Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

Structure-based virtual screening and molecular docking approaches to identify potential inhibitors against KIF2C to combat glioma

Glioma, a kind of malignant brain tumor, is extremely lethal. Kinesin family member 2C (KIF2C) was found to have an aberrant expression in several cancer types, including lung cancer and glioma. KIF2C may therefore be a useful therapeutic target for the treatment of glioma. In the current study, new drug candidates that may function as KIF2C enzyme inhibitors were discovered. MTi OpenScreen was used to carry out the structure-based virtual screening of an inbuilt drug library containing 150,000 compounds. These compounds belong to different classes, such as natural product-based compounds (NP-lib), purchasable approved drugs (Drugs-lib), and food constituents compound collection (FOOD-lib). Based on their binding affinities, a total of 84 compounds were further pushed to calculate ADMET properties. The compounds (16) meeting the ADMET cutoff ranges were then further docked to the receptor to find their plausible binding modes using the Glide tool's standard precision (SP) technique. The docking results were examined using the Glide gscore, and the best binding compounds (Rimacalib and Sarizotan) were chosen to test their stability with KIF2C protein through molecular dynamics (MD) simulation. Similarly, Principal Component Analysis and cross-correlation matrix were also examined. The MM/GBSA binding free energies showed a considerable energy contribution in the binding of hits with the KIF2C. Collectively, these findings strongly suggest the potential of the lead compounds to inhibit the biological function of KIF2C, emphasizing the need for further investigation in this area.Communicated by Ramaswamy H. Sarma.

Identification and molecular docking of novel chikungunya virus NSP4 inhibitory peptides from camel milk proteins

The chikungunya (CHIK) virus is an arbovirus belonging to the alphavirus (Togaviridae family). Around 85% of infected individuals suffer from symptoms such as high fever and severe joint pain; about 30 to 40% will develop a chronic joint illness. The Nsp4 protease is the most conserved protein in the alphavirus family and serves as an RNA-dependent RNA polymerase (RdRp). Targeting this enzyme might inhibit the CHIKV replication cycle. This work aims to in silico study the CHIKV RdRp inhibitory effect of peptides derived from camel milk protein as antiviral peptides. Various bioinformatics tools were recruited to identify, screen, predict and assess peptides obtained from camel milk as antiviral peptides (AVPs). During this study, CHIKV Nsp4 (polymerase) was used as a target to be inhibited by interaction with peptides derived from camel milk protein. Among 91 putative bioactive peptides, the best predicted 5 were further evaluated. Molecular docking showed that the top 5 AVPs generated better docking scores and interacted well with active sites of Nsp4 by the formation of different hydrogen bonds as well as other bonds. AVP63 and AVP20 showed the best Molecular docking and MD simulation results. The residue 315ASP of the GDD motif (catalytic core) exhibited a favorable interaction with the AVPs. The findings of this study suggest that the AVP20 derived from camel milk protein can be a potential novel CHIKV polymerase inhibitor.Communicated by Ramaswamy H. Sarma.

Comprehensive in silico analyses of flavonoids elucidating the drug properties against kidney disease by targeting AIM2

Kidney disorders are among the most common diseases and there is a scarcity of effective treatments for chronic kidney disease. There has been a progressive improvement in specific flavonoids for protective effects against kidney diseases. Flavonoids inhibit the regulatory enzymes to control inflammation-related diseases. In the present study, a hybrid approach of molecular docking analyses and molecular dynamic simulation was followed by principal component analyses and a dynamics cross-correlation matrix. In the present study, the top-ranked five flavonoids were reported, and the maximum binding affinity was observed against AIM2. Molecular docking analyses revealed that Glu_186, Phe_187, Lys_245, Glu_248, Ile_263, and Asn_265 are potent residues against AIM2 for ligand-receptor interactions. Extensive in silico analyses suggested that procyanidin is a potential molecule against AIM2. Moreover, the site-directed mutagenesis for the reported interacting residues of AIM2 could be important for further in vitro analyses. The observed novel results based on extensive computational analyses may be significant for potential drug design against renal disorders by targeting AIM2.