An arithmetic valuative criterion for proper maps of tame algebraic stacks

The valuative criterion for proper maps of schemes has many applications in arithmetic, e.g. specializing $\mathbb{Q}_{p}$-points to $\mathbb{F}_{p}$-points. For algebraic stacks, the usual valuative criterion for proper maps is ill-suited for these kind of arguments, since it only gives a specialization point defined over an extension of the residue field, e.g. a $\mathbb{Q}_{p}$-point will specialize to an $\mathbb{F}_{p^{n}}$-point for some $n$. We give a new valuative criterion for proper maps of tame stacks which solves this problem and is well-suited for arithmetic applications. As a consequence, we prove that the Lang-Nishimura theorem holds for tame stacks

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

l-Carnosine (beta-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

A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0

(1) Background: Virtual screening studies on the therapeutically relevant proteins of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) require a detailed characterization of their druggable binding sites, and, more generally, a convenient pocket mapping represents a key step for structure-based in silico studies; (2) Methods: Along with a careful literature search on SARS-CoV-2 protein targets, the study presents a novel strategy for pocket mapping based on the combination of pocket (as performed by the well-known FPocket tool) and docking searches (as performed by PLANTS or AutoDock/Vina engines); such an approach is implemented by the Pockets 2.0 plug-in for the VEGA ZZ suite of programs; (3) Results: The literature analysis allowed the identification of 16 promising binding cavities within the SARS-CoV-2 proteins and the here proposed approach was able to recognize them showing performances clearly better than those reached by the sole pocket detection; and (4) Conclusions: Even though the presented strategy should require more extended validations, this proved successful in precisely characterizing a set of SARS-CoV-2 druggable binding pockets including both orthosteric and allosteric sites, which are clearly amenable for virtual screening campaigns and drug repurposing studies. All results generated by the study and the Pockets 2.0 plug-in are available for download

PRENYLATED CURCUMIN ANALOGUES AS MULTIPOTENT TOOLS TO TACKLE ALZHEIMER'S DISEASE

Alzheimer's disease is likely to be caused by copathogenic factors including aggregation of A\u3b2 peptides into oligomers and fibrils, neuroinflammation and oxidative stress. To date, no effective treatments are available and because of the multifactorial nature of the disease, it emerges the need to act on different and simultaneous fronts. Despite the multiple biological activities ascribed to curcumin as neuroprotector, its poor bioavailability and toxicity limit the success in clinical outcomes. To tackle Alzheimer's disease on these aspects, the curcumin template was suitably modified and a small set of analogues was attained. In particular, derivative 1 turned out to be less toxic than curcumin. As evidenced by capillary electrophoresis and transmission electron microscopy studies, 1 proved to inhibit the formation of large toxic A\u3b2 oligomers, by shifting the equilibrium towards smaller non-toxic assemblies and to limit the formation of insoluble fibrils. These findings were supported by molecular docking and steered molecular dynamics simulations which confirmed the superior capacity of 1 to bind A\u3b2 structures of different complexity. Remarkably, 1 also showed in vitro anti-inflammatory and anti-oxidant properties. In summary, the curcumin-based analogue 1 emerged as multipotent compound worth to be further investigated and exploited in the Alzheimer's disease multi-target context

Itraconazole inhibits nuclear delivery of extracellular vesicle cargo by disrupting the entry of late endosomes into the nucleoplasmic reticulum

Extracellular vesicles (EVs) are mediators of intercellular communication under bothhealthy and pathological conditions, including the induction of pro-metastatic traits,but it is not yet known how and where functional cargoes of EVs are delivered to theirtargets in host cell compartments. We have described that after endocytosis, EVsreach Rab+late endosomes and a fraction of these enter the nucleoplasmic reticu-lum and transport EV biomaterials to the host cell nucleoplasm. Their entry thereinand docking to outer nuclear membrane occur through a tripartite complex formedby the proteins VAP-A, ORP and Rab (VOR complex). Here, we report that theantifungal compound itraconazole (ICZ), but not its main metabolite hydroxy-ICZor ketoconazole, disrupts the binding of Rab to ORP–VAP-A complexes, leadingto inhibition of EV-mediated pro-metastatic morphological changes including cellmigration behaviour of colon cancer cells. With novel, smaller chemical drugs, inhi-bition of the VOR complex was maintained, although the ICZ moieties responsiblefor antifungal activity and interference with intracellular cholesterol distributionwere removed. Knowing that cancer cells hijack their microenvironment and thatEVs derived from them determine the pre-metastatic niche, small-sized inhibitors ofnuclear transfer of EV cargo into host cells could nd cancer therapeutic applications,particularly in combination with direct targeting of cancer cell

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

IntroductionCollaborative 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 coveredIn 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 opinionStructure-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

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

Data from molecular dynamics simulations in support of the role of human CES1 in the hydrolysis of Amplex Red

This data article contains the results of molecular dynamics (MD) simulations performed to assess the stability of the previously computed complex between the hCES1 structure and the Amplex Red (AR) substrate (Miwa et al., 2015) [1] and to compare the dynamic behavior of this complex with that of the corresponding hCES1-deacetylAR product. The study involves both standard molecular dynamics (MD) and steered (SMD) simulations to offer a quantitative comparison of the stability for the two complexes. With regard the standard MD runs, the data article graphically reports the r.m.s.d. profile of the ligand׳s atoms as well as the dynamic behavior of key contacts involving the catalytic Ser221 residue. The SMD simulations provide a comparison of the pull forces required to undock the two ligands and reveal that Van der Waals and hydrophobic interactions play a key role in complex stabilization

Prediction and mechanism elucidation of analyte retention on phospholipid stationary phases (IAM-HPLC) by in silico calculated physico-chemical descriptors

The present study proposes a method for an in silico calculation of phospholipophilicity. Phospholipophilicity is intended as the measure of analyte affinity for phospholipids; it is currently assessed by HPLC measures of analyte retention on phosphatidylcholine-like stationary phases (IAM – Immobilized Artificial Membrane) resulting in log kW^IAM values. Due to the amphipathic and electrically charged nature of phospholipids, retention on these stationary phases results from complex mechanisms, being affected not only by lipophilicity (as measured by n-octanol/aqueous phase partition coefficients, log P) but also by the occurrence of polar and/or electrostatic intermolecular interaction forces. Differently from log P, to date no method has been proposed for in silico calculation of log kW^IAM.The study is aimed both at shedding new light into the retention mechanism on IAM stationary phases and at offering a high-throughput method to achieve such values. A wide set of physico-chemical and topological properties were taken into account, yielding a robust final model including four in silico calculated parameters (lipophilicity, hydrophilic/lipophilic balance, molecular size, and molecule flexibility). The here presented model was based on the analysis of 205 experimentally determined values, taken from the literature and measured by a single research group to minimize the interlaboratory variability; such model is able to predict phospholipophilicity values on both the two IAM stationary phases to date marketed, i.e. IAM.PC.MG and IAM.PC.DD2, with a fairly good degree (r² = 0.85) of accuracy.The present work allowed the development of a free on-line service aimed at calculating log kW^IAM values of any molecule included in the PubChem database, which is freely available at http://nova.disfarm.unimi.it/logkwiam.htm