Computational identification of potential tau tubulin kinase 1 (TTBK1) inhibitors: a structural analog approach.

Abnormal deposition or aggregation of protein alpha-synuclein and tau in the brain leads to neurodegenerative disorders. Excessive hyperphosphorylation of tau protein and aggregations destroys the microtubule structure resulting in neurofibrillary tangles in neurons and affecting cytoskeleton structure, mitochondrial axonal transport, and loss of synapses in neuronal cells. Tau tubulin kinase 1 (TTBK1), a specific neuronal kinase is a potential therapeutic target for neurodegenerative disorders as it is involved in hyperphosphorylation and aggregation of tau protein. TTBK inhibitors are now the subject of intense study, but limited numbers are found. Hence, this study involves structure-based virtual screening of TTBK1 inhibitor analogs to obtain efficient compounds targeting the TTBK1 using docking, molecular dynamics simulation and protein-ligand interaction profile. The initial analogs set containing 3884 compounds was subjected to Lipinski rule and the non-violated compounds were selected. Docking analysis was done on 2772 compounds through Autodock vina and Autodock 4.2. Data Warrior and SwissADME was utilized to filter the toxic compounds. The stability and protein-ligand interaction of the docked complex was analyzed through Gromacs and VMD. Molecular simulation results such as RMSD, Rg, and hydrogen bond interaction along with pharmacokinetic properties showed CID70794974 as the potential hit targeting TTBKl prompting the need for further experimental investigation to evaluate their potential therapeutic efficacy in Alzheimer's disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00242-z.

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are searching for new allosteric inhibitors as orthosteric inhibitors are non-selective and exert off-target effects. In the current study, we proposed an integrated computational workflow for identifying allosteric AKT1 inhibitors as this isoform is highly correlated with poor prognosis and survival. To achieve this objective, 84 classification QSAR models with six different machine learning algorithms were developed. The models created with RDKit_RF and RDKit_kstar outperformed internal and test set validation with an ROC of 0.98. The outperformed models were then used to screen Chembl, which contains over a million compounds, for AKT1 inhibitors. The Tanimoto similarity search approach identified the compounds structurally resembling AKT allosteric inhibitors. The filtered compounds were further subjected to docking phases, molecular dynamic simulation and mmpbsa to verify the binding mode of selected ones. All these analyses suggested hit 5 (CHEMBL3948083) as the potential allosteric inhibitor of AKT1 as the stability parameters, favourable binding affinity (-107.78 ± 11.56 KJ/mol) and ligand interaction were better in comparison to other compounds and reference compound. The residual analysis demonstrated that allosteric and isoform-specific residues such as Trp80 and Val270 contributed the larger energy for ligand binding. The proposed integrated approach in this study might achieve a futuristic outcome when employed in a pharmaceutical scheme different from the conventional method.Communicated by Ramaswamy H. Sarma.

Structure based virtual screening and discovery of novel inhibitors against FabD protein of Mycobacterium tuberculosis.

The highly flexible nature of Mycobacterium tuberculosis (Mtb) can be owed to its tough cell wall and multiple gene interaction system which makes it resistant to frontline TB drugs. Mycolic acids are the key components of the unique cell wall that protects the organism from external threats. Proteins of the fatty acid synthesis pathway are evolutionarily conserved that enables cellular survival in harsh conditions and hence have become attractive targets. Malonyl Co-A Acyl carrier protein transacylase (FabD; MCAT, EC2.3.1.39) is an enzyme in the branching point of the unique and vast fatty acid synthase (FAS-I and FAS-II) systems of Mtb. In the present investigation, in-silico structure based drug discovery with the compounds from an open source library (NPASS) is used for target fishing and employed to understand the interaction with the target protein FabD. The potential hit compounds were filtered using exhaustive docking, considering the binding energy, key residue interaction and drug likeness property. Three compounds from the library namely NPC475074 (Hit 1), NPC260631 (Hit 2) and NPC313985 (Hit 3) with binding energies -14.45, -13.29 and -12.37 respectively were taken for molecular dynamic simulation. The results suggested that Hit 3 (NPC313985) has stable interaction with FabD protein. This article further elaborates the interaction of the identified novel compounds Hit 1 and Hit 3 along with the other known compound (Hit 2) against Mtb FabD protein. The hit compounds identified from this study could be further evaluated against mutated FabD protein and considered for in-vitro evaluation.Communicated by Ramaswamy H. Sarma.

Identification of allosteric inhibitor against AKT1 through structure-based virtual screening.

AKT (serine/threonine protein kinase) is a potential therapeutic target for many types of cancer as it plays a vital role in cancer progression. Many AKT inhibitors are already in practice under single and combinatorial therapy. However, most of these inhibitors are orthosteric / pan-AKT that are non-selective and non-specific to AKT kinase and their isoforms. Hence, researchers are searching for novel allosteric inhibitors that bind in the interface between pH and kinase domain. In this study, we performed structure-based virtual screening from the afroDB (a diverse natural compounds library) to find the potential inhibitor targeting the AKT1. These compounds were filtered through Lipinski, ADMET properties, combined with a molecular docking approach to obtain the 8 best compounds. Then we performed molecular dynamics simulation for apoprotein, AKT1 with 8 complexes, and AKT1 with the positive control (Miransertib). Molecular docking and simulation analysis revealed that Bianthracene III (hit 1), 10-acetonyl Knipholonecyclooxanthrone (hit 2), Abyssinoflavanone VII (hit 5) and 8-c-p-hydroxybenzyldiosmetin (hit 6) had a better binding affinity, stability, and compactness than the reference compound. Notably, hit 1, hit 2 and hit 5 had molecular features required for allosteric inhibition.

Latent tuberculosis: interaction of virulence factors in Mycobacterium tuberculosis.

Tuberculosis (TB) remains a prominent health concern worldwide. Besides extensive research and vaccinations available, attempts to control the pandemic are cumbersome due to the complex physiology of Mycobacterium tuberculosis (Mtb). Alongside the emergence of drug-resistant TB, latent TB has worsened the condition. The tubercle bacilli are unusually behaved and successful with its strategies to modulate genes to evade host immune system and persist within macrophages. Under latent/unfavorable conditions, Mtb conceals itself from immune system and modulates its genes. Among many intracellular modulated genes, important are those involved in cell entry, fatty acid degradation, mycolic acid synthesis, phagosome acidification inhibition, inhibition of phagosome-lysosome complex and chaperon protein modulation. Though the study on these genes date back to early times of TB, an insight on their inter-relation within and to newly evolved genes are still required. This review focuses on the findings and discussions on these genes, possible mechanism, credibility as target for novel drugs and repurposed drugs and their interaction that enables Mtb in survival, pathogenesis, resistance and latency.

Genetic alterations and clinical dimensions of oral cancer: a review.

Oral cancer ranks sixth most prevalent type of cancer worldwide due to its alarming increase every year. Progression of oral cancer depends on various heterogeneity pathways, but their exact mechanism remains unclear. Genetic aberrations on oral cancer cells set back the effectiveness of existing therapies and make it more challenging by triggering drug resistance. To understand the intricate details of oral cancer pathogenesis and for advancing current therapies, genetic modifications are the most promising approach. In this review, we tabulated the information on genetic alterations, microbial associations, aberrant signalling pathways and their clinicopathological characters on the pathogenesis of oral cancer. We primarily discussed the pitfalls of the current treatment regimen and its associated drug resistance pattern, which will provide a clear insight into developing new drugs. We also highlighted the genetic-molecular targets with their current clinical status on drug development and its outcome.

Lauric acid and myristic acid from Allium sativum inhibit the growth of Mycobacterium tuberculosis H37Ra: in silico analysis reveals possible binding to protein kinase B.

CONTEXT: The bulb of Allium sativum Linn (Alliaceae) has numerous medicinal values. Though the petroleum ether extract of the bulb has shown to exhibit antimycobacterial activity, the phytochemical(s) responsible for this inhibitory activity is not known. OBJECTIVE: To characterize the bioactive compounds in the petroleum ether extract of Allium sativum (garlic) that inhibit the growth of Mycobacterium tuberculosis H37Ra. MATERIALS AND METHODS: Bioactivity-guided fractionation was employed to isolate the bioactive compounds. Antimycobacterial activity was evaluated by well-diffusion method and microplate alamar blue assay (MABA). Infrared spectroscopy, mass spectrometry and nuclear magnetic resonance spectroscopy were used to characterize the bioactive compounds. Autodock was used to obtain information on molecular recognition, and molecular dynamics simulation was performed using GROMACS. RESULTS: The bioactive compounds that inhibited the growth of M. tuberculosis H37Ra were found to be lauric acid (LA) and myristic acid (MA). The minimal inhibitory concentration of LA and MA was found to be 22.2 and 66.7 µg/mL, respectively. In silico analysis revealed that these fatty acids could bind at the cleft between the N-terminal and C-terminal lobes of the cytosolic domain of serine/threonine protein kinase B (PknB). DISCUSSION AND CONCLUSION: The inhibition activity was dependent on the alkyl chain length of the fatty acid, and the amino acid residues involved in binding to fatty acid was found to be conserved across the Pkn family of proteins. The study indicates the possibility of using fatty acid derivatives, involving Pkn family of proteins, to inhibit the signal transduction processes in M. tuberculosis.