Molecular Docking Based in-Silico Approach Reveals the Potential Inhibitory Activity of Nsaids against Sars-Cov-2 Proteins

The rapid rate of mutation of the RNA genome of the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is responsible for the emergence of viral variants, leading to the enhanced survivability of the virus. Hence, searching for new drugs that can restrict new viral infections by interacting with wild-type and mutated viral proteins is important. However, new drug development's economic and time-constraining nature makes drug repurposing a more viable solution to address the problem. In this work, we conducted a computational study to screen 23 Non-Steroidal Anti-Inflammatory Drugs (NSAID) interactions with 5 major viral proteins of SARS-CoV-2 that are mainly involved in host infection. Our in-silico results establish a database that shows that different NSAID ligands interact with the different viral proteins with good binding affinities. Stabilizing point mutations were introduced within the conserved amino acids involved in ligand-protein interactions. Redocking the NSAID ligands with these mutated viral proteins showed that the NSAID ligands could bind with the mutated and wild-type viral proteins with comparable binding affinities. We conclude that the NSAID ligands could be repurposed as therapeutic drugs against the SARS-CoV-2 virus. Additionally, our work generated a repository that includes binding affinities, possible modes of interaction, and specific interacting residues of the protein (wild-type and mutated) ligand complexes that could be used for future validation studies. Further, our results point to the potential of these drugs to treat other viral infections with similar disease etiology.Copyright All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research.

Computational investigation of Arbutus serratifolia Salisb molecules as new potential SARS-CoV-2 inhibitors

The outbreak of the current pandemic and the evolution of virus resistance against standard drugs led to the emergency of new and potent antiviral agents. Owing to its crucial role in viral replication, the protease enzyme is taken into survey to be a promising target for antiviral drug therapy using computational methods. In order to bring this important class of natural products in the limelight of research for prospective application as chemotherapeutic agents, the anti-SARS-CoV-2 activity of some bioactive molecules obtained from Arbutus serratifolia Salisb which is an Algerian medicinal plant, was investigated using in-silico methods. The molecular docking was performed by AutoDock Vina and UCSF Chimera, as well as ADMET and drug-likeness properties of these molecules were calculated using preADMET web-based application and the Swiss ADME server respectively. The phytochemicals (from Pr(1) to Pr(12)) were tested for their pharmacokinetic properties and docked into the main protease binding site on (PDB ID: 6Y84) in order to find a promising antiviral ligand. All tested molecules induced binding affinities into the binding pocket of (PDB ID: 6Y84) with energy scores ranging from moderate to better (from -6.4 to -8.00 kcal/mol). It is worthy to note that both Pr(2): (1S,5R,6S,8S,9S)-6,8-Dihydroxy-8-methyl-1,5,6,7,8,9-hexahydrocyclopenta [c] pyran-1-yl-β-D-glucopyranoside and Pr(7): ((1S,5S,6S,9S)-1-(β-D-Glucopyranosyloxy)-14-oxo-1,5,6,9-tetrahydro-1H-2,15-dioxacyclopenta [cd] inden-8-yl) methyl acetate, were found to be the best inhibitors with binding affinities (-7.7 kcal/mol and -8.0 kcal/mol), respectively, by virtue of the fact that all these tested molecules exhibited good binding affinities compared with those of Ritonavir and Nirmatrelvir (-1.73 and -1.93 kcal/mol), respectively, which are used as standard antiviral drugs to prevent viral growth. The amino acids: His-163;Glu-166;Arg-188;Thr-190 and Gln-192 represent the key residues of the interaction of SARS-CoV-2 main protease with Pr(7). Furthermore, the results of pharmacodynamic and pharmacokinetic investigations revealed that Pr(6), Pr(8) and Pr(9) uphold the drug-likeness criteria and more particularly, these substances can be absorbed by the human intestine. In addition, all these molecules were shown to be neither hepatotoxic nor significantly noxious to human organism. These natural products are therefore promising inhibitor candidates of viral main protease. However, further in-vitro, in-vivo and even clinical assays are required to probe their functional mechanisms and then to assess their antiviral potency against COVID-19. © 2023 Walter de Gruyter GmbH, Berlin/Boston 2023.

FingerDTA: A Fingerprint-Embedding Framework for Drug-Target Binding Affinity Prediction

Many efforts have been exerted toward screening potential drugs for targets, and conducting wet experiments remains a laborious and time-consuming approach. Artificial intelligence methods, such as Convolutional Neural Network (CNN), are widely used to facilitate new drug discovery. Owing to the structural limitations of CNN, features extracted from this method are local patterns that lack global information. However, global information extracted from the whole sequence and local patterns extracted from the special domain can influence the drugtarget affinity. A fusion of global information and local patterns can construct neural network calculations closer to actual biological processes. This paper proposes a Fingerprint-embedding framework for Drug-Target binding Affinity prediction (FingerDTA), which uses CNN to extract local patterns and utilize fingerprints to characterize global information. These fingerprints are generated on the basis of the whole sequence of drugs or targets. Furthermore, FingerDTA achieves comparable performance on Davis and KIBA data sets. In the case study of screening potential drugs for the spike protein of the coronavirus disease 2019 (COVID-19), 7 of the top 10 drugs have been confirmed potential by literature. Ultimately, the docking experiment demonstrates that FingerDTA can find novel drug candidates for targets. All codes are available at http://lanproxy.biodwhu.cn:9099/mszjaas/FingerDTA.git. © 2018 Tsinghua University Press.

Reinforcement Learning and Graph Neural Networks for Designing Novel Drugs with Optimized Affinity: Application to SARS-CoV-2

Drugs are generally designed for a specific target protein. Recent studies have demonstrated the capability of deep learning-based models to accelerate and cheapen the drug development process. The proposed deep learning models can generate novel molecules with optimized drug-like properties. However, the properties addressed are often limited and may be misleading. This is because they do not reflect the complete information about the binding affinity of the designed drug and the target protein. In this work, we exploit the state-of-The-Art progress made in drug-Target-Affinity (DTA) prediction to assess the binding affinity of drugs generated by a developed molecular generator against the corona-virus main protease (SARS-CoV-2 Mpro). The molecular generator is a recurrent neural network-based model, while the DTA predictor is a graph neural network (GNN), famously known as GraphDTA. We train the molecular generator using reinforcement learning (RL) to optimize the GraphDTA-predicted score. As this is the first benchmark of this kind (to the best of our knowledge), we report our benchmarking results;of 1.79% desirability;with the hope of motivating future improvements in this regard. © 2022 IEEE.

Saponin-based PAK-1 inhibitors as promising agents for struggle against SARS-CoV-2 infection

The serine/threonine p21-activating PAK kinases which act as important mediators of the Rho family of GTPases (Rho GTPases) Cdc42ĝ€¢GTP and Racĝ€¢GTP. PAK1 is one of the key molecules in the regulation of cytoskeletal actin assembly, phenotypic signaling, gene expression, and directly affects many cellular processes such as cell motility, invasion, metastasis, cell growth, angiogenesis, cell cycle progression. To date, several sulphated steroidal saponins have been reported to block the PAK1-dependent growth of A549 lung cancer. In this study, we investigated molecular interactions of N-triterpene saponins and PAK1 in silico molecular docking, and further evaluated the binding affinities. Molecular docking simulation was performed through AutoDock 4.2.2. an automated docking tool. We found that N-triterpene saponin 2 had the higher binding affinity towards PAK1 targeted protein. To the best of our knowledge, no report on N-triterpene saponins as a PAK1 inhibitor. © 2022 ACM.

Differences between Omicron SARS-CoV-2 RBD and other variants in their ability to interact with cell receptors and monoclonal antibodies.

SARS-CoV-2 remains a health threat with the continuous emergence of new variants. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). By using constant-pH Monte Carlo simulations, the free energy of interactions between the RBD from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and various mAbs) were predicted. Computed RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms (rs142984500 and rs4646116) typically found in Europeans which indicates a genetic susceptibility. This is amplified for Omicron (BA.1) and its sublineages BA.2 and BA.3. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 32 studied binders, groups of mAbs were identified from weak to strong binding affinities (e.g. S2K146). These mAbs with strong binding capacity and especially their combination are amenable to experimentation and clinical trials because of their high predicted binding affinities and possible neutralization potential for current known virus mutations and a universal coronavirus.Communicated by Ramaswamy H. Sarma.

DeepGLSTM: Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity

Development of new drugs is an expensive and time-consuming process. Due to the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can reduce the time span needed to develop new drugs by probing the list of existing FDA-approved drugs and their properties to reuse them for combating the new disease. We propose a novel architecture DeepGLSTM, which is a Graph Convolutional network and LSTM based method that predicts binding affinity values between the FDA-approved drugs and the viral proteins of SARS-CoV-2. Our proposed model has been trained on Davis, KIBA (Kinase Inhibitor Bioactivity), DTC (Drug Target Commons), Metz, ToxCast and STITCH datasets. We use our novel architecture to predict a Combined Score (calculated using Davis and KIBA score) of 2,304 FDA-approved drugs against 5 viral proteins. On the basis of the Combined Score, we prepare a list of the top-18 drugs with the highest binding affinity for 5 viral proteins present in SARS-CoV-2. Subsequently, this list may be used for the creation of new useful drugs. Copyright © 2022 by SIAM.

Computational Study on SARS-CoV-2 Viral Protein Interaction with Natural Compounds of Coriandrum sativum L

The SARS-COV-2 become a global pandemic causing significant mortality and morbidity all around the world. Until now there are no effective drugs or vaccines available against SARS-CoV-2. In this regard, medicinal plants captured enormous attention, as natural products are safe and easily available bioactive compounds in which maintain the disease homeostasis. Amongst, natural compounds of Coriandrum sativum L (coriander) have proved to be effective in viral infection, as they possess antiviral and anti-inflammatory activities. However, molecular regulation of such bioactivities remains elusive. We performed molecular docking analyses using AutoDock Vina to investigate the potential inhibitory activities of the seven natural compounds of coriander (limonene, geraniol, gamma-terpinene, geranyl acetate, caffeic acid, ferulic acid, gallic acid) against the essential proteins of SARS-CoV-2 (main protease (Mpro), nonstructural protein-13 (NSP-13), Papaine like protease (PLpro) and RNA dependent RNA polymerase(RdRp)) together with two main inflammatory proteins ( cyclooxygenase-2 (COX-2) and interleukin-6 (IL- 6)). The empirical and knowledge-based algorithm of AutoDock Vina was utilized to calculate free binding energies of ligands and BIOVIA discovery studio 2020 tool was used to visualize docking results. Our results reveal that gallic acid has a strong binding affinity to Mpro (-5.8 kcal/mol) and NSP13 (-7.0 kcal/mol) forming five and three conventional hydrogen bonds respectively. Further, caffeic acid demonstrates a higher binding affinity to PLpro (-7.4 kcal/mol) and RdRp (-6.7 kcal/mol) while securing four and three conventional hydrogen bonds respectively. Interestingly, both COX-2 (-6.9 kcal/mol) and IL-6 (-6.3 kcal/mol) also show a higher binding affinity to gallic acids. In addition, gallic acid stabilizes three conventional hydrogen bonds with COX-2 whereas it forms four conventional hydrogen bonds with IL-6. Further, drug-likeness properties of gallic acid and caffeic acid were determined using the SWISSADME server. Our results show that both gallic acid and caffeic do not violate Lipinski rules suggesting these compounds as new antiviral and anti-inflammatory drug candidates for SARS-CoV-2. © 2022 IEEE.

A Domain Model for SARS-Co V-2 Rational Vaccine Design to Engineer a Prediction Tool for Binding Affinity and Antigenicity

One of our greatest present challenges are designing vaccines against SARS COV2 and its variants. Rational vaccine design uses computational methods prior to development of a vaccine for testing in animals and humans the latest methods in rational vaccine design use machine learning techniques to predict binding affinity and antigenicity but offer the researchers only isolated stand-Alone tools. A difficulty that software engineers and data scientist face in development of tools for doctors and researchers is their lack of knowledge of the medical domain. This paper presents a set of domain model developed in collaboration between software engineers and a medical researcher in the process of building a tool scientists could use to predict binding affinity and antigenicity of potential designs of SARS COV2 vaccines. A domain model visualizes the real-world entities and their interrelationships, that together define the domain space. This domain model will be useful to other software engineers trying to predict other characteristics of vaccines, such as potential autoimmunity response. © 2021 IEEE.