MIND-BEST: Web Server for Drugs and Target Discovery; Design, Synthesis, and Assay of MAO-B Inhibitors and Theoretical−Experimental Study of G3PDH Protein fromTrichomonas gallinae

Abstract

Many drugs with very different affinity to a large number of receptors are described. Thus, in this work, we selected drug-target pairs (DTPs/nDTPs) of drugs with high affinity/nonaffinity for different targets. Quantitative structure-activity relationship (QSAR) models become a very useful tool in this context because they substantially reduce time and resource-consuming experiments. Unfortunately, most QSAR models predict activity against only one protein target and/or they have not been implemented on a public Web server yet, freely available online to the scientific community. To solve this problem, we developed a multitarget QSAR (mt-QSAR) classifier combining the MARCH-INSIDE software for the calculation of the structural parameters of drug and target with the linear discriminant analysis (LDA) method in order to seek the best model. The accuracy of the best LDA model was 94.4% (3,859/4,086 cases) for training and 94.9% (1,909/2,012 cases) for the external validation series. In addition, we implemented the model into the Web portal Bio-AIMS as an online server entitled MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (MIND-BEST), located at http://miaja.tic.udc.es/Bio-AIMS/MIND-BEST.php . This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally, we illustrated two practical uses of this server with two different experiments. In experiment 1, we report for the first time a MIND-BEST prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of eight rasagiline derivatives, promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF and -TOF/TOF MS, MASCOT search, 3D structure modeling with LOMETS, and MIND-BEST prediction for different peptides as new protein of the found in the proteome of the bird parasite Trichomonas gallinae, which is promising for antiparasite drug targets discovery.

Model for High-Throughput Screening of Multitarget Drugs in Chemical Neurosciences: Synthesis, Assay, and Theoretic Study of Rasagiline Carbamates

“…As shown in this scheme, they were synthesized from the hidroxy mono-or dipropargylaminoindans (1−4), previously described by us, and prepared following ) is the probability with which the model predicts a value of the measure higher than the cutoff for compound 11 procedures described in the literature. 28 Please see details about the techniques used for the characterization of compounds and the results obtained (NMR and IR spectra) in the Supporting Information. 3.2.2.…”

“…12,13 We can use different software to calculate molecular descriptors and develop mt-QSAR or mx-QSAR models: DRAGON, 14,15 MOE, 16 TOPS-MODE, 17−19 CODESSA, 20−22 TOMOCOMD, 23,24 or MARCH-INSIDE (MI), 25−27 software used in this work. For instance, we used MI to develop different mt-QSAR classifiers/web-servers such as MIND-BEST 28 or NL MIND-BEST. 29 The first two use MI only, but the third combines DRAGON and MI to calculate descriptors for drugs and proteins, respectively.…”

“…The 3-OH, 3-OAc, and 3-OBz rasagiline derivatives synthesized and experimentally assayed by our group were not very active against some of the expected targets. 28 This situation encouraged us to study the possible neuroprotective activity of our compounds directly in the neuronal culture. In fact, 3-OH, 3-OAc, and 3-OBz rasagiline derivatives were found to present interesting neuroprotective effects in experimental assays.…”

Natural compounds for the drug targets of Sexually Transmitted Diseases (STD) using virtual screening

“…The functional groups modify the electronic properties of the targets; as a result they have the potential to block the metabolic pathway or restraint the conformation. Different methods have been used to predict the drug targets interaction based on QSAR [18], reverse docking [19], [20], [21]. The interactions can also be interpreted by, side effect similarity, drug based similarity, and target based similarity, based on interpretation of the networks [22], [23].…”

Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

“…This mt-QSAR derived with MARCH-INSIDE is able to assemble the graphs drug-target networks that appears at a higher level based on the parameters of graphs at a lower structural level. The results obtained with this method to predict drug-target networks have been implemented in two public web-servers called the MIND-BEST and NL MIND-BEST [17,18], which can be available at the platform Bio-AIMS (http://miaja.tic. udc.es/Bio-AIMS/index.php).…”