From Primary Data to Ethnopharmacological Investigations on Achillea erba-rotta subsp. moschata (Wulfen) I.Richardson as a Remedy against Gastric Ailments in Valmalenco (Italy).

(1) Background: Within the framework of the European Interreg Italy-Switzerland B-ICE & Heritage project (2018-2022), this study originated from a three-year ethnobotanical survey in Valmalenco (Sondrio, Italy). Following a preliminary work published by our group, this research further explored the folk therapeutic use of Achillea erba-rotta subsp. moschata (Wulfen) I.Richardson (Asteraceae) for dyspepsia disorders, specifically its anti-inflammatory potential at a gastrointestinal level. (2) Methods: Semi-structured interviews were performed. The bitter taste was investigated through molecular docking software (PLANTS, GOLD), while the anti-inflammatory activity of the hydroethanolic extract, infusion, and decoction was evaluated based on the release of IL-8 and IL-6 after treatment with TNFα or Helicobacter pylori. The minimum inhibitory concentration and bacterial adhesion on the gastric epithelium were evaluated. (3) Results: In total, 401 respondents were interviewed. Molecular docking highlighted di-caffeoylquinic acids as the main compounds responsible for the interaction with bitter taste receptors. The moderate inhibition of IL-6 and IL-8 release was recorded, while, in the co-culture with H. pylori, stronger anti-inflammatory potential was expressed (29-45 µg/mL). The concentration-dependent inhibition of H. pylori growth was recorded (MIC = 100 µg/mL), with a significant anti-adhesive effect. (4) Conclusions: Confirming the folk tradition, the study emphasizes the species' potentiality for dyspepsia disorders. Future studies are needed to identify the components mostly responsible for the biological effects.

Discovery of a septin-4 covalent binder with antimetastatic activity in a mouse model of melanoma.

Cancer spreading through metastatic processes is one of the major causes of tumour-related mortality. Metastasis is a complex phenomenon which involves multiple pathways ranging from cell metabolic alterations to changes in the biophysical phenotype of cells and tissues. In the search for new effective anti-metastatic agents, we modulated the chemical structure of the lead compound AA6, in order to find the structural determinants of activity, and to identify the cellular target responsible of the downstream anti-metastatic effects observed. New compounds synthesized were able to inhibit in vitro B16-F10 melanoma cell invasiveness, and one selected compound, CM365, showed in vivo anti-metastatic effects in a lung metastasis mouse model of melanoma. Septin-4 was identified as the most likely molecular target responsible for these effects. This study showed that CM365 is a promising molecule for metastasis prevention, remarkably effective alone or co-administered with drugs normally used in cancer therapy, such as paclitaxel.

"DompeKeys": a set of novel substructure-based descriptors for efficient chemical space mapping, development and structural interpretation of machine learning models, and indexing of large databases.

The conversion of chemical structures into computer-readable descriptors, able to capture key structural aspects, is of pivotal importance in the field of cheminformatics and computer-aided drug design. Molecular fingerprints represent a widely employed class of descriptors; however, their generation process is time-consuming for large databases and is susceptible to bias. Therefore, descriptors able to accurately detect predefined structural fragments and devoid of lengthy generation procedures would be highly desirable. To meet additional needs, such descriptors should also be interpretable by medicinal chemists, and suitable for indexing databases with trillions of compounds. To this end, we developed-as integral part of EXSCALATE, Dompé's end-to-end drug discovery platform-the DompeKeys (DK), a new substructure-based descriptor set, which encodes the chemical features that characterize compounds of pharmaceutical interest. DK represent an exhaustive collection of curated SMARTS strings, defining chemical features at different levels of complexity, from specific functional groups and structural patterns to simpler pharmacophoric points, corresponding to a network of hierarchically interconnected substructures. Because of their extended and hierarchical structure, DK can be used, with good performance, in different kinds of applications. In particular, we demonstrate how they are very well suited for effective mapping of chemical space, as well as substructure search and virtual screening. Notably, the incorporation of DK yields highly performing machine learning models for the prediction of both compounds' activity and metabolic reaction occurrence. The protocol to generate the DK is freely available at https://dompekeys.exscalate.eu and is fully integrated with the Molecular Anatomy protocol for the generation and analysis of hierarchically interconnected molecular scaffolds and frameworks, thus providing a comprehensive and flexible tool for drug design applications.

Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

Human carnosinases: A brief history, medicinal relevance, and in silico analyses.

Carnosine, an endogenous dipeptide, has been found to have a plethora of medicinal properties, such as antioxidant, antiageing, and chelating effects, but with one downside: a short half-life. Carnosinases and two hydrolytic enzymes, which remain enigmatic, are responsible for these features. Hence, here we emphasize why research is valuable for better understanding crucial concepts like ageing, neurodegradation, and cancerogenesis, given that inhibition of carnosinases might significantly prolong carnosine bioavailability and allow its further use in medicine. Herein, we explore the literature regarding carnosinases and present a short in silico analysis aimed at elucidating the possible recognition pattern between CN1 and its ligands.

Structure of human TRPM8 channel.

TRPM8 is a non-selective cation channel permeable to both monovalent and divalent cations that is activated by multiple factors, such as temperature, voltage, pressure, and changes in osmolality. It is a therapeutic target for anticancer drug development, and its modulators can be utilized for several pathological conditions. Here, we present a cryo-electron microscopy structure of a human TRPM8 channel in the closed state that was solved at 2.7 Å resolution. Our structure comprises the most complete model of the N-terminal pre-melastatin homology region. We also visualized several lipids that are bound by the protein and modeled how the human channel interacts with icilin. Analyses of pore helices in available TRPM structures showed that all these structures can be grouped into different closed, desensitized and open state conformations based on the register of the pore helix S6 which positions particular amino acid residues at the channel constriction.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database.

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

MEDIATE - Molecular DockIng at homE: Turning collaborative simulations into therapeutic solutions.

INTRODUCTION: Collaborative computing has attracted great interest in the possibility of joining the efforts of researchers worldwide. Its relevance has further increased during the pandemic crisis since it allows for the strengthening of scientific collaborations while avoiding physical interactions. Thus, the E4C consortium presents the MEDIATE initiative which invited researchers to contribute via their virtual screening simulations that will be combined with AI-based consensus approaches to provide robust and method-independent predictions. The best compounds will be tested, and the biological results will be shared with the scientific community. AREAS COVERED: In this paper, the MEDIATE initiative is described. This shares compounds' libraries and protein structures prepared to perform standardized virtual screenings. Preliminary analyses are also reported which provide encouraging results emphasizing the MEDIATE initiative's capacity to identify active compounds. EXPERT OPINION: Structure-based virtual screening is well-suited for collaborative projects provided that the participating researchers work on the same input file. Until now, such a strategy was rarely pursued and most initiatives in the field were organized as challenges. The MEDIATE platform is focused on SARS-CoV-2 targets but can be seen as a prototype which can be utilized to perform collaborative virtual screening campaigns in any therapeutic field by sharing the appropriate input files.

Chemoenzymatic approach towards the synthesis of the antitumor and antileishmanial marine metabolite (+)-Harzialactone A via the stereoselective, biocatalyzed reduction of a prochiral ketone.

As a rich source of biological active compounds, marine natural products have been increasingly screened as candidates for developing new drugs. Among the several marine products and metabolites, (+)-Harzialactone A has drawn considerable attention for its antitumor and antileishmanial activity. In this work a chemoenzymatic approach has been implemented for the preparation of the marine metabolite (+)-Harzialactone A. The synthesis involved a stereoselective, biocatalyzed reduction of the prochiral ketone 4-oxo-5-phenylpentanoic acid or the corresponding esters, all generated by chemical reactions. A collection of different promiscuous oxidoreductases (both wild-type and engineered) and diverse microorganism strains were investigated to mediate the bioconversions. After co-solvent and co-substrate investigation in order to enhance the bioreduction performance, T. molischiana in presence of NADES (choline hydrochloride-glucose) and ADH442 were identified as the most promising biocatalysts, allowing the obtainment of the (S)-enantiomer with excellent ee (97% to > 99% respectively) and good to excellent conversion (88% to 80% respectively). The successful attempt in this study provides a new chemoenzymatic approach for the synthesis of (+)-Harzialactone A.

The VEGA web service: multipurpose online tools for molecular modelling and docking analyses.

The paper presents the VEGA Online web service, which includes a set of freely available tools deriving from the development of the VEGA suite of programs. In detail, the paper is focused on two tools: the VEGA Web Edition (WE) and the Score tool. The former is a versatile file format converter including relevant features for 2D/3D conversion, for surface mapping and for editing/preparing input files. The Score application allows rescoring docking poses and in particular includes the MLP Interactions Scores (MLPInS) for describing hydrophobic interactions. To the best of our knowledge, this web service is the only available resource by which one can calculate both the virtual log P of a given input molecule according to the MLP approach plus the corresponding MLP surface.

Ensemble of structure and ligand-based classification models for hERG liability profiling.

Drug-induced cardiotoxicity represents one of the most critical safety concerns in the early stages of drug development. The blockade of the human ether-à-go-go-related potassium channel (hERG) is the most frequent cause of cardiotoxicity, as it is associated to long QT syndrome which can lead to fatal arrhythmias. Therefore, assessing hERG liability of new drugs candidates is crucial to avoid undesired cardiotoxic effects. In this scenario, computational approaches have emerged as useful tools for the development of predictive models able to identify potential hERG blockers. In the last years, several efforts have been addressed to generate ligand-based (LB) models due to the lack of experimental structural information about hERG channel. However, these methods rely on the structural features of the molecules used to generate the model and often fail in correctly predicting new chemical scaffolds. Recently, the 3D structure of hERG channel has been experimentally solved enabling the use of structure-based (SB) strategies which may overcome the limitations of the LB approaches. In this study, we compared the performances achieved by both LB and SB classifiers for hERG-related cardiotoxicity developed by using Random Forest algorithm and employing a training set containing 12789 hERG binders. The SB models were trained on a set of scoring functions computed by docking and rescoring calculations, while the LB classifiers were built on a set of physicochemical descriptors and fingerprints. Furthermore, models combining the LB and SB features were developed as well. All the generated models were internally validated by ten-fold cross-validation on the TS and further verified on an external test set. The former revealed that the best performance was achieved by the LB model, while the model combining the LB and the SB attributes displayed the best results when applied on the external test set highlighting the usefulness of the integration of LB and SB features in correctly predicting unseen molecules. Overall, our predictive models showed satisfactory performances providing new useful tools to filter out potential cardiotoxic drug candidates in the early phase of drug discovery.

Obesity and Type 2 Diabetes: Adiposopathy as a Triggering Factor and Therapeutic Options.

Obesity and type 2 diabetes (T2DM) are major public health concerns associated with serious morbidity and increased mortality. Both obesity and T2DM are strongly associated with adiposopathy, a term that describes the pathophysiological changes of the adipose tissue. In this review, we have highlighted adipose tissue dysfunction as a major factor in the etiology of these conditions since it promotes chronic inflammation, dysregulated glucose homeostasis, and impaired adipogenesis, leading to the accumulation of ectopic fat and insulin resistance. This dysfunctional state can be effectively ameliorated by the loss of at least 15% of body weight, that is correlated with better glycemic control, decreased likelihood of cardiometabolic disease, and an improvement in overall quality of life. Weight loss can be achieved through lifestyle modifications (healthy diet, regular physical activity) and pharmacotherapy. In this review, we summarized different effective management strategies to address weight loss, such as bariatric surgery and several classes of drugs, namely metformin, GLP-1 receptor agonists, amylin analogs, and SGLT2 inhibitors. These drugs act by targeting various mechanisms involved in the pathophysiology of obesity and T2DM, and they have been shown to induce significant weight loss and improve glycemic control in obese individuals with T2DM.

Targeting the multifaceted neurotoxicity of Alzheimer's disease by tailored functionalisation of the curcumin scaffold.

Simultaneous modulation of multifaceted toxicity arising from neuroinflammation, oxidative stress, and mitochondrial dysfunction represents a valuable therapeutic strategy to tackle Alzheimer's disease. Among the significant hallmarks of the disorder, Aß protein and its aggregation products are well-recognised triggers of the neurotoxic cascade. In this study, by tailored modification of the curcumin-based lead compound 1, we aimed at developing a small library of hybrid compounds targeting Aß protein oligomerisation and the consequent neurotoxic events. Interestingly, from in vitro studies, analogues 3 and 4, bearing a substituted triazole moiety, emerged as multifunctional agents able to counteract Aß aggregation, neuroinflammation and oxidative stress. In vivo proof-of-concept evaluations, performed in a Drosophila oxidative stress model, allowed us to identify compound 4 as a promising lead candidate.

An integrated metabolomic and proteomic approach for the identification of covalent inhibitors of the main protease (Mpro) of SARS-COV-2 from crude natural extracts.

Mpro represents one of the most promising drug targets for SARS-Cov-2, as it plays a crucial role in the maturation of viral polyproteins into functional proteins. HTS methods are currently used to screen Mpro inhibitors, and rely on searching chemical databases and compound libraries, meaning that they only consider previously structurally clarified and isolated molecules. A great advancement in the hit identification strategy would be to set-up an approach aimed at exploring un-deconvoluted mixtures of compounds such as plant extracts. Hence, the aim of the present study is to set-up an analytical platform able to fish-out bioactive molecules from complex natural matrices even where there is no knowledge on the constituents. The proposed approach begins with a metabolomic step aimed at annotating the MW of the matrix constituents. A further metabolomic step is based on identifying those natural electrophilic compounds able to form a Michael adduct with thiols, a peculiar chemical feature of many Mpro inhibitors that covalently bind the catalytic Cys145 in the active site, thus stabilizing the complex. A final step consists of incubating recombinant Mpro with natural extracts and identifying compounds adducted to the residues within the Mpro active site by bottom-up proteomic analysis (nano-LC-HRMS). Data analysis is based on two complementary strategies: (i) a targeted search applied by setting the adducted moieties identified as Michael acceptors of Cys as variable modifications; (ii) an untargeted approach aimed at identifying the whole range of adducted peptides containing Cys145 on the basis of the characteristic b and y fragment ions independent of the adduct. The method was set-up and then successfully tested to fish-out bioactive compounds from the crude extract of Scutellaria baicalensis, a Chinese plant containing the catechol-like flavonoid baicalin and its corresponding aglycone baicalein which are well-established inhibitors of Mpro. Molecular dynamics (MD) simulations were carried out in order to explore the binding mode of baicalin and baicalein, within the SARS-CoV-2 Mpro active site, allowing a better understanding of the role of the nucleophilic residues (i.e. His41, Cys145, His163 and His164) in the protein-ligand recognition process.

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative.

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target.

Exscalate4CoV: Innovative High Performing Computing (HPC) Strategies to Tackle Pandemic Crisis.

This Special Issue was intended as a dissemination forum where the major results pursued by the EXSCALATE4CoV project (E4C, https://www [...].

Discovery of a Potent and Highly Selective Dipeptidyl Peptidase IV and Carbonic Anhydrase Inhibitor as "Antidiabesity" Agents Based on Repurposing and Morphing of WB-4101.

The management of patients with type 2 diabetes mellitus (T2DM) is shifting from cardio-centric to weight-centric or, even better, adipose-centric treatments. Considering the downsides of multidrug therapies and the relevance of dipeptidyl peptidase IV (DPP IV) and carbonic anhydrases (CAs II and V) in T2DM and in the weight loss, we report a new class of multitarget ligands targeting the mentioned enzymes. We started from the known α1-AR inhibitor WB-4101, which was progressively modified through a tailored morphing strategy to optimize the potency of DPP IV and CAs while losing the adrenergic activity. The obtained compound 12 shows a satisfactory DPP IV inhibition with a good selectivity CA profile (DPP IV IC50: 0.0490 µM; CA II Ki 0.2615 µM; CA VA Ki 0.0941 µM; CA VB Ki 0.0428 µM). Furthermore, its DPP IV inhibitory activity in Caco-2 and its acceptable pre-ADME/Tox profile indicate it as a lead compound in this novel class of multitarget ligands.

Computational Insights into the Sequence-Activity Relationships of the NGF(1-14) Peptide by Molecular Dynamics Simulations.

The Nerve Growth Factor (NGF) belongs to the neurothrophins protein family involved in the survival of neurons in the nervous system. The interaction of NGF with its high-affinity receptor TrkA mediates different cellular pathways related to Alzheimer's disease, pain, ocular dysfunction, and cancer. Therefore, targeting NGF-TrkA interaction represents a valuable strategy for the development of new therapeutic agents. In recent years, experimental studies have revealed that peptides belonging to the N-terminal domain of NGF are able to partly mimic the biological activity of the whole protein paving the way towards the development of small peptides that can selectively target specific signaling pathways. Hence, understanding the molecular basis of the interaction between the N-terminal segment of NGF and TrkA is fundamental for the rational design of new peptides mimicking the NGF N-terminal domain. In this study, molecular dynamics simulation, binding free energy calculations and per-residue energy decomposition analysis were combined in order to explore the molecular recognition pattern between the experimentally active NGF(1-14) peptide and TrkA. The results highlighted the importance of His4, Arg9 and Glu11 as crucial residues for the stabilization of NGF(1-14)-TrkA interaction, thus suggesting useful insights for the structure-based design of new therapeutic peptides able to modulate NGF-TrkA interaction.

Highly Potent and Selective Dopamine D4 Receptor Antagonists Potentially Useful for the Treatment of Glioblastoma.

To better understand the role of dopamine D4 receptor (D4R) in glioblastoma (GBM), in the present paper, new ligands endowed with high affinity and selectivity for D4R were discovered starting from the brain penetrant and D4R selective lead compound 1-(3-(4-phenylpiperazin-1-yl)propyl)-3,4-dihydroquinolin-2(1H)-one (6). In particular, the D4R antagonist 24, showing the highest affinity and selectivity over D2R and D3R within the series (D2/D4 = 8318, D3/D4 = 3715), and the biased ligand 29, partially activating D4R Gi-/Go-protein and blocking ß-arrestin recruitment, emerged as the most interesting compounds. These compounds, evaluated for their GBM antitumor activity, induced a decreased viability of GBM cell lines and primary GBM stem cells (GSC#83), with the maximal efficacy being reached at a concentration of 10 µM. Interestingly, the treatment with both compounds 24 and 29 induced an increased effect in reducing the cell viability with respect to temozolomide, which is the first-choice chemotherapeutic drug in GBM.

Extensive Sampling of Molecular Dynamics Simulations to Identify Reliable Protein Structures for Optimized Virtual Screening Studies: The Case of the hTRPM8 Channel.

(1) Background: Virtual screening campaigns require target structures in which the pockets are properly arranged for binding. Without these, MD simulations can be used to relax the available target structures, optimizing the fine architecture of their binding sites. Among the generated frames, the best structures can be selected based on available experimental data. Without experimental templates, the MD trajectories can be filtered by energy-based criteria or sampled by systematic analyses. (2) Methods: A blind and methodical analysis was performed on the already reported MD run of the hTRPM8 tetrameric structures; a total of 50 frames underwent docking simulations by using a set of 1000 ligands including 20 known hTRPM8 modulators. Docking runs were performed by LiGen program and involved the frames as they are and after optimization by SCRWL4.0. For each frame, all four monomers were considered. Predictive models were developed by the EFO algorithm based on the sole primary LiGen scores. (3) Results: On average, the MD simulation progressively enhances the performance of the extracted frames, and the optimized structures perform better than the non-optimized frames (EF1% mean: 21.38 vs. 23.29). There is an overall correlation between performances and volumes of the explored pockets and the combination of the best performing frames allows to develop highly performing consensus models (EF1% = 49.83). (4) Conclusions: The systematic sampling of the entire MD run provides performances roughly comparable with those previously reached by using rationally selected frames. The proposed strategy appears to be helpful when the lack of experimental data does not allow an easy selection of the optimal structures for docking simulations. Overall, the reported docking results confirm the relevance of simulating all the monomers of an oligomer structure and emphasize the efficacy of the SCRWL4.0 method to optimize the protein structures for docking calculations.