OCaR1 endows exocytic vesicles with autoregulatory competence by preventing uncontrolled Ca2+ release, exocytosis, and pancreatic tissue damage.

Regulated exocytosis is initiated by increased Ca2+ concentrations in close spatial proximity to secretory granules, which is effectively prevented when the cell is at rest. Here we showed that exocytosis of zymogen granules in acinar cells was driven by Ca2+ directly released from acidic Ca2+ stores including secretory granules through NAADP-activated two-pore channels (TPCs). We identified OCaR1 (encoded by Tmem63a) as an organellar Ca2+ regulator protein integral to the membrane of secretory granules that controlled Ca2+ release via inhibition of TPC1 and TPC2 currents. Deletion of OCaR1 led to extensive Ca2+ release from NAADP-responsive granules under basal conditions as well as upon stimulation of GPCR receptors. Moreover, OCaR1 deletion exacerbated the disease phenotype in murine models of severe and chronic pancreatitis. Our findings showed OCaR1 as a gatekeeper of Ca2+ release that endows NAADP-sensitive secretory granules with an autoregulatory mechanism preventing uncontrolled exocytosis and pancreatic tissue damage.

The Peptide Antibiotic Corramycin Adopts a ß-Hairpin-like Structure and Is Inactivated by the Kinase ComG.

The rapid development of antibiotic resistance, especially among difficult-to-treat Gram-negative bacteria, is recognized as a serious and urgent threat to public health. The detection and characterization of novel resistance mechanisms are essential to better predict the spread and evolution of antibiotic resistance. Corramycin is a novel and modified peptidic antibiotic with activity against several Gram-negative pathogens. We demonstrate that the kinase ComG, part of the corramycin biosynthetic gene cluster, phosphorylates and thereby inactivates corramycin, leading to the resistance of the host. Remarkably, we found that the closest structural homologues of ComG are aminoglycoside phosphotransferases; however, ComG shows no activity toward this class of antibiotics. The crystal structure of ComG in complex with corramycin reveals that corramycin adopts a ß-hairpin-like structure and allowed us to define the changes leading to a switch in substrate from sugar to peptide. Bioinformatic analyses suggest a limited occurrence of ComG-like proteins, which along with the absence of cross-resistance to clinically used drugs positions corramycin as an attractive antibiotic for further development.

Topographic Distribution of miRNAs (miR-30a, miR-223, miR-let-7a, miR-let-7f, miR-451, and miR-486) in the Plasma Extracellular Vesicles.

There are many articles on the quantitative analysis of miRNAs contained in a population of EVs of different sizes under various physiological and pathological conditions. For such analysis, it is important to correctly quantify the miRNA contents of EVs. It should be considered that quantification is skewed depending on the isolation protocol, and different miRNAs are degraded by nucleases with different efficiencies. In addition, it is important to consider the contribution of miRNAs coprecipitating with the EVs population, because the amount of miRNAs in the EVs population under study is skewed without appropriate enzymatic treatment. By studying a population of EVs from the blood plasma of healthy donors, we found that the absolute amount of miRNA inside the vesicles is commensurate with the amount of the same type of miRNA adhered to the outside of the EVs. The inside/outside ratio ranged from 1.02 to 2.64 for different investigated miRNAs. According to our results, we propose the hypothesis that high occupancy of miRNAs on the outer surface of EVs influence on the transporting RNA repertoire no less than the inner cargo received from the host cell.

Elucidation of unusual biosynthesis and DnaN-targeting mode of action of potent anti-tuberculosis antibiotics Mycoplanecins.

DNA polymerase III sliding clamp (DnaN) was recently validated as a new anti-tuberculosis target employing griselimycins. Three (2 S,4 R)-4-methylproline moieties of methylgriselimycin play significant roles in target binding and metabolic stability. Here, we identify the mycoplanecin biosynthetic gene cluster by genome mining using bait genes from the 4-methylproline pathway. We isolate and structurally elucidate four mycoplanecins comprising scarce homo-amino acids and 4-alkylprolines. Evaluating mycoplanecin E against Mycobacterium tuberculosis surprisingly reveals an excitingly low minimum inhibition concentration at 83 ng/mL, thus outcompeting griselimycin by approximately 24-fold. We show that mycoplanecins bind DnaN with nanomolar affinity and provide a co-crystal structure of mycoplanecin A-bound DnaN. Additionally, we reconstitute the biosyntheses of the unusual L-homoleucine, L-homonorleucine, and (2 S,4 R)-4-ethylproline building blocks by characterizing in vitro the full set of eight enzymes involved. The biosynthetic study, bioactivity evaluation, and drug target validation of mycoplanecins pave the way for their further development to tackle multidrug-resistant mycobacterial infections.

Exopolysaccharide-Treated Dendritic Cells Effectively Ameliorate Acute Graft-versus-Host Disease.

Graft-versus-host disease (GVHD) is a primary and often lethal complication of allogenic hematopoietic stem cell transplantation (HSCT). Prophylactic regimens for GVHD are given as standard pretransplantation therapy; however, up to 50% of these patients develop acute GVHD (aGVHD) and require additional immunosuppressive intervention. Using a mouse GVHD model, we previously showed that injecting mice with exopolysaccharide (EPS) from Bacillus subtilis prior to GVHD induction significantly increased 80-day survival after transplantation of complete allogeneic major histocompatibility complex-mismatched cells. To ask whether EPS might also inhibit GVHD in humans, we used humanized NSG-HLA-A2 mice and induced GVHD by i.v. injection of A2neg human peripheral blood mononuclear cells (PBMCs). Because we could not inject human donors with EPS, we transferred EPS-pretreated dendritic cells (DCs) to inhibit aGVHD. We derived these DCs from CD34+ human cord blood cells, treated them with EPS, and then injected them together with PBMCs into the NSG-HLA-A2 mice. We found that all mice that received untreated DCs were dead by day 35, whereas 25% of mice receiving EPS-treated DCs (EPS-DCs) survived. This DC cell therapy could be readily translatable to humans, because we can generate large numbers of human EPS-DCs and use them as an "off the shelf" treatment for patients undergoing HSCT.

PanPA: generation and alignment of panproteome graphs.

Motivation: Compared to eukaryotes, prokaryote genomes are more diverse through different mechanisms, including a higher mutation rate and horizontal gene transfer. Therefore, using a linear representative reference can cause a reference bias. Graph-based pangenome methods have been developed to tackle this problem. However, comparisons in DNA space are still challenging due to this high diversity. In contrast, amino acid sequences have higher similarity due to evolutionary constraints, whereby a single amino acid may be encoded by several synonymous codons. Coding regions cover the majority of the genome in prokaryotes. Thus, panproteomes present an attractive alternative leveraging the higher sequence similarity while not losing much of the genome in non-coding regions. Results: We present PanPA, a method that takes a set of multiple sequence alignments of protein sequences, indexes them, and builds a graph for each multiple sequence alignment. In the querying step, it can align DNA or amino acid sequences back to these graphs. We first showcase that PanPA generates correct alignments on a panproteome from 1350 Escherichia coli. To demonstrate that panproteomes allow comparisons at longer phylogenetic distances, we compare DNA and protein alignments from 1073 Salmonella enterica assemblies against E.coli reference genome, pangenome, and panproteome using BWA, GraphAligner, and PanPA, respectively; with PanPA aligning around 22% more sequences. We also aligned a DNA short-reads whole genome sequencing (WGS) sample from S.enterica against the E.coli reference with BWA and the panproteome with PanPA, where PanPA was able to find alignment for 68% of the reads compared to 5% with BWA. Availalability and implementation: PanPA is available at https://github.com/fawaz-dabbaghieh/PanPA.

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis.

BACKGROUND: Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature. RESULTS: In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics. CONCLUSIONS: Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Circulating CD8+ T Cell Subsets in Primary Sjögren's Syndrome.

Currently, multiple studies have indicated that CD8+ T lymphocytes play a role in causing damage to the exocrine glands through acinar injury in primary Sjögren's syndrome (pSS). The aim of this research was to assess the imbalance of circulating CD8+ T cell subsets. We analyzed blood samples from 34 pSS patients and 34 healthy individuals as controls. We used flow cytometry to enumerate CD8+ T cell maturation stages, using as markers CD62L, CD28, CD27, CD4, CD8, CD3, CD45RA and CD45. For immunophenotyping of 'polarized' CD8+ T cell subsets, we used the following monoclonal antibodies: CXCR5, CCR6, CXCR3 and CCR4. The findings revealed that both the relative and absolute numbers of 'naïve' CD8+ T cells were higher in pSS patients compared to the healthy volunteers. Conversely, the proportions of effector memory CD8+ T cells were notably lower. Furthermore, our data suggested that among patients with pSS, the levels of cytotoxic Tc1 CD8+ T cells were reduced, while the frequencies of regulatory cytokine-producing Tc2 and Tc17 CD8+ T cells were significantly elevated. Simultaneously, the Tc1 cell subsets displayed a negative correlation with immunoglobulin G, rheumatoid factor, the Schirmer test and unstimulated saliva flow. On the other hand, the Tc2 cell subsets exhibited a positive correlation with these parameters. In summary, our study indicated that immune dysfunction within CD8+ T cells, including alterations in Tc1 cells, plays a significant role in the development of pSS.

Inflammatory and Anti-Inflammatory Parameters in PCOS Patients Depending on Body Mass Index: A Case-Control Study.

BACKGROUND: it has been suggested that chronic low-grade inflammation plays an important role in the pathogenesis of polycystic ovary syndrome (PCOS). According to previous studies, it remains unclear which cytokines influence the development of this syndrome and whether their increase is associated with the presence of excess weight/obesity or is an independent factor. The aim of our research was to determine the parameters of chronic inflammation in women with PCOS in comparison with healthy women in the normal weight and the overweight subgroups. METHODS: This case-control study included 44 patients with PCOS (19 women with a body mass index (BMI) < 25 kg/m² and 25 women with a BMI ≥ 25 kg/m²) and 45 women without symptoms of PCOS (22 women with a BMI < 25 kg/m² and 23 women with a BMI ≥ 25 kg/m²). Thirty-two cytokines were analyzed in the plasma of the participants using Immunology multiplex assay HCYTA-60K-PX48 (Merck Life Science, LLC, Germany). RESULTS: Cytokines: interleukin-1 receptor antagonist (IL-1 RA), IL-2, IL-6, IL-17 E, IL-17 A, IL-18, and macrophage inflammatory protein-1 alpha (MIP-1 α) were increased in women with PCOS compared to controls, both in lean and overweight/obese subgroups (p < 0.05). Moreover, only lean women with PCOS had higher levels of IL-1 alpha, IL-4, IL-9, IL-12, IL-13, IL-15, tumor necrosis factor (TNF- α) alpha and beta, soluble CD40 and its ligand (SCD40L), fractalkine (FKN), monocyte-chemotactic protein 3 (MCP-3), and MIP-1 ß compared to the control group (p < 0.05). IL-22 was increased in the combined group of women with PCOS (lean and overweight/obese) compared to the control group (p = 0.012). CONCLUSION: Chronic low-grade inflammation is an independent factor affecting the occurrence of PCOS and does not depend on the presence of excess weight/obesity. For the first time, we obtained data on the increase in such inflammatory parameters as IL-9, MCP-3, and MIP-1α in women with PCOS.

Mechanism of TLR4-Mediated Anti-Inflammatory Response Induced by Exopolysaccharide from the Probiotic Bacillus subtilis.

Intestinal inflammatory diseases affect millions of people worldwide, and one class of drugs showing promise toward treatment of several inflammatory diseases is probiotics. Numerous studies have been performed using probiotics to prevent and treat intestinal inflammatory diseases. Most of these studies used intact bacteria, and neither the active molecule nor the molecular mechanisms by which they affect immune responses are known. We have shown that the probiotic Bacillus subtilis is anti-inflammatory and can protect mice from acute colitis induced by the enteric pathogen Citrobacter rodentium. We identified and purified the active molecule, exopolysaccharide (EPS), and showed that it protects mice from C. rodentium-induced colitis by inducing anti-inflammatory M2 macrophages or inhibitory dendritic cells (DCs), both of which inhibit excessive T cell responses. We showed previously that EPS affects macrophages and DCs in a TLR4-dependent manner, and in the current study we asked how EPS induces these anti-inflammatory cells and how they function to inhibit T cells. By investigating the signaling downstream of TLR4 that leads to acquisition of inhibitory properties of macrophages and DCs, we found that EPS induces expression of the inhibitory molecule IDO in bone marrow-derived DCs, and that inhibition of T cell proliferation by IDO-expressing bone marrow-derived DCs utilizes the kynurenine/aryl hydrocarbon receptor circuit. Furthermore, unlike LPS, EPS does not induce inflammatory cytokines upon injection in vivo, directly demonstrating different outcomes induced by two different TLR4 agonists.

Artificial intelligence for natural product drug discovery.

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Death of tonsillar B cells by NETosis.

Regulating B cell death is essential for generating antibodies and maintaining immune tolerance. B cells can die by apoptosis, and we report that human tonsil B cells, but not peripheral blood B cells also die by NETosis. This cell death is density-dependent, characterized by the loss of cell and nuclear membrane integrity, release of reactive oxygen species, and chromatin decondensation. Tonsil B cells secrete high levels of TNF, and inhibiting TNF prevented chromatin decondensation. By in situ fluorescence microscopy, B cell NETosis, as identified by the hyper citrullination of Histone-3, was localized to the light zone (LZ) of germinal centers in normal tonsil and overlapped with the B cell marker CD19/IgM. We propose a model in which stimulation of B cells in the LZ induces NETosis, driven in part by TNF. We also provide evidence that NETosis of tonsil B cells may be inhibited by an unidentified factor in tonsil. The results describe a previously unidentified form of B cell death and suggest a new mechanism to maintain B cell homeostasis during immune responses.

GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES.

Glycans are important polysaccharides on cellular surfaces that are bound to glycoproteins and glycolipids. These are one of the most common post-translational modifications of proteins in eukaryotic cells. They play important roles in protein folding, cell-cell interactions, and other extracellular processes. Changes in glycan structures may influence the course of different diseases, such as infections or cancer. Glycans are commonly represented using the IUPAC-condensed notation. IUPAC-condensed is a textual representation of glycans operating on the same topological level as the Symbol Nomenclature for Glycans (SNFG) that assigns colored, geometrical shapes to the main monomers. These symbols are then connected in tree-like structures, visualizing the glycan structure on a topological level. Yet for a representation on the atomic level, notations such as SMILES should be used. To our knowledge, there is no easy-to-use, general, open-source, and offline tool to convert the IUPAC-condensed notation to SMILES. Here, we present the open-access Python package GlyLES for the generalizable generation of SMILES representations out of IUPAC-condensed representations. GlyLES uses a grammar to read in the monomer tree from the IUPAC-condensed notation. From this tree, the tool can compute the atomic structures of each monomer based on their IUPAC-condensed descriptions. In the last step, it merges all monomers into the atomic structure of a glycan in the SMILES notation. GlyLES is the first package that allows conversion from the IUPAC-condensed notation of glycans to SMILES strings. This may have multiple applications, including straightforward visualization, substructure search, molecular modeling and docking, and a new featurization strategy for machine-learning algorithms. GlyLES is available at https://github.com/kalininalab/GlyLES .

Unusual peptide-binding proteins guide pyrroloindoline alkaloid formation in crocagin biosynthesis.

Ribosomally synthesized and post-translationally modified peptide natural products have provided many highly unusual scaffolds. This includes the intriguing alkaloids crocagins, which possess a tetracyclic core structure and whose biosynthesis has remained enigmatic. Here we use in vitro experiments to demonstrate that three proteins, CgnB, CgnC and CgnE, are sufficient for the production of the hallmark tetracyclic crocagin core from the precursor peptide CgnA. The crystal structures of the homologues CgnB and CgnE reveal them to be the founding members of a peptide-binding protein family and allow us to rationalize their distinct functions. We further show that the hydrolase CgnD liberates the crocagin core scaffold, which is subsequently N-methylated by CgnL. These insights allow us to propose a biosynthetic scheme for crocagins. Bioinformatic analyses based on these data led to the discovery of related biosynthetic pathways that may provide access to a structurally diverse family of peptide-derived pyrroloindoline alkaloids.

MetaProFi: an ultrafast chunked Bloom filter for storing and querying protein and nucleotide sequence data for accurate identification of functionally relevant genetic variants.

MOTIVATION: Bloom filters are a popular data structure that allows rapid searches in large sequence datasets. So far, all tools work with nucleotide sequences; however, protein sequences are conserved over longer evolutionary distances, and only mutations on the protein level may have any functional significance. RESULTS: We present MetaProFi, a Bloom filter-based tool that, for the first time, offers the functionality to build indexes of amino acid sequences and query them with both amino acid and nucleotide sequences, thus bringing sequence comparison to the biologically relevant protein level. MetaProFi implements additional efficient engineering solutions, such as a shared memory system, chunked data storage and efficient compression. In addition to its conceptual novelty, MetaProFi demonstrates state-of-the-art performance and excellent memory consumption-to-speed ratio when applied to various large datasets. AVAILABILITY AND IMPLEMENTATION: Source code in Python is available at https://github.com/kalininalab/metaprofi.

Simulated Tempering-Enhanced Umbrella Sampling Improves Convergence of Free Energy Calculations of Drug Membrane Permeation.

Molecular dynamics simulations have been widely used to study solute permeation across biological membranes. The potential of mean force (PMF) for solute permeation is typically computed using enhanced sampling techniques such as umbrella sampling (US). For bulky drug-like permeants, however, obtaining converged PMFs remains challenging and often requires long simulation times, resulting in an unacceptable computational cost. Here, we augmented US with simulated tempering (ST), an extended-ensemble technique that consists in varying the temperature of the system along a pre-defined temperature ladder. Simulated tempering-enhanced US (STeUS) was employed to improve the convergence of PMF calculations for the permeation of methanol and three common drug molecules. To obtain sufficient sampling of the umbrella histograms, which were computed only from the ground temperature, we modified the simulation time fraction spent at the ground temperature between 1/K and 50%, where K is the number of ST temperature states. We found that STeUS accelerates convergence, when compared to standard US, and that the benefit of STeUS is system-dependent. For bulky molecules, for which standard US poorly converged, the application of ST was highly successful, leading to a more than fivefold accelerated convergence of the PMFs. For the small methanol solute, for which conventional US converges moderately, the application of ST is only beneficial if 50% of the STeUS simulation time is spent at the ground temperature. This study establishes STeUS as an efficient and simple method for PMF calculations, thereby strongly reducing the computational cost of routine high-throughput studies of drug permeability.

Smart city perspectives in post-pandemic governance: Externalities reduction policy.

Background: The ongoing COVID-19 quarantine restrictions have caused multiple sharp decreases in activities associated with the movement of large masses of people. The economies of regions and cities that are critically dependent on tourist flows related to various segments have suffered. This research aims to provide an economic-mathematical model of smart cities externalities' impact from the point of view of achieving social and environmental goals Methods: The objective of this study was to develop an algorithm for supporting decision-makers. Methods of mathematical modeling, statistical processing of data received in real-time, as well as methods for finding solutions by expansion into dynamic series are used, and the theory of mathematical games is applied. The theoretical mathematical model presented considers the statistical processing of data provided in real time referring to the performance indicators of megacities. Results: The activities of administrations and governments aimed at maintaining stability over the past two years have been aimed at reducing the negative impact of the pandemic. The prospect of returning to normal conditions is complicated by a number of factors. The proposed approach allows the development of the fundamental basis for making administrative decisions within individual megapolises and in environmental policy on a territory of any scale. The developed mathematical model is abstract by definition and is applied by taking into account specific tasks and criteria. Since the tasks of the administration differ depending on the region and country, the choice of criteria is set individually. Conclusions: During the period of isolation, the volume of services in the Hotel - Restaurant- Catering/Café (HORECA) segment has decreased, and personnel has also been lost. The reduced pressure on public infrastructure and the departure of migrants means that, in the long term, this work cannot be restored within a short period of time.

Intestinal microbiome changes in an infant with right atrial isomerism and recurrent necrotizing enterocolitis: A case report and review of literature.

BACKGROUND: Necrotizing enterocolitis (NEC) is a multifactorial disease that predominantly affects premature neonates. Intestinal dysbiosis plays a critical role in NEC pathogenesis in premature neonates. The main risk factor for NEC in term infants is mesenteric hypoperfusion associated with ductal-dependent congenital heart disease (CHD) that eventually leads to intestinal ischemia. The incidence of NEC in neonates with critical CHD is 6.8%-13%. However, the role of the intestinal microbiome in NEC pathogenesis in infants with ductal-dependent CHD remains unclear. CASE SUMMARY: A male term neonate with right atrial isomerism underwent modified Blalock-Taussig shunt placement on the 14th day of life and had persistent mesenteric hypoperfusion after surgery. The patient had episodes of NEC stage IIA on the 1st and 28th days after cardiac surgery. Fecal microbial composition was analyzed before and after cardiac surgery by sequencing region V4 of the 16S rRNA gene. Before surgery, species belonging to genera Veillonella and Clostridia and class Gammaproteobacteria were detected, Bifidobacteriaceae showed a low abundance. The first NEC episode was associated with postoperative hemodynamic instability, intestinal ischemia-reperfusion injury during cardiopulmonary bypass, and a high abundance of Clostridium paraputrificum (Clostridium sensu stricto I) (56.1%). Antibacterial therapy after the first NEC episode resulted in increased abundance of Gammaproteobacteria, decreased abundance of Firmicutes, and low alpha diversity. These changes in the microbial composition promoted the growth of Clostridium sensu stricto I (72.0%) before the second NEC episode. CONCLUSION: A high abundance of Clostridium sensu stricto I and mesenteric hypoperfusion may have contributed to NEC in the present case.

Modeling the Effect of Hydrophobicity on the Passive Permeation of Solutes across a Bacterial Model Membrane.

Passive diffusion across biomembranes is an important mechanism of permeation for multiple drugs, including antibiotics. However, this process is frequently neglected while studying drug uptake and, in our view, warrants further investigation. Here, we apply molecular dynamics simulations to investigate the impact of changes in molecular hydrophobicity on the permeability of a series of inhibitors of the quorum sensing of Pseudomonas aeruginosa, previously discovered by us, across a membrane model. Overall, we show that permeation across this membrane model does not correlate with the molecule's hydrophobicity. We demonstrate that using a simple model for permeation, based on the difference between the maximum and minimum of the free energy profile, outperforms the inhomogeneous solubility-diffusion model, yielding a permeability ranking that better agrees with the experimental results, especially for hydrophobic permeants. The calculated differences in permeability could not explain differences in in bacterio activity. Nevertheless, substantial differences in molecular orientation along the permeation pathway correlate with the in bacterio activity, emphasizing the importance of analyzing, at an atomistic level, the permeation pathway of these solutes.

d-StructMAn: Containerized structural annotation on the scale from genetic variants to whole proteomes.

BACKGROUND: Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution. RESULTS: We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins. CONCLUSIONS: d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.