-C8 medium-chain fatty acids, which are valuable platform chemicals that can be used directly, or derivatized to service a wide range of chemical industries. However, the application of chain elongating bacteria for synthesizing products beyond C6-C8 medium-chain fatty acids has not been evaluated.

FOCUS on NOD2: Advancing IBD Drug Discovery with a User-Informed Machine Learning Framework.

In this study, we introduce the Framework for Optimized Customizable User-Informed Synthesis (FOCUS), a generative machine learning model tailored for drug discovery. FOCUS integrates domain expertise and uses Proximal Policy Optimization (PPO) to guide Monte Carlo Tree Search (MCTS) to efficiently explore chemical space. It generates SMILES representations of potential drug candidates, optimizing for druggability and binding efficacy to NOD2, PEP, and MCT1 receptors.

Design and Characterization of a Generalist Biosensor for Indole Derivatives.

Transcription factor (TF)-based biosensors are useful synthetic biology tools for applications in a variety of areas of biotechnology. A major challenge of biosensor circuits is the limited repertoire of identified and well-characterized TFs for applications of interest, in addition to the challenge of optimizing selected biosensors. In this work, we implement the IclR family repressor TF TtgV from DOT-T1E as an indole-derivative biosensor in .

From cassette to : a key genomic neighborhood in a chloroform and dichloromethane-transforming microbial consortium.

Unlabelled: Chloroform (CF) and dichloromethane (DCM) are groundwater contaminants of concern due to their high toxicity and inhibition of important biogeochemical processes such as methanogenesis. Anaerobic biotransformation of CF and DCM has been well documented but typically independently of one another. CF is the electron acceptor for certain organohalide-respiring bacteria that use reductive dehalogenases (RDases) to dechlorinate CF to DCM.

Computation-guided transcription factor biosensor specificity engineering for adipic acid detection.

Transcription factor (TF)-based biosensors that connect small-molecule sensing with readouts such as fluorescence have proven to be useful synthetic biology tools for applications in biotechnology. However, the development of specific TF-based biosensors is hindered by the limited repertoire of TFs specific for molecules of interest since current construction methods rely on a limited set of characterized TFs. In this study, we present an approach for engineering the specificity of TFs through a computation-based workflow using molecular docking that enables targeted alteration of TF ligand specificity.

DyMMM-LEAPS: An ML-based framework for modulating evenness and stability in synthetic microbial communities.

There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework.

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors.

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family.

Ni(II)-binding affinity of CcNikZ-II and its homologs: the role of the HH-prong and variable loop revealed by structural and mutational studies.

Extracytoplasmic Ni(II)-binding proteins (NiBPs) are molecular shuttles involved in cellular nickel uptake. Here, we determined the crystal structure of apo CcNikZ-II at 2.38 Å, which revealed a Ni(II)-binding site comprised of the double His (HH-)prong (His511, His512) and a short variable (v-)loop nearby (Thr59-Thr64, TEDKYT).

A Multifunctional ? Tandem Chloroform and Dichloromethane Degradation in a Mixed Microbial Culture.

Chloroform (CF) and dichloromethane (DCM) contaminate groundwater sites around the world but can be cleaned up through bioremediation. Although several strains of can reduce CF to DCM and multiple Peptococcaceae can ferment DCM, these processes cannot typically happen simultaneously due to CF sensitivity in the known DCM-degraders or electron donor competition. Here, we present a mixed microbial culture that can simultaneously metabolize CF and DCM and create an additional enrichment culture fed only DCM.

Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels.

Biotechnology has revolutionized the development of sustainable energy sources by harnessing biomass as a feedstock for energy production. However, challenges such as recalcitrant feedstocks and inefficient metabolic pathways hinder the large-scale integration of renewable energy systems. Enzyme engineering has emerged as a powerful tool to address these challenges by enhancing enzyme activity, specificity, and stability.

Genetic Engineering of Using CRISPR Systems To Mitigate Toxic Release in Biomining.

Biomining processes utilize microorganisms, such as , to extract valuable metals by producing sulfuric acid and ferric ions that dissolve sulfidic minerals. However, excessive production of these compounds can result in metal structure corrosion and groundwater contamination. Synthetic biology offers a promising solution to improve strains for sustainable, eco-friendly, and cost-effective biomining, but genetic engineering of these slow-growing microorganisms is challenging with current inefficient and time-consuming methods.

Engineered nickel bioaccumulation in by NikABCDE transporter and metallothionein overexpression.

Mine wastewater often contains dissolved metals at concentrations too low to be economically extracted by existing technologies, yet too high for environmental discharge. The most common treatment is chemical precipitation of the dissolved metals using limestone and subsequent disposal of the sludge in tailing impoundments. While it is a cost-effective solution to meet regulatory standards, it represents a lost opportunity.

Engineering Escherichia coli to produce aromatic chemicals from ethylene glycol.

Microbial overproduction of aromatic chemicals has gained considerable industrial interest and various metabolic engineering approaches have been employed in recent years to address the associated challenges. So far, most studies have used sugars (mostly glucose) or glycerol as the primary carbon source. In this study, we used ethylene glycol (EG) as the main carbon substrate.

Quantifying metal-binding specificity of CcNikZ-II from Clostridium carboxidivorans in the presence of competing metal ions.

Many proteins bind transition metal ions as cofactors to carry out their biological functions. Despite binding affinities for divalent transition metal ions being predominantly dictated by the Irving-Williams series for wild-type proteins, in vivo metal ion binding specificity is ensured by intracellular mechanisms that regulate free metal ion concentrations. However, a growing area of biotechnology research considers the use of metal-binding proteins in vitro to purify specific metal ions from wastewater, where specificity is dictated by the protein's metal binding affinities.

A Universal Strategy to Promote Secretion of G+/G- Bacterial Extracellular Vesicles and Its Application in Host Innate Immune Responses.

Both Gram-positive and Gram-negative bacteria release nanosized extracellular vesicles called membrane vesicles (MVs, 20-400 nm), which have great potential in various biomedical applications due to their abilities to deliver effector molecules and induce therapeutic responses. To fully utilize bacterial MVs for therapeutic purposes, regulated and enhanced production of MVs would be highly advantageous. In this study, we developed a universal method to enhance MV yields in both G+/G- bacteria through an autonomous controlled peptidoglycan hydrolase (PGase) expression system.

Genome-Scale Metabolic Modeling Reveals Sequential Dysregulation of Glutathione Metabolism in Livers from Patients with Alcoholic Hepatitis.

Alcoholic hepatitis (AH) is the most severe form of alcoholic liver disease for which there is no efficacious treatment aiding most patients. AH manifests differently in individuals, with some patients showing debilitating symptoms more so than others. Previous studies showed significant metabolic dysregulation associated with AH.

Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling.

Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family.

Systems engineering of Escherichia coli for n-butane production.

Rising concerns about climate change and sustainable energy have attracted efforts towards developing environmentally friendly alternatives to fossil fuels. Biosynthesis of n-butane, a highly desirable petro-chemical, fuel additive and diluent in the oil industry, remains a challenge. In this work, we first engineered enzymes Tes, Car and AD in the termination module to improve the selectivity of n-butane biosynthesis, and ancestral reconstruction and a synthetic RBS significantly improved the AD abundance.

KinMod database: a tool for investigating metabolic regulation.

The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks.