Denovo Designing, Virtual Screening and Lead Optimization of Potential Drug Candidate for Herpes Disease

Alzheimer’s disease (AD) is the most common form of dementia. In particular, neuroinflammation, mediated by microglia cells but also through CD8+ T-cells, actively contributes to disease pathology. Leukotrienes are involved in neuroinflammation and in the pathological hallmarks of AD. In consequence, leukotriene signaling—more specifically, the leukotriene receptors—has been recognized as a potential drug target to ameliorate AD pathology. Here, we analyzed the effects of the leukotriene receptor antagonist montelukast (MTK) on hippocampal gene expression in 5xFAD mice, a commonly used transgenic AD mouse model. We identified glial activation and neuroinflammation as the main pathways modulated by MTK. The treatment increased the number of Tmem119+ microglia and downregulated genes related to AD-associated microglia and to lipid droplet-accumulating microglia, suggesting that the MTK treatment targets and modulates microglia phenotypes in the disease model compared to the vehicle. MTK treatment further reduced infiltration of CD8+T-cells into the brain parenchyma. Finally, MTK treatment resulted in improved cognitive functions. In summary, we provide a proof of concept for MTK to be a potential drug candidate for AD and provide novel modes of action via modulation of microglia and CD8+ T-cells. Of note, 5xFAD females showed a more severe pathology, and in consequence, MTK treatment had a more pronounced effect in the females compared to the males. The effects on neuroinflammation, i.e., microglia and CD8+ T-cells, as well as the effects on cognitive outcome, were dose-dependent, therefore arguing for the use of higher doses of MTK in AD clinical trials compared to the approved asthma dose.

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Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein

Scientific Reports ◽

10.1038/s41598-021-83626-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Firoz A. Dain Md Opo ◽

Mohammed M. Rahman ◽

Foysal Ahammad ◽

Istiak Ahmed ◽

Mohiuddin Ahmed Bhuiyan ◽

...

Keyword(s):

Molecular Docking ◽

Virtual Screening ◽

Md Simulation ◽

Pharmacophore Model ◽

Pharmacophore Modeling ◽

Drug Candidate ◽

Inhibitor Of Apoptosis ◽

Inhibitor Of Apoptosis Protein ◽

Apoptosis Protein ◽

Apoptosis Process

AbstractX-linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases-3, caspases-7, and caspases-9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Different types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side effects, which is making difficulties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure-based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confirm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confirmed stability of the three compounds. Based on the finding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE-9896837409 (ZINC107434573) may serve as lead compounds to fight against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efficacy of the compounds.

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DeltaDelta Neural Networks for Lead Optimization of Small Molecule Potency

10.26434/chemrxiv.9761597.v1 ◽

2019 ◽

Author(s):

Jose Jimenez-Luna ◽

Laura Pérez-Benito ◽

Gerard Martinez-Rosell ◽

Simone Sciabola ◽

Rubben Torella ◽

...

Keyword(s):

Neural Networks ◽

Lead Optimization ◽

Learning Approach ◽

Potential Drug ◽

Protein Target ◽

Drug Molecules ◽

Simulation Based ◽

Machine Learning Approach ◽

Fundamental Challenge ◽

Deep Learning Model

The capability to rank different potential drug molecules against a protein target for potency has always been a fundamental challenge in computational chemistry due to its importance in drug design. While several simulation-based methodologies exist, they are hard to use prospectively and thus predicting potency in lead optimization campaigns remains an open challenge. Here we present the first machine learning approach specifically tailored for ranking ranking congeneric series based on deep 3D-convolutional neural networks. Furthermore we prove its effectiveness by blindly testing it on datasets provided by Janssen, Pfizer and Biogen totalling over 3246 ligands and 13 targets as well as several well-known openly available sets, representing one the largest evaluations ever performed. We also performed online learning simulations of lead optimization using the approach in a predictive manner obtaining significant advantage over experimental choice. We believe that the evaluation performed in this study is strong evidence of the usefulness of a modern deep learning model in lead optimization pipelines against more expensive simulation-based alternatives.

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In Silico Drug Designing of Protease Inhibitors to Find the Potential Drug Candidate for HIV1

Computational Biology and Bioinformatics ◽

10.11648/j.cbb.20130103.11 ◽

2013 ◽

Vol 1 (3) ◽

pp. 10 ◽

Cited By ~ 1

Author(s):

Alka Dwivedi

Keyword(s):

Protease Inhibitors ◽

In Silico ◽

Drug Candidate ◽

Potential Drug ◽

Drug Designing

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Virtual screening as therapeutic strategy of COVID-19 targeting angiotensin-converting enzyme 2

Frontiers in Molecular Immunology ◽

10.25082/fmi.2021.01.002 ◽

2021 ◽

Vol 2 (1) ◽

pp. 16-27

Author(s):

Zahra Sharifinia ◽

◽

Samira Asadi ◽

Mahyar Irani ◽

Abdollah Allahverdi ◽

...

Keyword(s):

Virtual Screening ◽

Angiotensin Converting Enzyme ◽

Host Cells ◽

Spike Protein ◽

Potential Drug ◽

Converting Enzyme ◽

Angiotensin Converting Enzyme 2 ◽

Approved Drugs ◽

Fda Approved Drugs

Objective: The receptor-binding domain (RBD) of the S1 domain of the SARS-CoV- 2 Spike protein performs a key role in the interaction with Angiotensin-converting enzyme 2 (ACE2), leading to both subsequent S2 domain-mediated membrane fusion and incorporation of viral RNA in host cells. Methods: In this study, we investigated the inhibitor’s targeted compounds through existing human ACE2 drugs to use as a future viral invasion. 54 FDA approved drugs were selected to assess their binding affinity to the ACE2 receptor. The structurebased methods via computational ones have been used for virtual screening of the best drugs from the drug database. Key Findings: The ligands “Cinacalcet” and “Levomefolic acid” highaffinity scores can be a potential drug preventing Spike protein of SARS-CoV-2 and human ACE2 interaction. Levomefolic acid from vitamin B family was proved to be a potential drug as a spike protein inhibitor in previous clinical and computational studies. Besides that, in this study, the capability of Levomefolic acid to avoid ACE2 and Spike protein of SARS-CoV-2 interaction is indicated. Therefore, it is worth to consider this drug for more in vitro investigations as ACE2 and Spike protein inhibition candidate. Conclusion: The two Cinacalcet and Levomefolic acid are the two ligands that have highest energy binding for human ACE2 blocking among 54 FDA approved drugs.

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“Analysis of binding properties of integrase protein to find out potential drug candidate for HIV-2 through insilico molecular docking ”

IOSR Journal of Pharmacy and Biological Sciences ◽

10.9790/3008-09224144 ◽

2014 ◽

Vol 9 (2) ◽

pp. 41-44

Author(s):

Vijay Laxmi Saxena ◽

◽

Shrasti Gupta ◽

Shweta Maurya

Keyword(s):

Molecular Docking ◽

Drug Candidate ◽

Potential Drug ◽

Binding Properties ◽

Integrase Protein

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Υπολογιστικά εργαλεία για την αναζήτηση νέων βιοδραστικών ενώσεων σε επιλεγμένους πρωτεϊνικούς στόχους

10.12681/eadd/40331 ◽

2017 ◽

Author(s):

Ευτυχία Κρίτση

Keyword(s):

Molecular Dynamics ◽

Molecular Docking ◽

Virtual Screening ◽

Molecular Dynamics Simulations ◽

In Silico ◽

Pharmacophore Modeling ◽

Lead Optimization ◽

Dynamics Simulations ◽

Hiv 1

Στην παρούσα διατριβή πραγματοποιήθηκε εκτενής μελέτη για την αναζήτηση πρόδρομων βιοδραστικών ενώσεων (hits) από χημικές βιβλιοθήκες για τρείς βιολογικούς στόχους, μέσω της εφαρμογής εμπορικά διαθέσιμων in silico τεχνικών και μεθοδολογιών.Οι στόχοι που επιλέχθηκαν ανήκουν σε διαφορετικές κατηγορίες πρωτεϊνών με μεγάλο φαρμακευτικό ενδιαφέρον, που όμως παρουσιάζουν διαφορετικό επίπεδο ωριμότητας όσον αφορά την εφαρμογή υπολογιστικών εργαλείωνγια την ανακάλυψη νέων φαρμακευτικών ενώσεων. Συγκεριμένα, οι στόχοι που μελετήθηκαν είναι οι ακόλουθοι:•το ένζυμο της 14-α διμεθυλάσης της λανοστερόλης (CYP51) για την αναζήτηση νέων πρόδρομων βιοδραστικών ενώσεων με αντιμικροβιακές ιδιότητες,•το ένζυμο της HIV τύπου 1 πρωτεάσης (HIV-1 PR) για την αναζήτηση νέων πρόδρομων βιοδραστικών ενώσεων με αντι-HIV δράση,•ο διαμεμβρανικός υποδοχέας της Αγγειοτασίνης ΙΙ (ΑΤ1) για την αναζήτηση νέων πρόδρομων βιοδραστικών με αντιυπερτασική δράσηΟι κυριότερες τεχνικές που χρησιμοποιήθηκαν για την αναζήτηση πρόδρομων βιοδραστικών ενώσεων περιλαμβάνουν την Εικονική Σάρωση (Virtual Screening) με χρήση Φαρμακοφόρων Μοντέλων (Pharmacophore modeling), τη Μοριακή Πρόσδεση (Molecular Docking), την πρόβλεψη μοριακών ιδιοτήτων καθώς και Προσομοιώσεις Μοριακής Δυναμικής (Molecular Dynamics Simulations). Η στρατηγική που ακολουθήθηκε διαφέρει σημαντικά ανά στόχο όσον αφορά τη μεθοδολογική προσέγγιση και την επιλογή των υπολογιστικών εργαλείων-αλγορίθμων, δίνοντας έμφαση στη συμπληρωματικότητα των αποτελεσμάτων τους. Για την ανάδειξη των πρόδρομων βιοδραστικών ενώσεων, πραγματοποιήθηκαν in vitro βιολογικές δοκιμές των ενώσεων που προτάθηκαν μέσω των υπολογιστικών τεχνικών. Οι ενώσεις που επιλέχθηκαν παρουσίασαν ανασταλτική δράση (ή συγγένεια πρόσδεσης) σε ικανοποιητικό εύρος τιμών 102 nM–μΜ για να χαρακτηριστούν πρόδρομες βιοδραστικές. Μείζονος σημασίας είναι και το γεγονός ότι οι δομικοί σκελετοί των προτεινόμενων ενώσεων για κάθε στόχο, είναι διαφορετικοί τόσο μεταξύ τους όσο και συγκρινόμενοι με τα υφιστάμενα φαρμακευτικά μόρια. Ως εκ τούτου, μπορούν να αποτελέσουν κατάλληλα "υποστρώματα" για το επόμενο στάδιο που αφορά τη βελτιστοποίησή τους προς ενώσεις-οδηγούς (hit to lead optimization) και δυνητικά προς νέα φαρμακευτικά προϊόντα.

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