IN SILICO AND IN VIVO PHARMACOLOGICAL STUDIES OF CLOZAPINE AND D-AMINO ACID OXIDASE INHIBITOR FOR COGNITIVE ENHANCEMENT

Objective: D-amino acid oxidase inhibitors (DAAOIs) are of particular focus for cognition study. Atypical antipsychotics are known DAAO inhibitors. The present examination was done to check out the binding affinity of atypical antipsychotics by docking toward the DAAO protein; in conclusion, the picked antipsychotic drug was checked for their cognition enhancing activity with scopolamine-induced amnesia.Methods: The crystal structure of DAAO was obtained from Protein Data Bank, the energy minimization was performed with CHARMM program, then active site prediction was made out using Ramachandran plot, and finally, docking examination was finished using Autodock 4.2 tool. For in vivo study, the mice were divided into three groups. Group I - vehicle (Saline) treated, Group II – saline +scopolamine (1 mg/kg, intraperitoneal [i.p]) treated, and Group III - clozapine (20 mg/kg, i.p) + scopolamine (1 mg/kg, i.p).Results: The Autodock examination shows significant binding affinity of - 5.22 for brexpiprazole and least or positive binding affinity of +1 for iloperidone. Clozapine with binding energy of - 2.87 was decided for completing the in vivo cognition study. The in vivo shows up that clozapine (20 mg/kg, i.p) exhibits a change in the impairment of spatial memory.Conclusion: The results recommend that the clozapine produces cognitive enhancement through both DAAOI and antipsychotic action. Clozapine has cognitive improvement potential, favoring its usage in reducing toxic impacts of scopolamin

Utilization from Computational Methods and Omics Data for Antiviral Drug Discovery to Control of SARS-CoV-2

SARS-CoV-2 pandemic issue threatening world health and economy became a major problem with its destructive impact. The researchers have seen that conventional methods related to medicine and immunological background do not resolve this disease by gained knowledge of viruses previously studied. Advances in computational biology comprising bioinformatics, simulation, and yielded databases have accelerated and strengthened our facilities to predict some cases related to the biological complex by comparison with the use of artificial intelligence. Various novel drugs by using in silico resources and in vivo imaging techniques associated with high-resolution technologies can cause the confidential development of methods for the detection of antiviral drugs and the production of diagnosis kits. In the future, we will start seeing these novel techniques’ positive reflection and their advantages in cost/time effective profits. This chapter highlights these approaches and addresses updated knowledge currently used for research and development

Coupling Dynamics and Evolutionary Information with Structure to Identify Protein Regulatory and Functional Binding Sites

Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, AR-Pred (Active and Regulatory site Prediction), which supplements protein geometry, evolutionary and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. Since the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median AUC of 91% and MCC of 0.68, whereas the less welldefined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at https://github.com/sambitmishra0628/ARPRED_ source

Current state-of-the-art of the research conducted in mapping protein cavities – binding sites of bioactive compounds, peptides or other proteins

Ο σκοπός της διπλωματικής εργασίας είναι η διερεύνηση και αποτύπωση των ερευνητικών μελετών που αφορούν στον χαρακτηρισμό μιας πρωτεϊνικής κοιλότητας – κέντρου πρόσδεσης βιοδραστικών ενώσεων, πεπτιδίων ή άλλων πρωτεϊνών. Στην παρούσα εργασία χρησιμοποιήθηκε η μέθοδος της βιβλιογραφικής επισκόπησης. Παρουσιάζονται τα κυριότερα ευρήματα προηγούμενων ερευνών που σχετίζονται με τη διαδικασία σχεδιασμού φαρμάκων και τον εντοπισμό φαρμακοφόρων με βάση ένα σύνολο προσδετών. Στη συνέχεια συγκρίνονται διαδικασίες επεξεργασίας και ανάλυσης της πρωτεϊνικής κοιλότητας προγενέστερων ερευνών με τη προσέγγιση που προτάθηκε από τους Παπαθανασίου και Φωτόπουλου το 2015. Αναδεικνύονται βασικά πλεονεκτήματα της προσέγγισης αυτής, όπως η εφαρμογή του αλγορίθμου πολυδιάστατη k-means ομαδοποίηση (multidimensional k-means clustering). Η εύρεση βιβλιογραφίας βασίστηκε σε αναζήτηση επιστημονικών άρθρων σε ξενόγλωσσα επιστημονικά περιοδικά, σε κεφάλαια βιβλίων και σε διάφορα άρθρα σε ηλεκτρονικούς ιστότοπους σχετικά με τον σχεδιασμό φαρμάκων και τις κοιλότητες που απαντώνται στις πρωτεΐνες. Στην παρούσα εργασία παρουσιάζονται εν συντομία εργαλεία που εντοπίστηκαν χρησιμοποιώντας λέξεις κλειδιά όπως για παράδειγμα δυναμική πρωτεϊνικής κοιλότητας, καταλυτικό κέντρο ενός ενζύμου, πρόσδεση, πρωτεϊνική θήκη κλπ. Στη συνέχεια συγκροτήθηκε κατάλογος με τα εργαλεία βιοπληροφορικής ανάλυσης που βρέθηκαν και ακολούθησε εκτενής αναφορά επιλεκτικά σε κάποια από αυτά. Κριτήριο επιλογής αυτών των εργαλείων αποτέλεσε η ημερομηνία δημοσίευσής τους, οι αλγόριθμοι και η μεθοδολογία που χρησιμοποιούν. Τα εργαλεία αυτά κατηγοριοποιήθηκαν με βάση τις λέξεις κλειδιά που χρησιμοποιήθηκαν για την εξόρυξη των δεδομένων από την βιβλιογραφία. Τέλος πραγματοποιήθηκε συγκριτική μελέτη αυτών αναδεικνύοντας τα πλεονεκτήματα και εστιάζοντας στην περαιτέρω αξιοποίησή τους.The aim of this thesis was to report on the current state-of-the-art of the research conducted concerning mapping of protein cavities with a potential function role as binding sites of bioactive compounds, peptides or other proteins. A literature review was performed with emphasis on the relevant tools developed during the last decade. In addition, the main research findings regarding drug design and druggable targets based on binding sites are presented. Processes performed in protein cavity detection and analysis, of previous research articles, are compared with the approach described by Anaxagoras Fotopoulos and Athanasios Papathanasiou (2015). The results showed that a competitive advantage of their approach is the multidimensional k-means algorithm for clustering. For the bibliographic review the scientific knowledgebase has been used, which includes international articles and journals, book chapters, as well as online articles regarding drug design and protein cavity. Search keywords such as protein cavity dynamics, catalytic sites of enzymes, protein pocket etc. were used to identify bioinformatics tools with text mining. A catalogue of the most recently developed tools is presented followed by a brief description of selected tools. The selection criteria imposed for preparing the catalogue and the detailed description included the publication date, as well as the algorithms and the methods they use. The tools were then classified according to the search keywords. The findings of this research are discussed, and the algorithms and methods they use are compared, highlighting the advantages of protein cavity detection

Descubrimiento de proteínas off target humanas para la proteasa NS3 del virus del dengue y sus implicaciones para el diseño de fármacos basados en estructura

Dengue Virus (DENV) is perhaps the most relevant infectious agent in the tropical and subtropical countries. Nowadays, worldwide efforts to develop new molecules capable to prevent DENV growth have increased...El virus del Dengue (DENV) es probablemente uno de los agentes infecciosos más relevantes para los países en las áreas tropicales y subtropicales. En la actualidad se han incrementado los esfuerzos a nivel mundial para desarrollar moléculas que tengan la capacidad de inhibir el crecimiento de DENV y por ende su infección..

The Utility of Multiple Structure Torsion Angle Alignment in Protein Active Site Description (ASD)

Proteins are responsible for various functions throughout organisms, or within the systems, they operate. Active-sites or functional/ binding sites are regions responsible for activity in a protein; they serve as a catalyst for reactions, attach or bind to other molecules (ligands), and maintain function. With the profusion of protein sequence and structure data, it\u27s increasingly relevant to develop automated methods of identifying and investigating active-sites for proteins. Active-sites identification will have a direct impact: in better understanding molecular basis for diseases, assisting in drug design, the study of targeting mutants, and for functional annotation of unknown proteins. The proper knowledge of active-sites will also be beneficial in protein design and engineering. Existing computational approaches to active-site identification fall short of the ideal. Several approaches fail to include some critical information, such as, global structure, local structure, amino acid position, and local biochemical properties. Here we present msTALI (Multiple Structure Torsion Angle Alignment) to better understand and characterize protein sequence-structure-function relationships. The existing studies establishing our understanding of active-sites stress the importance of sequence, structure, and biochemical properties of proteins in their function. Therefore, an ideal method for active-site analysis should consider all the information above. The msTALI tool is unique compared to other existing software in that it incorporates sequence, structure and biochemical properties of amino acids to perform its analysis. Furthermore, msTALI generates competitive results and exhibits an ability to address proteins undergoing rigid-body motion. Additionally, the customization capability of msTALI makes it an expandable algorithm; suitable for the valid identification of active-sites. We utilize msTALI successful structural alignment capabilities under premises for active-site studies. The theoretical background is paramount since the research is interdisciplinary. We discuss molecular biological constructs, relate such descriptions to active-site research, survey previous methods, and expand our methodology. The msTALI software is used first to examine sets of proteins with confirmed ATPase activity. We use several fold families to evaluate effectiveness. Additionally, we map the trajectory for additional studies with upward of ten functional classes of proteins to strengthen the targeting set of proteins for observation. Collectively, findings will expand the understanding of active-sites, yield development for automated site description, and generate the programmatic development of software

Computational and chemical approaches to drug repurposing

Drug repurposing, which entails discovering novel therapeutic applications for already existing drugs, provides numerous benefits compared to conventional drug discovery methods. This strategy can be pursued through two primary approaches: computational and chemical. Computational methods involve the utilization of data mining and bioinformatics techniques to identify potential drug candidates, while chemical approaches involve experimental screens oriented to finding new potential treatments based on existing drugs. Both computational and chemical methods have proven successful in uncovering novel therapeutic uses for established drugs. During my PhD, I participated in several experimental drug repurposing screens based on high-throughput phenotypic approaches. Finally, attracted by the potential of computational drug repurposing pipelines, I decided to contribute and generate a web platform focused on the use of transcriptional signatures to identify potential new treatments for human disease. A summary of these studies follows: In Study I, we utilized the tetracycline repressor (tetR)-regulated mechanism to create a human osteosarcoma cell line (U2OS) with the ability to express TAR DNA-binding protein 43 (TDP-43) upon induction. TDP-43 is a protein known for its association with several neurodegenerative diseases. We implemented a chemical screening with this system as part of our efforts to repurpose approved drugs. While the screening was unsuccessful to identify modulators of TDP-43 toxicity, it revealed compounds capable of inhibiting the doxycyclinedependent TDP-43 expression. Furthermore, a complementary CRISPR/Cas9 screening using the same cell system identified additional regulators of doxycycline-dependent TDP43 expression. This investigation identifies new chemical and genetic modulators of the tetR system and highlights potential limitations of using this system for chemical or genetic screenings in mammalian cells. In Study II, our objective was to reposition compounds that could potentially reduce the toxic effects of a fragment of the Huntingtin (HTT) protein containing a 94 amino acid long glutamine stretch (Htt-Q94), a feature of Huntington's disease (HD). To achieve this, we carried out a high-throughput chemical screening using a varied collection of 1,214 drugs, largely sourced from a drug repurposing library. Through our screening process, we singled out clofazimine, an FDA-approved anti-leprosy drug, as a potential therapeutic candidate. Its effectiveness was validated across several in vitro models as well as a zebrafish model of polyglutamine (polyQ) toxicity. Employing a combination of computational analysis of transcriptional signatures, molecular modeling, and biochemical assays, we deduced that clofazimine is an agonist for the peroxisome proliferator-activated receptor gamma (PPARγ), a receptor previously suggested to be a viable therapeutic target for HD due to its role in promoting mitochondrial biogenesis. Notably, clofazimine was successful in alleviating the mitochondrial dysfunction triggered by the expression of Htt-Q94. These findings lend substantial support to the potential of clofazimine as a viable candidate for drug repurposing in the treatment of polyQ diseases. In Study III, we explored the molecular mechanism of a previously identified repurposing example, the use of diethyldithiocarbamate-copper complex (CuET), a disulfiram metabolite, for cancer treatment. We found CuET effectively inhibits cancer cell growth by targeting the NPL4 adapter of the p97VCP segregase, leading to translational arrest and stress in tumor cells. CuET also activates ribosomal biogenesis and autophagy in cancer cells, and its cytotoxicity can be enhanced by inhibiting these pathways. Thus, CuET shows promise as a cancer treatment, especially in combination therapies. In Study IV, we capitalized on the Molecular Signatures Database (MSigDB), one of the largest signature repositories, and drug transcriptomic profiles from the Connectivity Map (CMap) to construct a comprehensive and interactive drug-repurposing database called the Drug Repurposing Encyclopedia (DRE). Housing over 39.7 million pre-computed drugsignature associations across 20 species, the DRE allows users to conduct real-time drugrepurposing analysis. This can involve comparing user-supplied gene signatures with existing ones in the DRE, carrying out drug-gene set enrichment analyses (drug-GSEA) using submitted drug transcriptomic profiles, or conducting similarity analyses across all database signatures using user-provided gene sets. Overall, the DRE is an exhaustive database aimed at promoting drug repurposing based on transcriptional signatures, offering deep-dive comparisons across molecular signatures and species. Drug repurposing presents a valuable strategy for discovering fresh therapeutic applications for existing drugs, offering numerous benefits compared to conventional drug discovery methods. The studies conducted in this thesis underscore the potential of drug repurposing and highlight the complementary roles of computational and chemical approaches. These studies enhance our understanding of the mechanistic properties of repurposed drugs, such as clofazimine and disulfiram, and reveal novel mechanisms for targeting specific disease pathways. Additionally, the development of the DRE platform provides a comprehensive tool to support researchers in conducting drug-repositioning analyses, further facilitating the advancement of drug repurposing studies

Modeling the Binding of Inhibitors/Drugs to the Human Serotonin Transporter

Human serotonin transporter (hSERT), a membrane protein from the neurotransmitter sodium symporter family, is implicated in depression disorder and has been the primary target of antidepressant discovery research for several decades. Since the currently available antidepressants may cause adverse effects and have several limitations, novel drugs are highly desired. However, the efforts to develop better therapeutics are hampered by the lack of a crystal structure of hSERT. Knowledge of the binding site of the drug and its orientation in the protein is crucial in structure-based drug discovery. We employed a novel computational protocol comprised of active site detection, docking, scoring, molecular dynamics simulations, and absolute binding free energy (ABFE) calculations to elucidate the binding site and the binding mode of a dual hSERT/5HT-1A blocker SSA-426 and our in-house hSERT inhibitor DJLDU-3-79 in hSERT. Through this approach, we propose that both of these inhibitors bind in the S1 pocket of hSERT and in a similar orientation. This disproves the earlier hypothesis that both these inhibitors bind in the S2 site; however, we are in agreement with the earlier hypothesis that both of the ligands orient similarly. Further, we resolved the ambiguity in binding energies and binding trends of the tricyclic antidepressant drugs clomipramine, imipramine, and desipramine with leucine transporter (LeuT) (a bacterial homologue of hSERT) through relative binding free energy (RBFE) calculations. Based on our RBFE results, we proposed that clomipramine should have the highest affinity for LeuT, followed by imipramine and desipramine. Finally, to achieve accuracy in binding energy estimations and to perform all CHARMM simulations, we developed CHARMM general force field parameters (CGenFF) for fifteen monoamine transporter ligands