Correction to "Targeting the Spike: Repurposing Mithramycin and Dihydroergotamine to Block SARS-CoV-2 Infection".

[This corrects the article DOI: 10.1021/acsomega.3c02921.].

Targeting the Spike: Repurposing Mithramycin and Dihydroergotamine to Block SARS-CoV-2 Infection.

The urgency to find complementary therapies to current SARS-CoV-2 vaccines, whose effectiveness is preserved over time and not compromised by the emergence of new and emerging variants, has become a critical health challenge. We investigate the possibility of jamming the opening of the Receptor Binding Domain (RBD) of the spike protein of SARS-CoV-2 with small compounds. Through in silico screening, we identified two potential candidates that would lock the Receptor Binding Domain (RBD) in a closed configuration, preventing the virus from infecting the host cells. We show that two drugs already approved by the FDA, mithramycin and dihydroergotamine, can block infection using concentrations in the µM range in cell-based assays. Further STD-NMR experiments support dihydroergotamine's direct interaction with the spike protein. Overall, our results indicate that repurposing of these compounds might lead to potential clinical drug candidates for the treatment of SARS-CoV-2 infection.

Ecology and Machine Learning-Based Classification Models of Gut Microbiota and Inflammatory Markers May Evaluate the Effects of Probiotic Supplementation in Patients Recently Recovered from COVID-19.

Gut microbiota (GM) modulation can be investigated as possible solution to enhance recovery after COVID-19. An open-label, single-center, single-arm, pilot, interventional study was performed by enrolling twenty patients recently recovered from COVID-19 to investigate the role of a mixed probiotic, containing Lactobacilli, Bifidobacteria and Streptococcus thermophilus, on gastrointestinal symptoms, local and systemic inflammation, intestinal barrier integrity and GM profile. Gastrointestinal Symptom Rating Scale, cytokines, inflammatory, gut permeability, and integrity markers were evaluated before (T0) and after 8 weeks (T1) of probiotic supplementation. GM profiling was based on 16S-rRNA targeted-metagenomics and QIIME 2.0, LEfSe and PICRUSt computational algorithms. Multiple machine learning (ML) models were trained to classify GM at T0 and T1. A statistically significant reduction of IL-6 (p < 0.001), TNF-α (p < 0.001) and IL-12RA (p < 0.02), citrulline (p value < 0.001) was reported at T1. GM global distribution and microbial biomarkers strictly reflected probiotic composition, with a general increase in Bifidobacteria at T1. Twelve unique KEGG orthologs were associated only to T0, including tetracycline resistance cassettes. ML classified the GM at T1 with 100% score at phylum level. Bifidobacteriaceae and Bifidobacterium spp. inversely correlated to reduction of citrulline and inflammatory cytokines. Probiotic supplementation during post-COVID-19 may trigger anti-inflammatory effects though Bifidobacteria and related-metabolism enhancement.

The Functional Characteristics of Goat Cheese Microbiota from a One-Health Perspective.

Goat cheese is an important element of the Mediterranean diet, appreciated for its health-promoting features and unique taste. A pivotal role in the development of these characteristics is attributed to the microbiota and its continuous remodeling over space and time. Nevertheless, no thorough study of the cheese-associated microbiota using two metaomics approaches has previously been conducted. Here, we employed 16S rRNA gene sequencing and metaproteomics to explore the microbiota of a typical raw goat milk cheese at various ripening timepoints and depths of the cheese wheel. The 16S rRNA gene-sequencing and metaproteomics results described a stable microbiota ecology across the selected ripening timepoints, providing evidence for the microbiologically driven fermentation of goat milk products. The important features of the microbiota harbored on the surface and in the core of the cheese mass were highlighted in both compositional and functional terms. We observed the rind microbiota struggling to maintain the biosafety of the cheese through competition mechanisms and/or by preventing the colonization of the cheese by pathobionts of animal or environmental origin. The core microbiota was focused on other biochemical processes, supporting its role in the development of both the health benefits and the pleasant gustatory nuances of goat cheese.

Gut Microbiota Functional Traits, Blood pH, and Anti-GAD Antibodies Concur in the Clinical Characterization of T1D at Onset.

Alterations of gut microbiota have been identified before clinical manifestation of type 1 diabetes (T1D). To identify the associations amongst gut microbiome profile, metabolism and disease markers, the 16S rRNA-based microbiota profiling and 1H-NMR metabolomic analysis were performed on stool samples of 52 T1D patients at onset, 17 T1D siblings and 57 healthy subjects (CTRL). Univariate, multivariate analyses and classification models were applied to clinical and -omic integrated datasets. In T1D patients and their siblings, Clostridiales and Dorea were increased and Dialister and Akkermansia were decreased compared to CTRL, while in T1D, Lachnospiraceae were higher and Collinsella was lower, compared to siblings and CTRL. Higher levels of isobutyrate, malonate, Clostridium, Enterobacteriaceae, Clostridiales, Bacteroidales, were associated to T1D compared to CTRL. Patients with higher anti-GAD levels showed low abundances of Roseburia, Faecalibacterium and Alistipes and those with normal blood pH and low serum HbA1c levels showed high levels of purine and pyrimidine intermediates. We detected specific gut microbiota profiles linked to both T1D at the onset and to diabetes familiarity. The presence of specific microbial and metabolic profiles in gut linked to anti-GAD levels and to blood acidosis can be considered as predictive biomarker associated progression and severity of T1D.

The Relationship Between Pediatric Gut Microbiota and SARS-CoV-2 Infection.

This is the first study on gut microbiota (GM) in children affected by coronavirus disease 2019 (COVID-19). Stool samples from 88 patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and 95 healthy subjects were collected (admission: 3-7 days, discharge) to study GM profile by 16S rRNA gene sequencing and relationship to disease severity. The study group was divided in COVID-19 (68), Non-COVID-19 (16), and MIS-C (multisystem inflammatory syndrome in children) (4). Correlations among GM ecology, predicted functions, multiple machine learning (ML) models, and inflammatory response were provided for COVID-19 and Non-COVID-19 cohorts. The GM of COVID-19 cohort resulted as dysbiotic, with the lowest α-diversity compared with Non-COVID-19 and CTRLs and by a specific ß-diversity. Its profile appeared enriched in Faecalibacterium, Fusobacterium, and Neisseria and reduced in Bifidobacterium, Blautia, Ruminococcus, Collinsella, Coprococcus, Eggerthella, and Akkermansia, compared with CTRLs (p < 0.05). All GM paired-comparisons disclosed comparable results through all time points. The comparison between COVID-19 and Non-COVID-19 cohorts highlighted a reduction of Abiotrophia in the COVID-19 cohort (p < 0.05). The GM of MIS-C cohort was characterized by an increase of Veillonella, Clostridium, Dialister, Ruminococcus, and Streptococcus and a decrease of Bifidobacterium, Blautia, Granulicatella, and Prevotella, compared with CTRLs. Stratifying for disease severity, the GM associated to "moderate" COVID-19 was characterized by lower α-diversity compared with "mild" and "asymptomatic" and by a GM profile deprived in Neisseria, Lachnospira, Streptococcus, and Prevotella and enriched in Dialister, Acidaminococcus, Oscillospora, Ruminococcus, Clostridium, Alistipes, and Bacteroides. The ML models identified Staphylococcus, Anaerostipes, Faecalibacterium, Dorea, Dialister, Streptococcus, Roseburia, Haemophilus, Granulicatella, Gemmiger, Lachnospira, Corynebacterium, Prevotella, Bilophila, Phascolarctobacterium, Oscillospira, and Veillonella as microbial markers of COVID-19. The KEGG ortholog (KO)-based prediction of GM functional profile highlighted 28 and 39 KO-associated pathways to COVID-19 and CTRLs, respectively. Finally, Bacteroides and Sutterella correlated with proinflammatory cytokines regardless disease severity. Unlike adult GM profiles, Faecalibacterium was a specific marker of pediatric COVID-19 GM. The durable modification of patients' GM profile suggested a prompt GM quenching response to SARS-CoV-2 infection since the first symptoms. Faecalibacterium and reduced fatty acid and amino acid degradation were proposed as specific COVID-19 disease traits, possibly associated to restrained severity of SARS-CoV-2-infected children. Altogether, this evidence provides a characterization of the pediatric COVID-19-related GM.

FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank.

This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids) as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies. This work also presents two sample use cases where maternal embryonic leucine zipper kinase and mesotrypsin are used as a target, being proteins of high biomedical relevance for the development of cancer therapies. Database URL: http://biochimica3.bio.uniroma3.it/fragments-web/.

Paediatric astroblastoma-like neuroepithelial tumour of the spinal cord with a MAMLD1-BEND2 rearrangement.

Astroblastomas are neuroepithelial tumours defined by the presence of MN1 rearrangement and are typically located in the cerebral hemispheres. Rare cases of astroblastoma-like tumours carrying an EWSR1-BEND2 fusion have been recently described in the brain stem and spinal cord. We report a paediatric case of neuroepithelial astroblastoma-like tumour occurring in the spine and carrying a novel MAMLD1-BEND2 fusion. We believe that our case aligns with the rare astroblastoma-like tumours with EWSR1-BEND2 fusion, in terms of non-hemispheric location, pathology, methylation profile and activation of BEND2 transcription. Whether they may represent a distinct entity or a variant of MN1-altered astroblastoma is not clear.

Insulin-Degrading Enzyme Is a Non Proteasomal Target of Carfilzomib and Affects the 20S Proteasome Inhibition by the Drug.

Carfilzomib is a last generation proteasome inhibitor (PI) with proven clinical efficacy in the treatment of relapsed/refractory multiple myeloma. This drug is considered to be extremely specific in inhibiting the chymotrypsin-like activity of the 20S proteasome, encoded by the ß5 subunit, overcoming some bortezomib limitations, the first PI approved for multiple myeloma therapy which is however burdened by a significant toxicity profile, due also to its off-target effects. Here, molecular approaches coupled with molecular docking studies have been used to unveil that the Insulin-Degrading Enzyme, a ubiquitous and highly conserved Zn2+ peptidase, often found to associate with proteasome in cell-based models, is targeted by carfilzomib in vitro. The drug behaves as a modulator of IDE activity, displaying an inhibitory effect over 10-fold lower than for the 20S. Notably, the interaction of IDE with the 20S enhances in vitro the inhibitory power of carfilzomib on proteasome, so that the IDE-20S complex is an even better target of carfilzomib than the 20S alone. Furthermore, IDE gene silencing after delivery of antisense oligonucleotides (siRNA) significantly reduced carfilzomib cytotoxicity in rMC1 cells, a validated model of Muller glia, suggesting that, in cells, the inhibitory activity of this drug on cell proliferation is somewhat linked to IDE and, possibly, also to its interaction with proteasome.

Binding of direct oral anticoagulants to the FA1 site of human serum albumin.

The anticoagulant therapy is widely used to prevent and treat thromboembolic events. Until the last decade, vitamin K antagonists were the only available oral anticoagulants; recently, direct oral anticoagulants (DOACs) have been developed. Since 55% to 95% of DOACs are bound to plasma proteins, the in silico docking and ligand-binding properties of drugs apixaban, betrixaban, dabigatran, edoxaban, and rivaroxaban and of the prodrug dabigatran etexilate to human serum albumin (HSA), the most abundant plasma protein, have been investigated. DOACs bind to the fatty acid (FA) site 1 (FA1) of ligand-free HSA, whereas they bind to the FA8 and FA9 sites of heme-Fe(III)- and myristic acid-bound HSA. DOACs binding to the FA1 site of ligand-free HSA has been validated by competitive inhibition of heme-Fe(III) recognition. Values of the dissociation equilibrium constant for DOACs binding to the FA1 site (ie, calc KDOAC ) derived from in silico docking simulations (ranging between 1.2 × 10-8 M and 1.4 × 10-6 M) agree with those determined experimentally from competitive inhibition of heme-Fe(III) binding (ie, exp KDOAC ; ranging between 2.5 × 10-7 M and 2.2 × 10-6 M). In addition, this study highlights the inequivalence of rivaroxaban binding to mammalian serum albumin. Given the HSA concentration in vivo (~7.5 × 10-4 M), values of KDOAC here determined indicate that the formation of the HSA:DOACs complexes in the absence and presence of FAs and heme-Fe(III) may occur in vivo. Therefore, HSA appears to be an important determinant for DOACs transport.

DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina.

MOTIVATION: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina's original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. RESULTS: By coupling intermolecular interaction, solvent accessible surface area features and Vina's energy terms, DockingApp RF's new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF's performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina's scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.

Computational Methods for the Identification of Molecular Targets of Toxic Food Additives. Butylated Hydroxytoluene as a Case Study.

Butylated hydroxytoluene (BHT) is one of the most commonly used synthetic antioxidants in food, cosmetic, pharmaceutical and petrochemical products. BHT is considered safe for human health; however, its widespread use together with the potential toxicological effects have increased consumers concern about the use of this synthetic food additive. In addition, the estimated daily intake of BHT has been demonstrated to exceed the recommended acceptable threshold. In the present work, using BHT as a case study, the usefulness of computational techniques, such as reverse screening and molecular docking, in identifying protein-ligand interactions of food additives at the bases of their toxicological effects has been probed. The computational methods here employed have been useful for the identification of several potential unknown targets of BHT, suggesting a possible explanation for its toxic effects. In silico analyses can be employed to identify new macromolecular targets of synthetic food additives and to explore their functional mechanisms or side effects. Noteworthy, this could be important for the cases in which there is an evident lack of experimental studies, as is the case for BHT.

Neonicotinoid trapping by the FA1 site of human serum albumin.

Neonicotinoids are a widely used class of insecticides that target the acetylcholine recognition site of the nicotinic acetylcholine receptors in the central nervous system of insects. Although neonicotinoids display a high specificity for insects, their use has been recently debated since several studies led to the hypothesis that they may have adverse ecological effects and potential risks to mammals and even humans. Due to their hydrophobic nature, neonicotinoids need specific carriers to allow their distribution in body fluids. Human serum albumin (HSA), the most abundant plasma protein, is a key carrier of endogenous and exogenous compounds. The in silico docking and ligand binding properties of acetamiprid, clothianidin, dinotefuran, imidacloprid, nitenpyram, thiacloprid, and thiamethoxam to HSA are here reported. Neonicotinoids bind to multiple fatty acid (FA) binding sites, preferentially to the FA1 pocket, with high affinity. Values of the dissociation equilibrium constant for neonicotinoid binding FA1 of HSA (i.e., calc Kn ) derived from in silico docking simulations (ranging between 3.9 × 10-5 and 6.3 × 10-4 M) agree with those determined experimentally from competitive inhibition of heme-Fe(III) binding (i.e., exp Kn ; ranging between 2.1 × 10-5 and 6.9 × 10-5 M). Accounting for the HSA concentration in vivo (~7.5 10-4 M), values of Kn here determined suggest that the formation of the HSA:neonicotinoid complexes may occur in vivo. Therefore, HSA appears to be an important determinant for neonicotinoid transport and distribution to tissues and organs, particularly to the liver where they are metabolized.

Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies.

In the current "genomic era" the number of identified genes is growing exponentially. However, the biological function of a large number of the corresponding proteins is still unknown. Recognition of small molecule ligands (e.g., substrates, inhibitors, allosteric regulators, etc.) is pivotal for protein functions in the vast majority of the cases and knowledge of the region where these processes take place is essential for protein function prediction and drug design. In this regard, computational methods represent essential tools to tackle this problem. A significant number of software tools have been developed in the last few years which exploit either protein sequence information, structure information or both. This review describes the most recent developments in protein function recognition and binding site prediction, in terms of both freely-available and commercial solutions and tools, detailing the main characteristics of the considered tools and providing a comparative analysis of their performance.

Fragment-Based Ligand-Protein Contact Statistics: Application to Docking Simulations.

In this work, the information contained in the contacts between fragments of small-molecule ligands and protein residues has been collected and its exploitability has been verified by using the scoring of docking simulations as a test case for bringing about a proof of concept. Contact statistics between small-molecule fragments and binding site residues were collected and analyzed using a dataset composed of 200,000+ binding sites and associated ligands, derived from the database of the LIBRA ligand binding site recognition software, as a starting point. The fragments were generated by applying the decomposition algorithm implemented in BRICS. A simple "potential" based on the contact frequencies was tested against the CASF-2013 benchmark; its performance was then evaluated through the rescoring of docking poses generated for the DUD-E dataset. The results obtained indicate that this approach, its simplicity notwithstanding, yields promising results that are comparable, and in some cases, superior, to those obtained with other, more complex scoring functions.

Multiple molecular dynamics simulations of human LOX-1 and Trp150Ala mutant reveal the structural determinants causing the full deactivation of the receptor.

Multiple classical molecular dynamics simulations have been applied to the human LOX-1 receptor to clarify the role of the Trp150Ala mutation in the loss of binding activity. Results indicate that the substitution of this crucial residue, located at the dimer interface, markedly disrupts the wild-type receptor dynamics. The mutation causes an irreversible rearrangement of the subunits interaction pattern that in the wild-type protein allows the maintaining of a specific symmetrical motion of the monomers. The subunits dislocation determines a loss of linearity of the arginines residues composing the basic spine and a consequent alteration of the long-range electrostatic attraction of the substrate. Moreover, the anomalous subunits arrangement observed in the mutated receptor also affects the integrity of the hydrophobic tunnel, actively involved in the short-range hydrophobic recognition of the substrate. The combined effect of these structural rearrangements generates the impairing of the receptor function.