Discovery and design of new PI3K inhibitors through pharmacophore-based virtual screening, molecular docking, and binding free energy analysis

An in silico approach to identify novel and potential Akt1 (protein kinase B-alpha) inhibitors as anticancer drugs

Akt1 (protein kinase B) has become a major focus of attention due to its significant functionality in a variety of cellular processes and the inhibition of Akt1 could lead to a decrease in tumour growth effectively in cancer cells. In the present work, we discovered a set of novel Akt1 inhibitors by using multiple computational techniques, i.e. pharmacophore-based virtual screening, molecular docking, binding free energy calculations, and ADME properties. A five-point pharmacophore hypothesis was implemented and validated with AADRR38. The obtained R2 and Q2 values are in the acceptable region with the values of 0.90 and 0.64, respectively. The generated pharmacophore model was employed for virtual screening to find out the potential Akt1 inhibitors. Further, the selected hits were subjected to molecular docking, binding free energy analysis, and refined using ADME properties. Also, we designed a series of 6-methoxybenzo[b]oxazole analogues by comprising the structural characteristics of the hits acquired from the database. Molecules D1-D10 were found to have strong binding interactions and higher binding free energy values. In addition, Molecular dynamic simulation was performed to understand the conformational changes of protein-ligand complex.

Target-Based Small Molecule Drug Discovery for Colorectal Cancer: A Review of Molecular Pathways and In Silico Studies

Colorectal cancer is one of the most prevalent cancer types. Although there have been breakthroughs in its treatments, a better understanding of the molecular mechanisms and genetic involvement in colorectal cancer will have a substantial role in producing novel and targeted treatments with better safety profiles. In this review, the main molecular pathways and driver genes that are responsible for initiating and propagating the cascade of signaling molecules reaching carcinoma and the aggressive metastatic stages of colorectal cancer were presented. Protein kinases involved in colorectal cancer, as much as other cancers, have seen much focus and committed efforts due to their crucial role in subsidizing, inhibiting, or changing the disease course. Moreover, notable improvements in colorectal cancer treatments with in silico studies and the enhanced selectivity on specific macromolecular targets were discussed. Besides, the selective multi-target agents have been made easier by employing in silico methods in molecular de novo synthesis or target identification and drug repurposing.

Integrated computational approach for in silico design of new purinyl pyridine derivatives as B-Raf kinase inhibitors

B-Raf is one among the most frequently mutating proto-oncogene which is associated with the serine/threonine Raf kinase family involved in the RAS-RAF-MEK-ERK pathway, which is the most deregulated pathway in human cancers. Mutant B-Raf ^V600E got an excellent scope for investigation in cancer as a potential therapeutic target. Formerly B-Raf^V600E is considered the molecular target for numerous antitumor compounds like purinyl pyridine and pyrimidine derivatives. In the current research work using molecular docking approach of Schrodinger Glide 5.6 version, ligand docking, pharmacophore-based virtual screening, binding free energy calculations of a series of 2-amino purinyl pyridine and pyrimidine derivatives were modeled, their docking values were predicted, that were considered to be potent against B-Raf ^V600E. A five-point hypothesis accompanied by a hydrogen bond acceptor(A), two hydrogen bond donors(D), and two aromatic rings (R) was built with a justifiable R² value of 0.91 and a Q² value of 0.64. Then by using Asinex Elite Synergy database, virtual screening was performed, and identified several potential hits. Subsequently, the molecules which had interactions with the target B-Raf kinase were determined by subjecting the obtained hits for SP and XP docking processes. Finally, for the top leads obtained, binding free energies were accomplished. About 16 new purinyl pyridine molecules were also designed. Almost nine molecules manifested crucial ligand interactions and binding free energies. At the outset, this research paved the way for us in spotting new molecules with B-Raf inhibitory activity, which can further be explored to design molecules with enhanced pharmacokinetic profiles.

Discovery of 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives as novel PI3Kα inhibitors via docking-based virtual screening

PI3Kα is an attractive target for PIK3CA mutated malignant tumor and searching for lead compounds with novel scaffold is important for the development of PI3Kα inhibitors. Therefore, the strategy of docking-based virtual screening was performed to discovery potent inhibitors. The 4L2Y_A PI3Kα crystal structure was used as the model protein receptor due to its high docking reliability. After the multistep virtual screening protocol and biological evaluation, three hits were picked up and further similarity searching led to more potent 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives ES-25 and ES-27. In addition, the primary SAR of these novel derivatives was discussed, which provide a basis for the further structural modification.

Identification of therapeutic target in S2 domain of SARS nCov-2 Spike glycoprotein: Key to design and discover drug candidates for inhibition of viral entry into host cell

Humanity is facing a grieve danger of coronavirus disease-19 caused by severe acute respiratory syndrome novel coronavirus-2 (SARS nCov-2). There is an urgent need of therapeutics that can help in overcoming this global pandemic. Identifying novel therapeutic target and screening already approved drug is a faster approach in this situation. Spike glycoprotein (Sgp) of SARS nCoV-2 is potentials target where in researchers have targeted receptor binding domain (RBD) of S1 domain. The S2 domain of Sgp also plays a pivotal role in viral entry, but the mechanism is less understood. We analyzed the structure of Sgp S2 domain in pre-fusion state and Heptad repeat region in its post-fusion state available from protein data bank. Sgp shows three major regions in S2 domain, the fusion peptide (FP), heptad repeat 1 (HR1) and central helical (CH) region. The HR1 region undergoes structural changes by flipping approximately 180∘ and coil up to form a rod like structure during fusion process implying its role in viral entry into the host cell. This structural change in S2 domain helices is crucial step, if this process is hindered by targeting the HR1 and CH region then the progression of virus can be stopped. Possible binding cavity was identified near the HR1 and CH region in S2 domain and docking-based virtual screening of FDA approved drugs was performed. Promising candidates like Troxerutin, Thymopentin and Daclatasvir can be used as therapeutics provided an immediate in-vitro and clinical studies are carried out by research groups.

Development of Multi-Target Chemometric Models for the Inhibition of Class I PI3K Enzyme Isoforms: A Case Study Using QSAR-Co Tool

The present work aims at establishing multi-target chemometric models using the recently launched quantitative structure–activity relationship (QSAR)-Co tool for predicting the activity of inhibitor compounds against different isoforms of phosphoinositide 3-kinase (PI3K) under various experimental conditions. The inhibitors of class I phosphoinositide 3-kinase (PI3K) isoforms have emerged as potential therapeutic agents for the treatment of various disorders, especially cancer. The cell-based enzyme inhibition assay results of PI3K inhibitors were curated from the CHEMBL database. Factors such as the nature and mutation of cell lines that may significantly alter the assay outcomes were considered as important experimental elements for mt-QSAR model development. The models, in turn, were developed using two machine learning techniques as implemented in QSAR-Co: linear discriminant analysis (LDA) and random forest (RF). Both techniques led to models with high accuracy (ca. 90%). Several molecular fragments were extracted from the current dataset, and their quantitative contributions to the inhibitory activity against all the proteins and experimental conditions under study were calculated. This case study also demonstrates the utility of QSAR-Co tool in solving multi-factorial and complex chemometric problems. Additionally, the combination of different in silico methods employed in this work can serve as a valuable guideline to speed up early discovery of PI3K inhibitors.