Rational Design of Highly Selective Sialyllactose-Imprinted Nanogels.

We describe a facile method to prepare water-compatible molecularly imprinted polymer nanogels (MIP NGs) as synthetic antibodies against target glycans. Three different phenylboronic acid (PBA) derivatives were explored as monomers for the synthesis of MIP NGs targeting either α2,6- or α2,3-sialyllactose, taken as oversimplified models of cancer-related sT and sTn antigens. Starting from commercially available 3-acrylamidophenylboronic acid, also its 2-substituted isomer and the 5-acrylamido-2-hydroxymethyl cyclic PBA monoester derivative were initially evaluated by NMR studies. Then, a small library of MIP NGs imprinted with the α2,6-linked template was synthesized and tested by mobility shift Affinity Capillary Electrophoresis (msACE) to rapidly assess an affinity ranking. Finally, the best monomer o-acrylamido PBA was selected for the synthesis of polymers targeting both sialyllactoses. The resulting MIP NGs display an affinity constant ≈ 106 M-1 and selectivity towards imprinted glycans. This general procedure could be applied to any non-modified carbohydrate template possessing a reducing end.

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.

Triazole derivatives inhibit the VOR complex-mediated nuclear transport of extracellular particles: Potential application in cancer and HIV-1 infection.

Extracellular vesicles (EVs) appear to play an important role in intercellular communication in various physiological processes and pathological conditions such as cancer. Like enveloped viruses, EVs can transport their contents into the nucleus of recipient cells, and a new intracellular pathway has been described to explain the nuclear shuttling of EV cargoes. It involves a tripartite protein complex consisting of vesicle-associated membrane protein-associated protein A (VAP-A), oxysterol-binding protein (OSBP)-related protein-3 (ORP3) and late endosome-associated Rab7 allowing late endosome entry into the nucleoplasmic reticulum. Rab7 binding to ORP3-VAP-A complex can be blocked by the FDA-approved antifungal drug itraconazole. Here, we design a new series of smaller triazole derivatives, which lack the dioxolane moiety responsible for the antifungal function, acting on the hydrophobic sterol-binding pocket of ORP3 and evaluate their structure-activity relationship through inhibition of VOR interactions and nuclear transfer of EV and HIV-1 cargoes. Our investigation reveals that the most effective compounds that prevent nuclear transfer of EV cargo and productive infection by VSV-G-pseudotyped HIV-1 are those with a side chain between 1 and 4 carbons, linear or branched (methyl) on the triazolone region. These potent chemical drugs could find clinical applications either for nuclear transfer of cancer-derived EVs that impact metastasis or viral infection.

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.

Screening of Mpro Protease (SARS-CoV-2) Covalent Inhibitors from an Anthocyanin-Rich Blueberry Extract Using an HRMS-Based Analytical Platform.

BACKGROUND: The viral main protease (Mpro) of SARS-CoV-2 has been recently proposed as a key target to inhibit virus replication in the host. Therefore, molecules that can bind the catalytic site of Mpro could be considered as potential drug candidates in the treatment of SARS-CoV-2 infections. Here we proposed the application of a state-of-the-art analytical platform which combines metabolomics and protein structure analysis to fish-out potential active compounds deriving from a natural matrix, i.e., a blueberry extract. METHODS: The experiments focus on finding MS covalent inhibitors of Mpro that contain in their structure a catechol/pyrogallol moiety capable of binding to the nucleophilic amino acids of the enzyme's catalytic site. RESULTS: Among the potential candidates identified, the delphinidin-3-glucoside showed the most promising results. Its antiviral activity has been confirmed in vitro on Vero E6 cells infected with SARS-CoV-2, showing a dose-dependent inhibitory effect almost comparable to the known Mpro inhibitor baicalin. The interaction of delphinidin-3-glucoside with the Mpro pocket observed was also evaluated by computational studies. CONCLUSIONS: The HRMS analytical platform described proved to be effective in identifying compounds that covalently bind Mpro and are active in the inhibition of SARS-CoV-2 replication, such as delphinidin-3-glucoside.

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.

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.

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.

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.

The VEGA suite of programs: an versatile platform for cheminformatics and drug design projects.

The purpose of the article is to offer an overview of the latest release of the VEGA suite of programs. This software has been constantly developed and freely released during the last 20 years and has now reached a significant diffusion and technology level as confirmed by the about 22 500 registered users. While being primarily developed for drug design studies, the VEGA package includes cheminformatics and modeling features, which can be fruitfully utilized in various contexts of the computational chemistry. To offer a glimpse of the remarkable potentials of the software, some examples of the implemented features in the cheminformatics field and for structure-based studies are discussed. Finally, the flexible architecture of the VEGA program which can be expanded and customized by plug-in technology or scripting languages will be described focusing attention on the HyperDrive library including highly optimized functions. AVAILABILITY AND IMPLEMENTATION: The VEGA suite of programs and the source code of the VEGA command-line version are available free of charge for non-profit organizations at http://www.vegazz.net.

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 [...].

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.

MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database.

(1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44.

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation.

(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease.

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.

Structure-guided approach to identify a novel class of anti-leishmaniasis diaryl sulfide compounds targeting the trypanothione metabolism.

Leishmania protozoans are the causative agent of leishmaniasis, a neglected tropical disease consisting of three major clinical forms: visceral leishmaniasis (VL), cutaneous leishmaniasis, and mucocutaneous leishmaniasis. VL is caused by Leishmania donovani in East Africa and the Indian subcontinent and by Leishmania infantum in Europe, North Africa, and Latin America, and causes an estimated 60,000 deaths per year. Trypanothione reductase (TR) is considered to be one of the best targets to find new drugs against leishmaniasis. This enzyme is fundamental for parasite survival in the human host since it reduces trypanothione, a molecule used by the tryparedoxin/tryparedoxin peroxidase system of Leishmania to neutralize the hydrogen peroxide produced by host macrophages during infection. Recently, we solved the X-ray structure of TR in complex with the diaryl sulfide compound RDS 777 (6-(sec-butoxy)-2-((3-chlorophenyl)thio)pyrimidin-4-amine), which impairs the parasite defense against the reactive oxygen species by inhibiting TR with high efficiency. The compound binds to the catalytic site and engages in hydrogen bonds the residues more involved in the catalysis, namely Glu466', Cys57 and Cys52, thereby inhibiting the trypanothione binding. On the basis of the RDS 777-TR complex, we synthesized structurally related diaryl sulfide analogs as TR inhibitors able to compete for trypanothione binding to the enzyme and to kill the promastigote in the micromolar range. One of the most active among these compounds (RDS 562) was able to reduce the trypanothione concentration in cell of about 33% via TR inhibition. RDS 562 inhibits selectively Leishmania TR, while it does not inhibit the human homolog glutathione reductase.

Unveiling the molecular mechanisms underpinning biorecognition of early-glycated human serum albumin and receptor for advanced glycation end products.

Serum levels of early-glycated albumin are significantly increased in patients with diabetes mellitus and may play a role in worsening inflammatory status and sustaining diabetes-related complications. To investigate possible pathological recognition involving early-glycated albumin and the receptor for advanced glycation end products (RAGE), an early-glycated human serum albumin (HSAgly), with a glycation pattern representative of the glycated HSA form abundant in diabetic patients, and the recombinant human RAGE ectodomain (VC1) were used. Biorecognition between the two interactants was investigated by combining surface plasmon resonance (SPR) analysis and affinity chromatography coupled with mass spectrometry (affinity-MS) for peptide extraction and identification. SPR analysis proved early-glycated albumin could interact with the RAGE ectodomain with a steady-state affinity constant of 6.05 ± 0.96 × 10-7 M. Such interaction was shown to be specific, as confirmed by a displacement assay with chondroitin sulfate, a known RAGE binder. Affinity-MS studies were performed to map the surface area involved in the recognition. These studies highlighted that a region surrounding Lys525 and part of subdomain IA were involved in VC1 recognition. Finally, an in silico analysis highlighted (i) a key role for glycation at Lys525 (the most commonly glycated residue in HSA in diabetic patients) through a triggering mechanism similar to that previously observed for AGEs or advanced lipoxidation end products and (ii) a stabilizing role for subdomain IA. Albeit a moderate affinity for complex formation, the high plasma levels of early-glycated albumin and high percentage of glycation at Lys525 in diabetic patients make this interaction of possible pathological relevance. Graphical abstract.

Novel adamantyl retinoid-related molecules with POLA1 inhibitory activity.

Atypical retinoids (AR) or retinoid-related molecules (RRMs) represent a promising class of antitumor compounds. Among AR, E-3-(3'-adamantan-1-yl-4'-hydroxybiphenyl-4-yl)acrylic acid (adarotene), has been extensively investigated. In the present work we report the results of our efforts to develop new adarotene-related atypical retinoids endowed also with POLA1 inhibitory activity. The effects of the synthesized compounds on cell growth were determined on a panel of human and hematological cancer cell lines. The most promising compounds showed antitumor activity against several tumor histotypes and increased cytotoxic activity against an adarotene-resistant cell line, compared to the parent molecule. The antitumor activity of a selected compound was evaluated on HT-29 human colon carcinoma and human mesothelioma (MM487) xenografts. Particularly significant was the in vivo activity of the compound as a single agent compared to adarotene and cisplatin, against pleural mesothelioma MM487. No reduction of mice body weight was observed, thus suggesting a higher tolerability with respect to the parent compound adarotene.

Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays.

Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM2. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM2 ligands. Overall, the study enhances the understanding of the factors governing the hM2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.

Activation Effects of Carnosine- and Histidine-Containing Dipeptides on Human Carbonic Anhydrases: A Comprehensive Study.

l-Carnosine (ß-Ala-l-His) and several other histidine-containing peptides, including two N-methylated forms on the imidazole ring (l-anserine and l-balenine), two derivatives modified on the carboxyl function (carcinine and l-carnosinamide), two analogues differing in the length of the N-terminal residue (l-homocarnosine and Gly-l-His) and the N-acetyl derivatives, were investigated as activators of four isoforms of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1). The four human isoforms hCA I, II, VA and IX were activated in the low to high micromolar range, with a rather complex structure activity relationship. A performed computational study allowed us to rationalize these results and to propose a binding mode of these activators within the enzyme active site. Similarly to other CA activators, the here studied peptides could find relevant pharmacological applications such as in the management of CA deficiencies, for therapy memory and enhancing cognition or for artificial tissues engineering.