Three-dimensional interactions analysis of the anticancer target c-src kinase with its inhibitors

Src family kinases (SFKs) constitute the biggest family of non-receptor tyrosine kinases considered as therapeutic targets for cancer therapy. An aberrant expression and/or activation of the proto-oncogene c-Src kinase, which is the oldest and most studied member of the family, has long been demonstrated to play a major role in the development, growth, progression and metastasis of numerous human cancers, including colon, breast, gastric, pancreatic, lung and brain carcinomas. For these reasons, the pharmacological inhibition of c-Src activity represents an effective anticancer strategy and a few compounds targeting c-Src, together with other kinases, have been approved as drugs for cancer therapy, while others are currently undergoing preclinical studies. Nevertheless, the development of potent and selective inhibitors of c-Src aimed at properly exploiting this biological target for the treatment of cancer still represents a growing field of study. In this review, the co-crystal structures of c-Src kinase in complex with inhibitors discovered in the past two decades have been described, highlighting the key ligand–protein interactions necessary to obtain high potency and the features to be exploited for addressing selectivity and drug resistance issues, thus providing useful information for the design of new and potent c-Src kinase inhibitors

Development of a cheminformatics platform for selectivity analyses of carbonic anhydrase inhibitors

The selectivity for a specific human Carbonic Anhydrase (hCA) isoform is an important property a hCA inhibitor (CAI) should be endowed with, in order to constitute a valuable therapeutic tool for the treatment of a desired pathology. In this context, we developed a chemoinformatic platform that allows the analysis of the structure and selectivity profile of known CAIs reported in literature, with the aim of identifying structural motifs connected to ligand selectivity, thus providing useful guidelines for the design of novel ligands selective for the desired hCA isoform. The platform is able to perform ultrafast structure and selectivity analyses through ligand fingerprint similarity, with no need of structural information about the target receptor and ligands’ binding mode. It is easily accessible to the non-expert user through the implementation of a KNIME Analytic Platform workflow and could be extended to analyze the selectivity profile of known ligands of different target proteins

The history of nanoscience and nanotechnology: From chemical-physical applications to nanomedicine

Nanoscience breakthroughs in almost every field of science and nanotechnologies make life easier in this era. Nanoscience and nanotechnology represent an expanding research area, which involves structures, devices, and systems with novel properties and functions due to the arrangement of their atoms on the 1-100 nm scale. The field was subject to a growing public awareness and controversy in the early 2000s, and in turn, the beginnings of commercial applications of nanotechnology. Nanotechnologies contribute to almost every field of science, including physics, materials science, chemistry, biology, computer science, and engineering. Notably, in recent years nanotechnologies have been applied to human health with promising results, especially in the field of cancer treatment. To understand the nature of nanotechnology, it is helpful to review the timeline of discoveries that brought us to the current understanding of this science. This review illustrates the progress and main principles of nanoscience and nanotechnology and represents the pre-modern as well as modern timeline era of discoveries and milestones in these fields

Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors

Carbonic anhydrase II (CAII) is a zinc-containing metalloenzyme whose aberrant activity is associated with various diseases such as glaucoma, osteoporosis, and different types of tumors; therefore, the development of CAII inhibitors, which can represent promising therapeutic agents for the treatment of these pathologies, is a current topic in medicinal chemistry. Molecular docking is a commonly used tool in structure-based drug design of enzyme inhibitors. However, there is still a need for improving docking reliability, especially in terms of scoring functions, since the complex pattern of energetic contributions driving ligand⁻protein binding cannot be properly described by mathematical functions only including approximated energetic terms. Here we report a novel CAII-specific fingerprint-based (IFP) scoring function developed according to the ligand⁻protein interactions detected in the CAII-inhibitor co-crystal structures of the most potent CAII ligands. Our IFP scoring function outperformed the ability of Autodock4 scoring function to identify native-like docking poses of CAII inhibitors and thus allowed a considerable improvement of docking reliability. Moreover, the ligand⁻protein interaction fingerprints showed a useful application in the binding mode analysis of structurally diverse CAII ligands

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

Synthesis and AT1 affinity evaluation of benzamidophenyl analogs of known AT1 receptor ligands with similar aromatic skeleton

n/

Effect of Cooking Techniques on the in vitro Protein Digestibility, Fatty Acid Profile, and Oxidative Status of Mealworms (Tenebrio molitor)

Tenebrio molitor (T. molitor) (mealworm) larvae are one of the most promising insects for feed–food purposes. Mealworms are rich in several macro and micro nutritional elements and can be practically reared on side stream substrates. In this study, the effects of seven different cooking techniques were tested on the nutritional value of mealworms focusing the attention on protein digestibility, fatty acid (FA) profile, and oxidative status. Uncooked larvae (UC) were used as control and compared to two combinations of temperature/time in oven cooking (70°C for 30 min, OC70-30, 150°C for 10 min, OC150-10), two methods of frying (mealworms fried in sunflower oil as deep fry, DF, or pan fry, PF), microwaving (MW), boiling (in plastic bag under vacuum, BO), and steaming (ST). Proximate composition, in vitro digestibility (gastric and duodenal), FA profile, and oxidative status (tocopherol and tocotrienol, carbonyl, and lipid oxidation) were then tested. Cooking technique affected all the tested parameters. As expected, cooking affected proximate composition in relation to the method applied (dry matter increased after oven cooking and frying; lipids increased by frying). In vitro digestion revealed the highest value for the OC70-30 method, followed by UC and ST. Deep frying revealed the worst digestibility percentage. FA profile was deeply affected by the cooking technique, with general decrease in SFA and MUFA. The highest modifications in FA profile were revealed in ST larvae with an increased percentage of linoleic acid linked to the lowering of SFA and MUFA contents. Furthermore, deep frying larvae in sunflower oil increased the relative abundance of PUFAs. Tocols values were higher in DF and MW groups than PF (about 6-fold more) and all other groups (7-fold more). Carbonyls increased with oven cooking (OC150-10 and OC70-30), whereas the values were lower with frying and similar to ST and UC. Lipid oxidation was highest as well in OC150-10 but similar to frying methods (DF and PF). Based on the obtained results, it can be concluded that mealworm larvae surely meet human nutritional requirements, but the cooking method must be carefully chosen to maintain a high nutritional value

Salicylaldoxime derivatives as new leads for the development of carbonic anhydrase inhibitors

n/

Former foodstuff products in Tenebrio molitor rearing: Effects on growth, chemical composition, microbiological load, and antioxidant status

Tenebrio molitor (mealworm) larvae represent one of the most interesting edible insects and could be reared on alternative feeds, such as former foodstuff products (FFPs). In the present work, five different FFPs (brewery spent grains, bread and cookie leftovers, and mixes of brewer's spent grain or bread with cookies) were employed as feeding substrates. Larvae's growth performances, chemical composition, microbial loads, and antioxidant status were determined. Chemical compositions of the substrates affected all the tested parameters. Brewery spent grains-fed larvae showed a faster growth period and higher crude protein and carbohydrate contents. The use of cookies as a single substrate or their addition to spent grains or bread increased the lipids contents, while growth was delayed. Microbial loads were partially affected by the fed diet. The antioxidant status of larvae showed different concentrations of tocopherols isoforms (δ, γ, α) in relation to the diet; however, no differences were detected in relation to the global antioxidant capacity (2,2-azinobis-(3 ethylbenzothiazoline-6-sulfonic acid), ABTS reducing activity; 1,1-diphenyl-2-pircydrazyl, DPPH radical scavenging activity; ferric reducing ability, FRAP). Results point out a high plasticity of mealworm larvae and the potential to tailor the final outcomes in relation to the substrate employed. Mealworms could be practically reared on FFPs to produce food-feed with high nutrient values