Watermelon: setup and validation of an in silico fragment-based approach.

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

Tau seeding activity in skin biopsy differentiates tauopathies from synucleinopathies.

Most neurodegenerative diseases lack definitive diagnostic tests, and the identification of easily accessible and reliable biomarkers remains a critical unmet need. Since tau protein is highly expressed in skin of tauopathies patients, we aimed to exploit the ultrasensitive seeding activity assay (SAA) to assess tau seeding activity in skin of patients with tauopathies. In this multicentric, case-control study, patients with tauopathies and synucleinopathies were consecutively recruited and sex-matched to healthy controls (HC). Subjects underwent a double 3 mm skin biopsy in cervical area and ankle. Skin tau-SAA, using TauK18 and TauK19 as reaction substrates for 4R and 3R isoforms, seeding score, clinical scales, biochemical and morphological characterization of SAA end-products were evaluated. We analyzed 58 subjects: 24 tauopathies (18 progressive supranuclear palsy, PSP, and 6 corticobasal degeneration, CBD), 20 synucleinopathies (14 Parkinson's disease, PD, and 6 multiple system atrophy, MSA), and 14 HC. PSP and CBD showed higher tau seeding activity at both anatomical sites. A greater sensitivity of 4R-SAA than 3R-SAA was observed. 4R tau-SAA identified tauopathies with 71% sensitivity and 93% specificity. Accuracy was higher for PSP than CBD: PSP vs HC / PD (AUC 0.825), while CBD vs HC / PD (AUC 0.797), and PSP vs MSA (AU 0.778). SAA end-products showed differences in biochemical and morphological characterization according to the anatomical site. Skin tau-SAA identifies tauopathies with good accuracy and can be used to implement the in-vivo clinical diagnosis of patients with neurodegenerative diseases. Further characterization of peripheral tau seed in skin may elucidate the structure of tau deposits in brain.

An update on antibacterial AlkylGuanidino Ureas: Design of new derivatives, synergism with colistin and data analysis of the whole library.

Antimicrobial resistance (AMR) represents one of the most challenging global Public Health issues, with an alarmingly increasing rate of attributable mortality. This scenario highlights the urgent need for innovative medicinal strategies showing activity on resistant isolates (especially, carbapenem-resistant Gram-negative bacteria, methicillin-resistant S. aureus, and vancomycin-resistant enterococci) yielding new approaches for the treatment of bacterial infections. We previously reported AlkylGuanidino Ureas (AGUs) with broad-spectrum antibacterial activity and a putative membrane-based mechanism of action. Herein, new tetra- and mono-guanidino derivatives were designed and synthesized to expand the structure-activity relationships (SARs) and, thereby, tested on the same panel of Gram-positive and Gram-negative bacteria. The membrane-active mechanism of selected compounds was then investigated through molecular dynamics (MD) on simulated bacterial membranes. In the end, the newly synthesized series, along with the whole library of compounds (more than 70) developed in the last decade, was tested in combination with subinhibitory concentrations of the last resort antibiotic colistin to assess putative synergistic or additive effects. Moreover, all the AGUs were subjected to cheminformatic and machine learning analyses to gain a deeper knowledge of the key features required for bioactivity.

VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules.

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Identification of New GSK3ß Inhibitors through a Consensus Machine Learning-Based Virtual Screening.

Glycogen synthase kinase-3 beta (GSK3ß) is a serine/threonine kinase that plays key roles in glycogen metabolism, Wnt/ß-catenin signaling cascade, synaptic modulation, and multiple autophagy-related signaling pathways. GSK3ß is an attractive target for drug discovery since its aberrant activity is involved in the development of neurodegenerative diseases such as Alzheimer's and Parkinson's disease. In the present study, multiple machine learning models aimed at identifying novel GSK3ß inhibitors were developed and evaluated for their predictive reliability. The most powerful models were combined in a consensus approach, which was used to screen about 2 million commercial compounds. Our consensus machine learning-based virtual screening led to the identification of compounds G1 and G4, which showed inhibitory activity against GSK3ß in the low-micromolar and sub-micromolar range, respectively. These results demonstrated the reliability of our virtual screening approach. Moreover, docking and molecular dynamics simulation studies were employed for predicting reliable binding modes for G1 and G4, which represent two valuable starting points for future hit-to-lead and lead optimization studies.

Slow wave activity across sleep-night could predict levodopa-induced dyskinesia.

A disruption in the slow wave activity (SWA) mediated synaptic downscaling process features Parkinson's disease (PD) patients presenting levodopa-induced dyskinesia (LID). To corroborate the role of SWA in LID development, 15 PD patients with LID, who underwent a polysomnography before LID's appearance, were included. Slow wave sleep epochs were extracted, combined and segmented into early and late sleep. SWA power was calculated. A linear regression model established that the SWA overnight decrease could predict the time to the emergence of LID. Our finding supports the link between SWA-mediated synaptic downscaling and the development of LID. If confirmed, it could pave the way to the study of possible sleep targeted therapies able to protect PD patients from LID development.

MolBook UNIPI─Create, Manage, Analyze, and Share Your Chemical Data for Free.

Here, we present MolBook UNIPI, freely available and user-friendly software specifically designed for medicinal chemists as a powerful tool for the easy management of virtual libraries of chemical compounds. With MolBook UNIPI, it is possible to create, store, handle, and share molecular databases in a very simple and intuitive way. The software allows users to rapidly generate libraries of bioactive ligands, building blocks, or commercial compounds by either manually creating single molecules or automatically importing compounds from public databases and pre-existing libraries. MolBook UNIPI databases can be enriched with all kinds of data and can be filtered based on molecular structures or properties, allowing the desired molecules, along with their structures and features, to be easily accessible in just a few clicks. Moreover, new molecular properties and potential toxicological effects of compounds can be rapidly and reliably predicted. Notably, all of these functions can be easily mastered even by inexperienced users, with no prior cheminformatics knowledge or programming skills, which makes MolBook UNIPI an invaluable tool for medicinal chemists. MolBook UNIPI can be downloaded free of charge from the project web page https://molbook.farm.unipi.it/.

Transcranial Magnetic Stimulation Improves Executive Functioning through Modulation of Social Cognitive Networks in Patients with Mild Cognitive Impairment: Preliminary Results.

(1) Background: Patients with mild cognitive impairment (MCI) often present impairment in executive functions (EFs). This study aimed to investigate the effect of high-frequency repetitive transcranial magnetic stimulation (rTMS) on EFs in patients with MCI. (2) Methods: A prospective trial was conducted on 11 patients with MCI. Participants underwent 25 min of 20 Hz rTMS for ten days on the right temporo-parietal junction (RTPJ) and medial prefrontal cortex (MPFC). Before (T0) and after rTMS treatment (T1), global cognitive profile and EFs were investigated using the Montreal cognitive assessment (MoCA), trial making test (TMT) A and B, and frontal assessment battery (FAB). Depression symptoms were assessed using the geriatric depression scale (GDS). Statistical analysis included Wilcoxon signed-rank test. (3) Results: After treatment, patients showed a significant improvement in the MoCA EFs subtask (T0 vs. T1, p = 0.015) and TMT-B (T0 vs. T1, p = 0.028). Five MCI patients with EF impairment showed full recovery of these deficits. No significant changes in the GDS were observed. (4) Conclusions: rTMS stimulation over the TPJ and MPFC induced significant short-term improvements in EFs in MCI patients. These findings suggest that the TPJ and MPFC may be involved in the attention-executive skills to redirect attention toward behaviorally relevant stimuli.

Resveratrol Analogues as Dual Inhibitors of Monoamine Oxidase B and Carbonic Anhydrase VII: A New Multi-Target Combination for Neurodegenerative Diseases?

Neurodegenerative diseases (NDs) are described as multifactorial and progressive syndromes with compromised cognitive and behavioral functions. The multi-target-directed ligand (MTDL) strategy is a promising paradigm in drug discovery, potentially leading to new opportunities to manage such complex diseases. Here, we studied the dual ability of a set of resveratrol (RSV) analogs to inhibit two important targets involved in neurodegeneration. The stilbenols 1−9 were tested as inhibitors of the human monoamine oxidases (MAOs) and carbonic anhydrases (CAs). The studied compounds displayed moderate to excellent in vitro enzyme inhibitory activity against both enzymes at micromolar/nanomolar concentrations. Among them, the best compound 4 displayed potent and selective inhibition against the MAO-B isoform (IC50 MAO-A 0.43 µM vs. IC50 MAO-B 0.01 µM) with respect to the parent compound resveratrol (IC50 MAO-A 13.5 µM vs. IC50 MAO-B > 100 µM). It also demonstrated a selective inhibition activity against hCA VII (KI 0.7 µM vs. KI 4.3 µM for RSV). To evaluate the plausible binding mode of 1−9 within the two enzymes, molecular docking and dynamics studies were performed, revealing specific and significant interactions in the active sites of both targets. The new compounds are of pharmacological interest in view of their considerably reduced toxicity previously observed, their physicochemical and pharmacokinetic profiles, and their dual inhibitory ability. Compound 4 is noteworthy as a promising lead in the development of MAO and CA inhibitors with therapeutic potential in neuroprotection.

Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors.

Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors.

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study.

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.

Tau protein quantification in skin biopsies differentiates tauopathies from alpha-synucleinopathies.

Abnormal accumulation of microtubule-associated protein tau (τ) is a characteristic feature of atypical parkinsonisms with tauopathies, such as progressive supranuclear palsy and corticobasal degeneration. However, pathological τ has also been observed in α-synucleinopathies like Parkinson's disease and multiple system atrophy. Based on the involvement of the peripheral nervous system in several neurodegenerative diseases, we characterized and compared τ expression in skin biopsies of patients clinically diagnosed with Parkinson's disease, multiple system atrophy, progressive supranuclear palsy and corticobasal degeneration and in healthy control subjects. In all groups, τ protein was detected along both somatosensory and autonomic nerve fibres in the epidermis and dermis by immunofluorescence. We found by western blot the presence of mainly two different bands at 55 and 70 kDa, co-migrating with 0N4R/1N3R and 2N4R isoforms, respectively. At the RNA level, the main transcript variants were 2N and 4R, and both were more expressed in progressive supranuclear palsy/corticobasal degeneration by real-time PCR. Enzyme-linked immunosorbent assay demonstrated significantly higher levels of total τ protein in skin lysates of progressive supranuclear palsy/corticobasal degeneration compared to the other groups. Multivariate regression analysis and receiver operating characteristics curve analysis of τ amount at both sites showed a clinical association with tauopathies diagnosis and high diagnostic value for progressive supranuclear palsy/corticobasal degeneration versus Parkinson's disease (sensitivity 90%, specificity 69%) and progressive supranuclear palsy/corticobasal degeneration versus multiple system atrophy (sensitivity 90%, specificity 86%). τ protein increase correlated with cognitive impairment in progressive supranuclear palsy/corticobasal degeneration. This study is a comprehensive characterization of τ in the human cutaneous peripheral nervous system in physiological and pathological conditions. The differential expression of τ, both at transcript and protein levels, suggests that skin biopsy, an easily accessible and minimally invasive exam, can help in discriminating among different neurodegenerative diseases.

Impairment of sleep homeostasis in cervical dystonia patients.

Alterations in brain plasticity seem to play a role in the pathophysiology of cervical dystonia (CD). Since evidences indicate that sleep regulates brain plasticity, we hypothesized that an alteration in sleep homeostatic mechanisms may be involved in the pathogenesis of CD. We explored sleep in control subjects (CTL) and CD patients before (Tpre-BoNT) and after (Tpost-BoNT) botulinum toxin (BoNT) treatment. A physiological slow wave activity (SWA) power decrease throughout the night was observed in CTL but not in CD at Tpre-BoNT. BoNT restored the physiological SWA decrease in CD at Tpost-BoNT. Furthermore, in the first part of the night, CD at Tpost-BNT showed a frontal increase and parietal decrease in SWA power compared to CD at Tpre-BoNT, with a SWA distribution comparable to that observed in CTL. Our data highlighted a pathophysiological relationship between SWA during sleep and CD and provided novel insight into the transient central plastic effect of BoNT.

VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions.

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity.

Alpha-synuclein oligomers and small nerve fiber pathology in skin are potential biomarkers of Parkinson's disease.

The proximity ligation assay (PLA) is a specific and sensitive technique for the detection of αSyn oligomers (αSyn-PLA), early and toxic species implicated in the pathogenesis of PD. We aimed to evaluate by skin biopsy the diagnostic and prognostic capacity of αSyn-PLA and small nerve fiber reduction in PD in a longitudinal study. αSyn-PLA was performed in the ankle and cervical skin biopsies of PD (n = 30), atypical parkinsonisms (AP, n = 23) including multiple system atrophy (MSA, n = 12) and tauopathies (AP-Tau, n = 11), and healthy controls (HC, n = 22). Skin biopsy was also analyzed for phosphorylated αSyn (P-αSyn) and 5G4 (αSyn-5G4), a conformation-specific antibody to aggregated αSyn. Intraepidermal nerve fiber density (IENFD) was assessed as a measure of small fiber neuropathy. αSyn-PLA signal was more expressed in PD and MSA compared to controls and AP-Tau. αSyn-PLA showed the highest diagnostic accuracy (PD vs. HC sensitivity 80%, specificity 77%; PD vs. AP-Tau sensitivity 80%, specificity 82%), however, P-αSyn and 5G4, possible markers of later phases, performed better when considering the ankle site alone. A small fiber neuropathy was detected in PD and MSA. A progression of denervation not of pathological αSyn was detected at follow-up and a lower IENFD at baseline was associated with a greater cognitive and motor decline in PD. A skin biopsy-derived compound marker, resulting from a linear discrimination analysis model of αSyn-PLA, P-αSyn, αSyn-5G4, and IENFD, stratified patients with accuracy (77.8%), including the discrimination between PD and MSA (84.6%). In conclusion, the choice of pathological αSyn marker and anatomical site influences the diagnostic performance of skin biopsy and can help in understanding the temporal dynamics of αSyn spreading in the peripheral nervous system during the disease. Skin denervation, not pathological αSyn is a potential progression marker for PD.

Recent Advances in In Silico Target Fishing.

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Predicting Isoform-Selective Carbonic Anhydrase Inhibitors via Machine Learning and Rationalizing Structural Features Important for Selectivity.

Carbonic anhydrases (CAs) catalyze the physiological hydration of carbon dioxide and are among the most intensely studied pharmaceutical target enzymes. A hallmark of CA inhibition is the complexation of the catalytic zinc cation in the active site. Human (h) CA isoforms belonging to different families are implicated in a wide range of diseases and of very high interest for therapeutic intervention. Given the conserved catalytic mechanisms and high similarity of many hCA isoforms, a major challenge for CA-based therapy is achieving inhibitor selectivity for hCA isoforms that are associated with specific pathologies over other widely distributed isoforms such as hCA I or hCA II that are of critical relevance for the integrity of many physiological processes. To address this challenge, we have attempted to predict compounds that are selective for isoform hCA IX, which is a tumor-associated protein and implicated in metastasis, over hCA II on the basis of a carefully curated data set of selective and nonselective inhibitors. Machine learning achieved surprisingly high accuracy in predicting hCA IX-selective inhibitors. The results were further investigated, and compound features determining successful predictions were identified. These features were then studied on the basis of X-ray structures of hCA isoform-inhibitor complexes and found to include substructures that explain compound selectivity. Our findings lend credence to selectivity predictions and indicate that the machine learning models derived herein have considerable potential to aid in the identification of new hCA IX-selective compounds.

Discovery of a new ATP-citrate lyase (ACLY) inhibitor identified by a pharmacophore-based virtual screening study.

ATP citrate lyase (ACLY) is an important enzyme that catalyzes the conversion of citrate to acetyl-CoA in normal cells, facilitating the de novo fatty acid synthesis. Lipids and fatty acids were found to be accumulated in different types of tumors, such as brain, breast, rectal and ovarian cancer, representing a great source of energy for cancer cell growth and metabolism. Since ACLY-mediated conversion of citrate to acetyl-CoA constitutes the basis for fatty acid synthesis, ACLY seems to be quite an unexplored and promising therapeutic target for anticancer drug design. A pharmacophore-based virtual screening (VS) protocol with the aid of hierarchical docking, consensus docking (CD), molecular dynamics (MD) simulations and ligand-protein binding free energy calculations led to the identification of compound VS1, which showed a moderate but promising inhibitory activity, demonstrating to be 2.5 times more potent than reference inhibitor 2-hydroxycitrate. These results validate the reliability of our VS workflow and pave the way for the design of novel and more potent ACLY inhibitors.Communicated by Ramaswamy H. Sarma.