The SARS-CoV-2 pandemic, caused by the novel coronavirus, has posed a significant global health threat since its emergence in late 2019. The World Health Organization declared the outbreak a pandemic on March 11, 2020, due to its rapid global spread and impact on public health. New variants have raised concerns about their potential impact on the transmission of the virus and the effectiveness of current diagnostic tools, treatments, and vaccines. This study aims to investigate the effect of new variants in Pakistani virus strains on human receptors, specifically ACE2 and NRP1. In-silico analysis provides a powerful tool to analyze the potential impact of new variants on protein structure, function, and interactions. The SARS-CoV-2 virus is evolving quickly. After being exposed in Wuhan, SARS-CoV-2 underwent numerous mutations, leading to several variants' emergence. These variants stabilize the interaction of spike protein with human receptors ACE2 and NRP1. The study aims to check the molecular effect of these variants on human receptors using the in-silico approach. We use in-silico mutational tools to analyze new variants in SARS-CoV-2 and to check the molecular interaction of spike protein with human receptors (ACE2 and NRP1). Genomic sequences of 41 SARS-CoV-2 strains were sequenced using Ion Torrent (NGS) and submitted to the GISAID database. Spike protein of SARS-CoV-2 sequence trimmed and translated into a protein sequence using ExPasy. We used multiple sequence alignments to check for variants in the spike protein of strains. We utilized mutation tools such as Mupro, SIFT, SNAP2, and Mutpred2.3D structures of SARS-CoV-2 spike proteins (wild and mutated) to analyze further the mutations, ACE2 and NRP1 modelled by the ITASSER protein modelling server. Interactions of spike proteins (wild and mutant) analyzed by MD Docking, Simulation, and MMGBSA RESULTS: Variants I210T, V213G, S371F, S373P, T478K, F486V, Y505H, and D796Y were identified in SARS-CoV-2 Pakistani strains' spike protein. Variant Y505H were found to affect protein function. MD Docking, MMGBSA and MD simulation revealed that these variants increased spike protein's binding affinity with human receptors (ACE2 and NRP1). MD simulation revealed that mutated spike protein stabilized earlier than wild when interacting with ACE2 after 40 ns and interaction with NRP1 stabilized after 30 ns for mutated spike protein compared to wild. These variants in Pakistani strains of SARS-CoV-2 are increasing the stability of spike protein with human receptors. These findings provide insight into how the SARS-CoV-2 virus evolves and adapts to human hosts. This information may help develop strategies to control the virus's spread and develop effective treatments and vaccines in the future.

Background: Neisseria gonorrhoeae is the bacterium responsible for gonorrhoea, one of the most common sexually transmitted infections (STIs) globally. In 2020, the World Health Organization (WHO) estimated 82.4 million new cases of Neisseria gonorrhoeae infections. Current treatments rely on antibiotics, but the emergence of multi drug resistance (MDR) strains poses a significant threat to public health. This research aims to use computational modeling of cyclotides as antimicrobial agents targeting the Neisseria gonorrhoeae PorB Porin protein to inhibit its pathogenicity.Methodology: The PorB Porin protein was retrieved from the Protein Data Bank (PDB ID: 4AUI), cleaned, and visualized using Discovery Visual Studio. Physicochemical properties were predicted using ProtParam. Cyclotides were obtained from the CyBase database, with 3D models generated and refined via the Swiss Model for docking studies. HDOCK was used for molecular docking. Toxicity and allergenicity predictions were performed with ToxinPred and AlgPred. A heatmap of the peptide was created using Protein-Sol. Molecular dynamics (MD) simulations were conducted for 100,000 picoseconds using Desmond from Schrödinger LLC, while binding energy was analyzed using MMGBSA. Immune response simulations were done with C-ImmSim 10.1, and peptide simulation in water was performed via WebGro.Results: The protein’s GRAVY value is −0.539, indicating moderate hydrophilicity, and its isoelectric point is 9.14, suggesting a fundamental nature. Globa D had the highest docking score (−270.04 kcal/mol) and was deemed non-toxic and non-allergenic. MD simulations showed stable protein-ligand interactions, and MMGBSA revealed a low binding energy of −36.737 kcal/mol. Immune simulations indicated an effective immune response and peptide simulations demonstrated Globa D’s stability in water, making it a potential candidate for pharmaceutical applications.Conclusion: Globa D proved the best drug candidate against Neisseria gonorrhoeae by inhibiting PorB Porin protein chain A. Further in vitro and in vivo studies are recommended to validate these findings and explore clinical applications.

The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying novel anticancer targets and developing new drugs. Computational approaches can significantly quicken research on complex disorders, enabling the discovery of effective treatments. This study explores anticancer targets by assessing the potential of naturally occurring compounds derived from various plants to cure colorectal cancer. Twenty compounds were sourced from PubChem, and the RPS20 protein structure was obtained from AlphaFold, and mutation “V50S” was added. Validation of mutated RPS20 protein was performed using the Ramachandran plot and ERRAT. Binding sites on the mutated RPS20 protein were identified with DeepSite, followed by virtual screening to pinpoint the most promising natural lead drug candidate. Indirubin emerged as the lead drug candidate, fulfilling all ADMET criteria and exhibiting a good binding affinity. Further development included designing an AI-based drug using the WADDAICA server, which was validated through molecular docking, molecular dynamics (MD) simulation, and MMGBSA. The electronic properties of indirubin were studied using DFT calculations. The results show a moderate HOMO-LUMO gap, indicating its potential reactivity and the possible capability for biological target interactions. These findings indicate that indirubin could serve as a potent and effective cancer inhibitor, offering high efficacy with minimal side effects.

This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'