Modulation of ribosomal subunit associations by eIF6 is critical for mitotic exit and cancer progression.

Moderating the pool of active ribosomal subunits is critical for maintaining global translation rates. A factor crucial for modulating the 60S ribosomal subunits is eukaryotic translation initiation factor 6. Release of eIF6 from 60S is essential to permit 60S interactions with 40S. Here, using the N106S mutant of eIF6, we show that disrupting eIF6 interaction with 60S leads to an increase in vacant 80S. It further highlights a dichotomy in the anti-association activity of eIF6 that is distinct from its role in 60S biogenesis and shows that the nucleolar localization of eIF6 is not dependent on uL14-BCCIP interactions. Limiting active ribosomal pools markedly deregulates translation especially in mitosis and leads to chromosome segregation defects, mitotic exit delays and mitotic catastrophe. Ribo-Seq analysis of the eIF6-N106S mutant shows a significant downregulation in the translation efficiencies of mitotic factors and specifically transcripts with long 3'UTRs. eIF6-N106S mutation also limits cancer invasion, and this role is correlated with the overexpression of eIF6 only in high-grade invasive cancers suggesting that deregulation of eIF6 is probably not an early event in cancers. Thus, this study highlights the segregation of eIF6 functions and its role in moderating 80S availability for mitotic translation and cancer progression.

Investigation of the impact of nonsynonymous mutations on thyroid peroxidase dimer.

Congenital hypothyroidism is one of the most common preventable endocrine disorders associated with thyroid dysgenesis or dyshormonogenesis. Thyroid peroxidase (TPO) gene defect is mainly responsible for dyshormonogenesis; a defect in the thyroid hormone biosynthesis pathway. In Bangladesh, there is limited data regarding the genetic etiology of Congenital Hypothyroidism (CH). The present study investigates the impact of the detected mutations (p.Ala373Ser, and p.Thr725Pro) on the TPO dimer protein. We have performed sequential molecular docking of H2O2 and I- ligands with both monomers of TPO dimer to understand the iodination process in thyroid hormone biosynthesis. Understanding homodimer interactions at the atomic level is a critical challenge to elucidate their biological mechanisms of action. The docking results reveal that mutations in the dimer severely disrupt its catalytic interaction with essential ligands. Molecular dynamics simulation has been performed to validate the docking results, thus realizing the consequence of the mutation in the biological system's mimic. The dynamics results expose that mutations destabilize the TPO dimer protein. Finally, principal component analysis exhibits structural and energy profile discrepancies in wild-type and mutant dimers. The findings of this study highlight that the mutations in TPO protein can critically affect the dimer structure and loss of enzymatic activity is persistent. Other factors also might influence the hormone synthesis pathway, which is under investigation.

Molecular investigation of TSHR gene in Bangladeshi congenital hypothyroid patients.

The disorder of thyroid gland development or thyroid dysgenesis accounts for 80-85% of congenital hypothyroidism (CH) cases. Mutations in the TSHR gene are mostly associated with thyroid dysgenesis, and prevent or disrupt normal development of the gland. There is limited data available on the genetic spectrum of congenital hypothyroid children in Bangladesh. Thus, an understanding of the molecular aetiology of thyroid dysgenesis is a prerequisite. The aim of the study was to investigate the effect of mutations in the TSHR gene on the small molecule thyrogenic drug-binding site of the protein. We identified two nonsynonymous mutations (p.Ser508Leu, p.Glu727Asp) in the exon 10 of the TSHR gene in 21 patients with dysgenesis by sequencing-based analysis. Later, the TSHR368-764 protein was modeled by the I-TASSER server for wild-type and mutant structures. The model proteins were targeted by thyrogenic drugs, MS437 and MS438 to perceive the effect of mutations. The damaging effect in drug-protein complexes of mutants was explored by molecular docking and molecular dynamics simulations. The binding affinity of wild-type protein was much higher than the mutant cases for both of the drug ligands (MS437 and MS438). Molecular dynamics simulates the dynamic behavior of wild-type and mutant complexes. MS437-TSHR368-764MT2 and MS438-TSHR368-764MT1 showed stable conformations in biological environments. Finally, Principle Component Analysis revealed structural and energy profile discrepancies. TSHR368-764MT1 exhibited much more variations than TSHR368-764WT and TSHR368-764MT2, emphasizing a more damaging pattern in TSHR368-764MT1. This genetic study might be helpful to explore the mutational impact on drug binding sites of TSHR protein which is important for future drug design and selection for the treatment of congenital hypothyroid children with dysgenesis.

Isolation and In Silico Prediction of Potential Drug-like Compounds with a New Dimeric Prenylated Quinolone Alkaloid from Zanthoxylum rhetsa (Roxb.) Root Extracts Targeted against SARS-CoV-2 (Mpro).

A new dimeric prenylated quinolone alkaloid, named 2,11-didemethoxy-vepridimerine A, was isolated from the root bark of Zanthoxylum rhetsa, together with twelve known compounds. The structure of the new compound was elucidated on the basis of spectroscopic investigations (NMR and Mass). The interaction of the isolated compounds with the main protease of SARS-CoV-2 (Mpro) was evaluated using molecular docking followed by MD simulations. The result suggests that 2,11-didemethoxy-vepridimerine A, the new compound, has the highest negative binding affinity against the Mpro with a free energy of binding of -8.5 Kcal/mol, indicating interaction with the Mpro. This interaction was further validated by 100 ns MD simulation. This implies that the isolated new compound, which can be employed as a lead compound for an Mpro-targeting drug discovery program, may be able to block the action of Mpro.

Remdesivir analogs against SARS-CoV-2 RNA-dependent RNA polymerase.

The COVID-19 pandemic has already taken many lives but is still continuing its spread and exerting jeopardizing effects. This study is aimed to find the most potent ligands from 703 analogs of remdesivir against RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus . RdRp is a major part of a multi-subunit transcription complex of the virus, which is essential for viral replication. In clinical trials, it has been found that remdesivir is effective to inhibit viral replication in Ebola and in primary human lung cell cultures; it effectively impedes replication of a broad-spectrum pre-pandemic bat coronaviruses and epidemic human coronaviruses. After virtual screening, 30 most potent ligands and remdesivir were modified with triphosphate. Quantum mechanics-based quantitative structure-activity relationship envisages the binding energy for ligands applying partial least square (PLS) regression. PLS regression remarkably predicts the binding energy of the effective ligands with an accuracy of 80% compared to the value attained from molecular docking. Two ligands (L4:58059550 and L28:126719083), which have more interactions with the target protein than the other ligands including standard remdesivir triphosphate, were selected for further analysis. Molecular dynamics simulation is done to assess the stability and dynamic nature of the drug-protein complex. Binding-free energy results via PRODIGY server and molecular mechanics/Poisson-Boltzmann surface area method depict that the potential and solvation energies play a crucial role. Considering all computational analysis, we recommend the best remdesivir analogs can be utilized for efficacy test through in vitro and in vivo trials against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Designing potent inhibitors against the multidrug resistance P-glycoprotein.

The multidrug transporter P-glycoprotein is an ATP binding cassette (ABC) exporter responsible for resistance to tumor cells during chemotherapy. This study was designed with computational approaches aimed at identifying the best potent inhibitors of P-glycoprotein. Although many compounds have been suggested to inhibit P-glycoprotein, however, their information on bioavailability, selectivity, ADMET properties, and molecular interactions has not been revealed. Molecular docking, ADMET analysis, molecular dynamics, Principal component analysis (PCA), and binding free energy calculations were performed. Two compounds D1 and D2 showed the best docking score against P-glycoprotein and both compounds have 4-thiazolidinone derivatives containing indolin-3 one moiety are novel anti-tumor compounds. ADMET calculation analysis predicted D1 and D2 to have acceptable pharmacokinetic properties. The MD simulation discloses that D1-P-glycoprotein and D2-P-glycoprotein complexes are in stable conformation as apo-form. Hydrophobic amino acid such as phenylalanine plays significant on the interactions of inhibitors. Principal component analysis shows that both complexes are relatively similar variables as apo-form except planarity and Columbo energy profile. In addition, Quantitative Structural Activity Relationship (QSAR) of the ligand candidates were subjected to the principal component analysis (PCA) for pattern recognition. Partial-least-square (PLS) regression analysis was further utilized to model drug candidates' QSAR for subsequent prediction of the binding energy of validated drug candidates. PCA revealed groupings of the drug candidates based on the similarity or differences in drug candidates QSAR. Moreover, the developed PLS regression accurately predicted the values of the binding energy of drug candidates, with low residual error of prediction.Communicated by Ramaswamy H. Sarma.

Molecular modelling approaches predicted 1,2,3-triazolyl ester of ketorolac (15K) to be a novel allosteric modulator of the oncogenic kinase PAK1.

P21-activated kinases (PAKs) are serine/threonine protein kinase which have six different isoforms (PAK1-6). Of those, PAK1 is overexpressed in many cancers and considered to be a major chemotherapeutic target. Most of the developed PAK1 inhibitor drugs work as pan-PAK inhibitors and show undesirable toxicity due to having untargeted kinase inhibition activities. Selective PAK1 inhibitors are therefore highly desired and oncogenic drug hunters are trying to develop allosteric PAK1 inhibitors. We previously synthesized 1,2,3-triazolyl ester of ketorolac (15K) through click chemistry technique, which exhibits significant anti-cancer effects via inhibiting PAK1. Based on the selective anticancer effects of 15K against PAK1-dependent cancer cells, we hypothesize that it may act as an allosteric PAK1 inhibitor. In this study, computational analysis was done with 15K to explore its quantum chemical and thermodynamic properties, molecular interactions and binding stability with PAK1, physicochemical properties, ADMET, bioactivities, and druglikeness features. Molecular docking analysis demonstrates 15K as a potent allosteric ligand that strongly binds to a novel allosteric site of PAK1 (binding energy ranges - 8.6 to - 9.2 kcal/mol) and does not target other PAK isoforms; even 15K shows better interactions than another synthesized PAK1 inhibitor. Molecular dynamics simulation clearly supports the stable binding properties of 15K with PAK1 crystal. Density functional theory-based calculations reveal that it can be an active drug with high softness and moderate polarity, and ADMET predictions categorize it as a non-toxic drug as evidenced by in vitro studies with brine shrimp and fibroblast cells. Structure-activity relationship clarifies the role of ester bond and triazol moiety of 15K in establishing novel allosteric interactions. Our results summarize that 15K selectively inhibits PAK1 as an allosteric inhibitor and in turn shows anticancer effects without toxicity.

Virtual screening, molecular dynamics, density functional theory and quantitative structure activity relationship studies to design peroxisome proliferator-activated receptor-γ agonists as anti-diabetic drugs.

Type 2 diabetes (T2D) is generally characterized by elevated blood glucose levels, insulin resistance, and relative lack of insulin; however, insulin resistance is the predominant risk factor. Hence, the use of insulin sensitizer drugs to increase insulin sensitivity has gained immense interest as an attractive treatment option for T2D and their major target is a nuclear receptor PPAR-γ (peroxisome proliferator-activated receptor-γ). A wide range of synthetic insulin sensitizers such as thiazolidinedione act as PPAR-γ agonists thereby enhancing insulin action and improving hyperglycemia in patients. Nonetheless, they pose severe adverse effects for human, necessitating an emergent need to develop effective insulin sensitizer drugs. Herein, virtual screening of 10,000 ligands is performed and the best five ligands are identified. MET364, ILE341, CYS285, ALA292, PHE282, and LEU330 residues are found to play an important role in ligand binding. It is shown from the molecular dynamics simulations results of the top-ranked ligands that increased numbers of hydrogen bonds are formed with PPAR-γ catalytic residues. Quantum chemical calculations reveal that all the best ligands can demonstrate good thermodynamic stability and pharmacokinetic properties. Partial-least-square (PLS) regression of quantitative structural activity relationship (QSAR) is utilized to model and predict the binding energy for ligands. Principal component analysis is further explored for the best ligands' QSAR pattern recognition. Importantly, the predicted values of the binding energy of the potential ligands by the PLS regression is favourably compared with the values of binding energy obtained from molecular docking with incredible high accuracy of 98%.

Investigating the binding affinity, interaction, and structure-activity-relationship of 76 prescription antiviral drugs targeting RdRp and Mpro of SARS-CoV-2.

SARS-CoV-2 virus outbreak poses a major threat to humans worldwide due to its highly contagious nature. In this study, molecular docking, molecular dynamics, and structure-activity relationship are employed to assess the binding affinity and interaction of 76 prescription drugs against RNA dependent RNA polymerase (RdRp) and Main Protease (Mpro) of SARS-CoV-2. The RNA-dependent RNA polymerase is a vital enzyme of coronavirus replication/transcription complex whereas the main protease acts on the proteolysis of replicase polyproteins. Among 76 prescription antiviral drugs, four drugs (Raltegravir, Simeprevir, Cobicistat, and Daclatasvir) that are previously used for human immunodeficiency virus (HIV), hepatitis C virus (HCV), Ebola, and Marburg virus show higher binding energy and strong interaction with active sites of the receptor proteins. To explore the dynamic nature of the interaction, 100 ns molecular dynamics (MD) simulation is performed on the selected protein-drug complexes and apo-protein. Binding free energy of the selected drugs is performed by MM/PBSA. Besides docking and dynamics, partial least square (PLS) regression method is applied for the quantitative structure activity relationship to generate and predict the binding energy for drugs. PLS regression satisfactorily predicts the binding energy of the effective antiviral drugs compared to binding energy achieved from molecular docking with a precision of 85%. This study highly recommends researchers to screen these potential drugs in vitro and in vivo against SARS-CoV-2 for further validation of utility.

Halogenated derivatives of methotrexate as human dihydrofolate reductase inhibitors in cancer chemotherapy.

Methotrexate is a widely used anti-metabolite in cancer chemotherapy. A series of halogenated drugs is designed from Methotrexate to assess their interactions with human dihydrofolate reductase. The aim of this study is to evaluate the performance of the modified drugs compared to the parent Methotrexate. Density Functional Theory is employed to optimize these drugs. Molecular docking calculation of these optimized drugs against dihydrofolate reductase is performed to find out binding affinity. In addition, molecular dynamics simulation is considered for the complexes of best two modified drugs with their receptors. Modifications by the halogens show significant changes in the charge distribution, dipole moment, thermodynamic stability, enthalpy and free energy. The highest binding affinity value (-36.401 KJ/mol) was obtained for M14. Hybrid quantum mechanics/molecular mechanics calculation shows a binding energy of -255.140 KJ/mol. Modified drugs have significant hydrogen and non-covalent bonding interactions with amino acids of the receptor. Molecular dynamics simulation disclosed that the root-mean-square-deviation of the alpha carbon associated with M6-1KMV and M14-1KMV complexes is 2.367 Å and 2.622 Å, respectively. Moreover, the interactions between modified drugs and receptor are mostly persevered in 25 nanosecond molecular dynamics simulation. Ensemble-based docking also confirmed that modified drugs show strong non-bonding interactions with different crystallographic and molecular dynamics based conformers. The best scored drugs show considerable pharmacokinetic properties. Modified derivatives M5, M6, M8, M10, M13 and M14 show the better binding affinity and a good number of hydrogen and other non-bonding interactions with the target protein which are similar to other anticancer drugs.Communicated by Ramaswamy H. Sarma.

A computational approach to explore and identify potential herbal inhibitors for the p21-activated kinase 1 (PAK1).

The oncogenic kinase PAK1 (p21-activated kinase 1) is involved in developing many diseases including cancers, neurofibromatosis, Alzheimer's disease, diabetes (type 2), and hypertension. Thus, it is thought to be a prominent therapeutic target, and its selective inhibitors have a huge market potential. Recently, herbal PAK1 inhibitors have gained immense interest over synthetic ones mainly due to their non-toxic effects. Till date, many herbal compounds have been suggested to inhibit PAK1, but their information on selectivity, bioavailability, ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, and molecular interactions with PAK1 has not been explored. Hence, this study was designed with computational approaches to explore and identify the best herbal PAK1-blockers showing good ADMET properties, druggable features and binding affinity with PAK1. Herbal inhibitors reported here were initially filtered with Lipinski's rule of five (RO5). Then, molecular docking between these inhibitors and PAK1 catalytic sites was performed using AutoDock Vina and GOLD suite to determine the binding affinity and interactions. Finally, 200 ns molecular dynamics (MD) simulations on three top-ranked inhibitors including cucurbitacin I (C-I), nymphaeol A (NA), and staurosporine (SPN) were carried out. The binding free energies and interactions revealed that NA can strongly bind with the PAK1 catalytic cleft. PASS prediction and ADMET profiling supported that NA is appeared to be a more selective and safer inhibitor than C-I and SPN. These results conform to the previous experimental evidences, and therefore, NA from Okinawa propolis could be a promising inhibitor for treating PAK1-dependent illnesses.Communicated by Ramaswamy H. Sarma.