Affinity-based protein profiling (AfBPP) allows us to identify target proteins that bind drugs or other small molecules of interest in complex samples. As an enrichment technique, label-free AfBPP often generates data with high missingness, particularly in negative control samples. We developed an R package, chemoprotR, which enables both quantitative and qualitative statistical analyses of chemoproteomic data, and applied it to the identification of specific benzodiazepine drug targets in the brain. Benzodiazepines comprise a class of drugs that affect GABAA receptors through positive allosteric modulation, but benzodiazepine interactions with other proteins are not fully understood. To this end, we synthesized benzodiazepine affinity-based probes (AfBPs) and applied them to rat brain synaptosomes. Our benzodiazepine AfBPs identified GABAA receptor subunits and other proteins with ion channel functions. Across the three probes, there was minimal overlap in protein targets identified by competitive labeling with flurazepam, and FR-DA, the probe based on flurazepam, yielded more significant protein targets than the probes based on flunitrazepam. These results demonstrate the ability of benzodiazepine AfBPs to identify protein targets when used with an authentic benzodiazepine to compete for binding sites and highlight the utility of combined statistical analyses for the interpretation of presence-absence data in AfBPP data sets.

Striated muscle contraction occurs through interactions between overlapping myosin-based thick and actin-based thin filaments within the sarcomere. For effective contraction to occur, the length of the thin filament must be maintained to ensure sufficient overlap with the thick filament. The proteins leiomodin and tropomodulin compete for binding at the pointed ends of thin filaments to regulate their length, using their homologous N-terminal actin and tropomyosin binding sites. Leiomodin also contains a region called the C-terminal extension, absent in tropomodulin. In this region, the cardiac isoform (leiomodin-2) contains additional actin-binding sites that enable it to bind along the sides of thin filaments in a Ca2+-dependent manner. Here, using nuclear magnetic resonance spectroscopy, we localize the regions of the C-terminal extension that contain residues involved in thin filament side-binding. Using co-sedimentation assays, we reveal that these regions can bind thin filaments independently of one another and discover that the poly-proline region serves as a linker, maintaining an adequate distance between two of the regions required for effective interaction. In addition to its role in side-binding, we provide evidence that the poly-proline region interacts with profilin and propose a new mechanism by which leiomodin-2 may assist in the polymerization of profilin-bound actin at thin filament pointed ends.

Abstract Bacteriophages can rewire host chemistry via auxiliary viral genes (AVGs). Using metagenomic and metatranscriptomic data from the native soil microbiome, we identified transcriptionally active AVGs, including a viral catechol 1,2-dioxygenase (V-C12DO). V-C12DO shares ∼40% sequence identity with its nearest bacterial homologs and lacks the helical dimerization domain. Despite truncation, V-C12DO retains more than ∼25% of the global consensus residues compared to C12DOs across domains of life, including the two tyrosines and two histidines that coordinate the non-heme Fe(III) active site. A 1.7 Å crystal structure also showed the conservation of the canonical β-sandwich scaffold for the iron. We next confirmed that V-C12DO cleaves catechol and remains highly active across a broad range of temperatures (30-60 °C), pH (5.5-9), and salinity (up to 2 M), exceeding those of known bacterial CD12Os. This work shows that truncated phage enzymes preserve the core catalytic chemistry and potentially further expand host metabolic versatility across dynamic environmental conditions.

Synthetic opioids such as fentanyl and related analogs have been widely used for pain management. However, their negative side effects, including respiratory depression and high potential for addiction, underscore the need for a deeper understanding of fentanyl's interactions with proteins throughout the human body. Fentanyl analogs bind and activate opioid receptors in the central and peripheral nervous systems, triggering numerous downstream signaling pathways. Increasingly, fentanyl has been shown to interact with non-opioid receptors, and elucidation of these non-canonical fentanyl-protein interactions may provide insights into the mechanisms contributing to fentanyl's adverse effects and illuminate novel countermeasure strategies. To identify proteins in mammalian tissues that may interact with fentanyl, we designed and synthesized three affinity-based probes (AfBPs) that include the fentanyl core and feature a diazirine photoaffinity group and alkyne handle for click chemistry at different positions. Molecular docking simulations predicted that these AfBPs bind the mu opioid receptor similarly to fentanyl. Affinity-based protein profiling using the FA-T1 probe in vitro in tissues from six animal species identified histamine N-methyltransferase (HNMT), endophilin-B1 (SH3GLB1), fructosamine-3-kinase (FN3K), cutA divalent cation tolerance analog (CUTA), and monoamine oxidase B (MAOB) among the top proteins that bind fentanyl in multiple species and tissue types. Molecular docking of fentanyl and remifentanil with these protein structures identified putative binding sites. The interaction of fentanyl with specific proteins was empirically assessed through protein structural analyses. These findings highlight potential fentanyl-protein interactions that may contribute to the acute and long-term impacts of fentanyl exposures. ### Competing Interest Statement The authors have declared no competing interest.

The wine-red coloration of xylem in antisense cinnamyl alcohol dehydrogenase (CAD) down-regulated tobacco (Nicotiana tabacum) has long been reported as due to wine-red colored lignin. To investigate validity of the wine-red lignin report, both wild type (WT) and anti-sense CAD down-regulated tobacco stem xylem tissues were examined, with the red pigment found to be facilely removed by treatment with an 0.5 % HCl in MeOH solution at 4 °C. The red pigment in the xylem tissues was rapidly reconstituted in both pre-treated antisense CAD and WT xylem tissues by incubation with sinapyl aldehyde in MeOH solution, and then facilely removed by subsequent 0.5 % HCl in MeOH treatment. Only sinapyl aldehyde was demonstrated as contributing to the red pigmentation, this being consistent with its anionic quinone methide canonical form. No evidence was obtained for a wine-red lignin. Additionally, lignin-derived isolates from both antisense CAD and WT lines essentially differed only in presence of small amounts (ca. 1.41 %) of 8-O-4' linked sinapyl aldehyde end groups and a slightly smaller molecular weight distribution (MWD) of the lignin from the antisense CAD line. No evidence was obtained that poly-hydroxycinnamyl aldehydes contributed to bulk lignification; the slightly lower MWD may result from hydroxycinnamyl aldehydes functioning as terminators of polymerization during lignification. More importantly, these findings are placed in context of what is now known about 1) lignin macromolecular assembly proper in planta, 2) dirigent protein functions, 3D structures, and active sites as entry points to distinct plant phenol metabolic classes, including lignins, 3) AlphaFold2 modeling of Dir-e subfamily proteins involved in lignification, and 4) what urgently remains to be done in the study of lignification.

A literature survey was conducted to identify current practices used by NMR metabolomics investigators when conducting and reporting their metabolomics studies. A total of 463 papers from 2020 to 80 papers from 2010 were selected from PubMed and were manually analyzed by a team of investigators to assess the extent and completeness of the experimental procedures and protocols reported. A significant number of the papers did not report on essential experimental details, incompletely stated which statistical methods were used, improperly applied supervised multivariate statistical analyses, or lacked validation of statistical models. A large diversity of protocols and software were identified, which suggests a lack of consensus and a relatively limited use of commonly agreed upon standards for conducting and reporting NMR metabolomics studies. The overall intent of the survey is to inform and encourage the NMR metabolomics community to develop and adopt best practices for the field.

The Natural Products Magnetic Resonance Database (NP-MRD; https://np-mrd.org) is a comprehensive, freely accessible, web-based resource for the deposition, distribution, extraction, and retrieval of nuclear magnetic resonance (NMR) data on natural products (NPs). The NP-MRD was initially established to support compound de-replication and data dissemination for the NP community. However, that community has now grown to include many users from the metabolomics, microbiomics, foodomics, and nutrition science fields. Indeed, since its launch in 2022, the NP-MRD has expanded enormously in size, scope, and popularity. The current version of NP-MRD now contains nearly 7 x more compounds (281 859 versus 40 908) and 7 x more NMR spectra (5.5 million versus 817 278) than the first release. More specifically, an additional 4.6 million predicted spectra and another 11 0 0 0 spectra simulated from experimental chemical shifts were deposited into the database. Likewise, the number of NMR raw spectral data depositions has grown from 165 spectra per year to > 10 0 0 0 per year. As a result of this expansion, the number of monthly webpage views has grown from 55 to 20 0 0 0 and the number of monthly visitors has increased from 7 to 2500. To address this growth and to better support the expanding needs of its diverse community of users, many additional improvements to the NP-MRD have been made. These include significant enhancements to the data submission process, notable updates to the database's spectral search utilities and useful additions to support better NMR spectral analysis / prediction. Significant efforts have also been undertaken to remediate and update many of NP-MRD's database entries. This manuscript describes these database improvements and expansion efforts, along with how they have been implemented and what future upgrades to the NP-MRD are planned.

Red alder (Alnus rubra) has highly desirable wood, dye pigment, and (traditional) medicinal properties which have been capitalized on for thousands of years, including by Pacific West Coast Native Americans. A rapidly growing tree species native to North American western coastal and riparian regions, it undergoes symbiosis with actinobacterium Frankia via their nitrogen-fixing root nodules. Red alder’s desirable properties are, however, largely attributed to its bioactive plant phenol metabolites, including for plant defense, for its attractive wood and bark coloration, and various beneficial medicinal properties. Integrated transcriptome and metabolome data analyses were carried out using buds, leaves, stems, roots, and root nodules from greenhouse grown red alder saplings with samples collected during different time-points (Spring, Summer, and Fall) of the growing season. Pollen and catkins were collected from field grown mature trees. Overall plant phenol biochemical pathways operative in red alder were determined, with a particular emphasis on potentially identifying candidates for the long unknown gateway entry points to the proanthocyanidin (PA) and ellagitannin metabolic classes, as well as in gaining better understanding of the biochemical basis of diarylheptanoid formation, i.e. that help define red alder’s varied medicinal uses, and its extensive wood and dye usage.

Noncanonical amino acids (ncAAs) provide numerous avenues for the introduction of novel functionality to peptides and proteins. ncAAs can be incorporated through solid-phase synthesis or genetic code expansion in conjugation with heterologous expression of the encoded protein modification. Due to the difficulty of synthesis or overexpression, wide chemical space, and lack of empirically resolved structures, modeling the effects of ncAA mutation is critical for rational protein design. To evaluate the structural and functional perturbations ncAAs introduce, we utilize molecular potentials that describe the forces in the protein structure. Most potentials such as CHARMM are designed to model canonical residues but can be parametrized to include novel ncAAs. In this work, we introduce NCAP, a software package to generate CHARMM-compatible parameters from quantum chemical calculations. Unlike currently available tools, NCAP is designed to recognize the ncAA structure and automatically bridge the gap between quantum chemical calculations and CHARMM potential parameters. For our software, we discuss the workflow, validation against canonical parameter sets, and comparison with published ncAA-protein structures.

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

Pea phytoalexins (-)-maackiain and (+)-pisatin have opposite C6a/C11a configurations, but biosynthetically how this occurs is unknown. Pea dirigent-protein (DP) PsPTS2 generates 7,2'- dihydroxy-4',5'-methylenedioxyisoflav-3-ene (DMDIF), and stereoselectivity toward four possible 7,2'-dihydroxy-4',5'- methylenedioxyisoflavan-4-ol (DMDI) stereoisomers was investigated. Stereoisomer configurations were determined using NMR spectroscopy, electronic circular dichroism, and molecular orbital analyses. PsPTS2 efficiently converted cis- (3R,4R)-DMDI into DMDIF 20-fold faster than the trans- (3R,4S)-isomer. The 4R-configured substrate's near beta-axial OH orientation significantly enhanced its leaving group abilities in generating A-ring mono-quinone methide (QM), whereas 4S- isomer's alpha-equatorial-OH was a poorer leaving group. Docking simulations indicated that the 4R-configured beta-axial OH was closest to Asp51, whereas 4S-isomer's alpha-equatorial OH was further away. Neither cis-(3 S ,4 S )- nor trans-(3 S ,4 R )-DMDIs were substrates, even with the former having C3/C4 stereo- chemistry as in (+)-pisatin. PsPTS2 used cis-(3 R ,4 R )-7,2 0-dihy- droxy-4'-methoxyisoflavan-4-ol [cis-(3R,4R)-DMI] and C3/C4 stereoisomers to give 2',7-dihydroxy-4'-methoxyisoflav-3-ene (DMIF). DP homologs may exist in licorice ( Glycyrrhiza pallidiflora) and tree legume Bolusanthus speciosus, as DMIF occurs in both species. PsPTS1 utilized cis-(3 R ,4 R )-DMDI to give (-)-maackiain 2200-fold more efficiently than with cis-(3 R ,4 R )- DMI to give (-)-medicarpin. PsPTS1 also slowly converted trans-(3 S ,4 R )-DMDI into (+)-maackiain, reflecting the better 4R configured OH leaving group. PsPTS2 and PsPTS1 provisionally provide the means to enable differing C6a and C11a confi gurations in (+)-pisatin and (-)-maackiain, via identical DP- engendered mono-QM bound intermediate generation, which PsPTS2 either re-aromatizes to give DMDIF or PsPTS1 intramolecularly cyclizes to afford (-)-maackiain. Substrate docking simulations using PsPTS2 and PsPTS1 indicate cis- (3R,4R)-DMDI binds in the anti-configuration in PsPTS2 to afford DMDIF, and the syn-con fi guration in PsPTS1 to give maackiain.

BACKGROUND:The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.AIM OF REVIEW:The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies.KEY SCIENTIFIC CONCEPTS OF REVIEW:We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized experts in metabolomics, natural products discovery, chemical ecology, chemical and biological threat assessment, cheminformatics, computational chemistry, cloud computing, artificial intelligence, and novel technology development. These experts were invited to assess the value and feasibility of a grand-scale project to create new technologies that would allow the identification and quantification of all small molecules, or to decode the molecular universe. The Decoding the Molecular Universe project would extend and complement the success of the Human Genome Project by developing new capabilities and technologies to measure small molecules (defined as non-protein, non-polymer molecules less than 1500 Daltons) of any origin and generated in biological systems or produced abiotically. Workshop attendees 1) explored what new understanding of biological and environmental systems could be revealed through the lens of small molecules; 2) characterized the similarities in current needs and technical challenges between each science or mission area for unambiguous and comprehensive determination of the composition and quantities of small molecules of any sample; 3) determined the extent to which technologies or methods currently exist for unambiguously and comprehensively determining the small molecule composition of any sample and in a reasonable time; and 4) identified the attributes of the ideal technology or approach for universal small molecule measurement and identification. The workshop concluded with a discussion of how a project of this scale could be undertaken, possible thrusts for the project, early proof-of-principle applications, and similar efforts upon which the project could be modeled.

The Coordinating Research Council (CRC) is actively involved in developing and applying advanced analytical techniques to the chemical characterization of transportation fuels. This article complements a 2017 CRC project to quantify and compare the effects of a commercially available renewable diesel fuel (hydrotreated vegetable oil [HVO]) and an ultralow sulfur diesel (ULSD) fuel on engine-out gaseous and particulate matter (PM) emissions from a light-duty vehicle. Results showed that the combustion of HVO fuel had an advantage over ULSD in terms of lowering engine-out emissions (THC, CO, NOx, etc.). Furthermore, this advantage is strongly related to the fuel composition. This article summarizes the results of advanced and comprehensive analytical tests on the same ULSD and HVO fuels and attempts to connect some of the engine-out emissions results to fuel composition and specific chemical structures. A variety of test methods, generally unavailable in combination, were employed, such as one-dimensional (1D) and two-dimensional (2D) gas chromatography (GC), nuclear magnetic resonance spectroscopy (NMR), and high-pressure solid-liquid phase transition experiments. In summary, the ULSD sample was found to have representation across the expected set of hydrocarbon classes typical for the sample type. Interestingly, a high content of cycloparaffins (>50 wt%) and a very low content of diaromatics (similar to 2 wt%) were present. While not without precedent, these are higher and lower, respectively, than typically found for commercial ULSD compositions. In contrast, HVO was found to consist of only two hydrocarbon classes: n-paraffins (similar to 10 wt%) and iso-paraffins (similar to 90 wt%), both predominantly in a narrow carbon atom number range (i.e., C14-C18). HVO engine-out emissions results for the LA-92 and steady-state testing can be tracked to the narrow carbon atom number range of the n-paraffins and iso-paraffins, which result in a high cetane number fuel having a narrow distillation range. Previously, the low-temperature operability of HVO has been a concern, but that appears not be the case for this particular HVO. HVO and ULSD were evaluated at pressures up to similar to 275 MPa and found to have comparable solid-liquid equilibria despite significant compositional differences.

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases either are not designed for natural products data or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data.