Activity-based annotation: the emergence of systems biochemistry

Activity-based metaproteomics driven discovery and enzymological characterization of potential α-galactosidases in the mouse gut microbiome

The gut microbiota offers an extensive resource of enzymes, but many remain uncharacterized. To distinguish the activities of similar annotated proteins and mine the potentially applicable ones in the microbiome, we applied an effective Activity-Based Metaproteomics (ABMP) strategy using a specific activity-based probe (ABP) to screen the entire gut microbiome for directly discovering active enzymes and their potential applications, not for exploring host-microbiome interactions. By using an activity-based cyclophellitol aziridine probe specific to α-galactosidases (AGAL), we successfully identified and characterized several gut microbiota enzymes possessing AGAL activities. Cryo-electron microscopy analysis of a newly characterized enzyme (AGLA5) revealed the covalent binding conformations between the AGAL5 active site and the cyclophellitol aziridine ABP, which could provide insights into the enzyme's catalytic mechanism. The four newly characterized AGALs have diverse potential activities, including raffinose family oligosaccharides (RFOs) hydrolysis and enzymatic blood group transformation. Collectively, we present a ABMP platform that facilitates gut microbiota AGALs discovery, biochemical activity annotations and potential industrial or biopharmaceutical applications.

More than two components: complexities in bacterial phosphosignaling

For over 40 years, the two-component systems (TCSs) have taken front and center in our thinking about the signaling mechanisms by which bacteria sense and respond to their environment. In contrast, phosphorylation on Ser/Thr and Tyr (O-phosphorylation) was long thought to be mostly restricted to eukaryotes and a somewhat accessory signaling mechanism in bacteria. Several recent studies exploring systems aspects of bacterial O-phosphorylation, however, now show that it is in fact pervasive, with some bacterial proteomes as highly phosphorylated as those of eukaryotes. Labile, non-canonical protein phosphorylation sites on Asp, Arg, and His are now also being identified in large numbers in bacteria and first cellular functions are discovered. Other phosphomodifications on Cys, Glu, and Lys remain largely unexplored. The surprising breadth and complexity of bacterial phosphosignaling reveals a vast signaling capacity, the full scope of which we may only now be beginning to understand but whose functions are likely to affect all aspects of bacterial physiology and pathogenesis.

One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse

The increasing availability of experimental and computational protein structures entices their use for function prediction. Here we develop an automated procedure to identify enzymes involved in metabolic reactions by assessing substrate conformations docked to a library of protein structures. By screening AlphaFold-modeled vitamin B6-dependent enzymes, we find that a metric based on catalytically favorable conformations at the enzyme active site performs best (AUROC Score=0.84) in identifying genes associated with known reactions. Applying this procedure, we identify the mammalian gene encoding hydroxytrimethyllysine aldolase (HTMLA), the second enzyme of carnitine biosynthesis. Upon experimental validation, we find that the top-ranked candidates, serine hydroxymethyl transferase (SHMT) 1 and 2, catalyze the HTMLA reaction. However, a mouse protein absent in humans (threonine aldolase; Tha1) catalyzes the reaction more efficiently. Tha1 did not rank highest based on the AlphaFold model, but its rank improved to second place using the experimental crystal structure we determined at 2.26 Å resolution. Our findings suggest that humans have lost a gene involved in carnitine biosynthesis, with HTMLA activity of SHMT partially compensating for its function.

The pharmacology and therapeutic role of cannabidiol in diabetes

In recent years, cannabidiol (CBD), a non-psychotropic cannabinoid, has garnered substantial interest in drug development due to its broad pharmacological activity and multi-target effects. Diabetes is a chronic metabolic disease that can damage multiple organs in the body, leading to the development of complications such as abnormal kidney function, vision loss, neuropathy, and cardiovascular disease. CBD has demonstrated significant therapeutic potential in treating diabetes mellitus and its complications owing to its various pharmacological effects. This work summarizes the role of CBD in diabetes and its impact on complications such as cardiovascular dysfunction, nephropathy, retinopathy, and neuropathy. Strategies for discovering molecular targets for CBD in the treatment of diabetes and its complications are also proposed. Moreover, ways to optimize the structure of CBD based on known targets to generate new CBD analogues are explored.

Enzyme Substrate Prediction from Three-Dimensional Feature Representations Using Space-Filling Curves

Compact and interpretable structural feature representations are required for accurately predicting properties and function of proteins. In this work, we construct and evaluate three-dimensional feature representations of protein structures based on space-filling curves (SFCs). We focus on the problem of enzyme substrate prediction, using two ubiquitous enzyme families as case studies: the short-chain dehydrogenase/reductases (SDRs) and the S-adenosylmethionine-dependent methyltransferases (SAM-MTases). Space-filling curves such as the Hilbert curve and the Morton curve generate a reversible mapping from discretized three-dimensional to one-dimensional representations and thus help to encode three-dimensional molecular structures in a system-independent way and with only a few adjustable parameters. Using three-dimensional structures of SDRs and SAM-MTases generated using AlphaFold2, we assess the performance of the SFC-based feature representations in predictions on a new benchmark database of enzyme classification tasks including their cofactor and substrate selectivity. Gradient-boosted tree classifiers yield binary prediction accuracy of 0.77-0.91 and area under curve (AUC) characteristics of 0.83-0.92 for the classification tasks. We investigate the effects of amino acid encoding, spatial orientation, and (the few) parameters of SFC-based encodings on the accuracy of the predictions. Our results suggest that geometry-based approaches such as SFCs are promising for generating protein structural representations and are complementary to the existing protein feature representations such as evolutionary scale modeling (ESM) sequence embeddings.

Mycobacterium smegmatis: The Vanguard of Mycobacterial Research

The genus Mycobacterium contains several slow-growing human pathogens, including Mycobacterium tuberculosis, Mycobacterium leprae, and Mycobacterium avium. Mycobacterium smegmatis is a nonpathogenic and fast growing species within this genus. In 1990, a mutant of M. smegmatis, designated mc²155, that could be transformed with episomal plasmids was isolated, elevating M. smegmatis to model status as the ideal surrogate for mycobacterial research. Classical bacterial models, such as Escherichia coli, were inadequate for mycobacteria research because they have low genetic conservation, different physiology, and lack the novel envelope structure that distinguishes the Mycobacterium genus. By contrast, M. smegmatis encodes thousands of conserved mycobacterial gene orthologs and has the same cell architecture and physiology. Dissection and characterization of conserved genes, structures, and processes in genetically tractable M. smegmatis mc²155 have since provided previously unattainable insights on these same features in its slow-growing relatives. Notably, tuberculosis (TB) drugs, including the first-line drugs isoniazid and ethambutol, are active against M. smegmatis, but not against E. coli, allowing the identification of their physiological targets. Furthermore, Bedaquiline, the first new TB drug in 40 years, was discovered through an M. smegmatis screen. M. smegmatis has become a model bacterium, not only for M. tuberculosis, but for all other Mycobacterium species and related genera. With a repertoire of bioinformatic and physical resources, including the recently established Mycobacterial Systems Resource, M. smegmatis will continue to accelerate mycobacterial research and advance the field of microbiology.