Exploration of potential inhibitors for tuberculosis via structure-based drug design, molecular docking, and molecular dynamics simulation studies

Mycobacterial β-carbonic anhydrases: Molecular biology, role in the pathogenesis of tuberculosis and inhibition studies

Mycobacterium tuberculosis (Mtb), which causes tuberculosis (TB), is still a major global health problem. According to the World Health Organization (WHO), TB still causes more deaths worldwide than any other infectious agent. Drug-sensitive TB is treatable using first-line drugs; treatment of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB requires second- and third-line drugs. However, due to the long duration of treatment, the noncompliance of patients with different levels of resistance of Mtb to these drugs has worsened the situation. Previously developed anti-TB drugs targeted the replication machinery, protein synthesis, and cell wall biosynthesis pathways of Mtb. Therefore, novel drugs targeting alternate pathways crucial for the survival and pathogenesis of Mtb in the human host are needed. The genome of Mtb encodes three β-carbonic anhydrases (CAs) that are fundamental for pH homeostasis, hypoxia, survival, and pathogenesis. Recently, several studies have shown that the β-CAs of Mtb could be inhibited both in vitro and in vivo using small chemical molecules, suggesting that these enzymes could be novel targets for developing anti-TB compounds that are devoid of resistance by Mtb. In addition, homologs of β-CAs are absent in humans; therefore, drugs developed to target these enzymes might have minimal off-target effects. In this work, we describe the roles of β-CAs in Mtb and discuss bioinformatics and cheminformatics tools used in development and discovery of novel inhibitors of these enzymes. In addition, we summarize the in vitro and in vivo studies demonstrating that the β-CAs of Mtb are indeed druggable targets.

Tanshinone IIA from Salvia miltiorrhiza alleviates follicular maturation arrest symptoms in zebrafish via binding to the human androgen receptors and modulating Tox3 and Dennd1a

Follicular maturation arrest is a prevalent endocrine disorder characterized by hormonal imbalance, ovarian dysfunction, and metabolic disturbances leading to Polycystic ovarian syndrome (PCOS). Tanshinone IIA (TIIA), a bioactive compound derived from Salvia miltiorrhiza, has shown promising therapeutic potential in various diseases, including cardiovascular diseases and cancer. However, its effects on reproductive health and gynecological disorders, particularly PCOS, remain poorly understood. In this study, we investigated the potential therapeutic effects of TIIA on ovarian function. Using a combination of experimental and computational approaches, we elucidated the molecular mechanisms underlying TIIA's pharmacological impact on ovarian function, follicular development, and androgen receptor signaling. Molecular docking and dynamics simulations revealed that TIIA interacts with the human androgen receptor (HAR), modulating its activity and downstream signaling pathways. Our results demonstrate that TIIA treatment alleviates PCOS-like symptoms in a zebrafish model, including improved follicular development, lowered GSI index, improved antioxidant status (SOD, CAT), decreased LDH levels, and enhanced AChE levels by regulating Tox3 and Dennd1a pathway. Our findings suggest that TIIA may hold promise as a novel therapeutic agent for the management of PCOS or ovulation induction.

Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches

Decaprenylphosphoryl-β-D-ribose-2'-epimerase (DprE1), a crucial enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 enzyme (PDB: 4KW5). Based on their binding affinities (< - 9.0 kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R2) value of 0.9608 and predictive coefficient (Q2) value of 0.7313. The induced fit docking (IFD) studies and in-silico prediction of anti-TB sensitivity for these top 10 hits were also implemented. Molecular dynamics simulations (MDS) were performed for the top 5 hit molecules for 200 ns to check the stability of the hits with DprE1. Based on their conformational stability throughout the 200 ns simulation, hit 2 (chEMBL_SDF:357100) was identified as the best hit against DprE1 with an accepted safety profile. The MD results were also in accordance with the docking score, MM-GBSA value, and 3D-QSAR predicted activity. The hit 2 molecule, (N-(3-((2-(((1r,4r)-4-(dimethylamino)cyclohexyl)amino)-9-isopropyl-9H-purin-6-yl)amino)phenyl)acrylamide) could serve as a lead for the discovery of a novel DprE1 inhibiting anti-TB drug.

Computational Approaches for the Structure-Based Identification of Novel Inhibitors Targeting Nucleoid-Associated Proteins in Mycobacterium Tuberculosis

Implementation of computational tools in the identification of novel drug targets for Tuberculosis (TB) has been a promising area of research. TB has been a chronic infectious disease caused by Mycobacterium tuberculosis (Mtb) localized primarily on the lungs and it has been one of the most successful pathogen in the history of mankind. Extensively arising drug resistivity in TB has made it a global challenge and need for new drugs has become utmost important.The involvement of Nucleoid-Associated Proteins (NAPs) in maintaining the structure of the genomic material and regulating various cellular processes like transcription, DNA replication, repair and recombination makes significant, has opened a new arena to find the drugs targeting Mtb. The current study aims to identify potential inhibitors of NAPs through a computational approach. In the present work we worked on the eight NAPs of Mtb, namely, Lsr2, EspR, HupB, HNS, NapA, mIHF and NapM. The structural modelling and analysis of these NAPs were carried out. Moreover, molecular interaction were checked and binding energy was identified for 2500 FDA-approved drugs that were selected for antagonist analysis to choose novel inhibitors targeting NAPs of Mtb. Drugs including Amikacin, streptomycin, kanamycin, and isoniazid along with eight FDA-approved molecules that were found to be potential novel targets for these mycobacterial NAPs and have an impact on their functions. The potentiality of several anti-tubercular drugs as therapeutic agents identified through computational modelling and simulation unlocks a new gateway for accomplishing the goal to treat TB.

A drug design strategy based on molecular docking and molecular dynamics simulations applied to development of inhibitor against triple-negative breast cancer by Scutellarein derivatives

Triple-negative breast cancer (TNBC), accounting for 10-15% of all breast malignancies, is more prevalent in women under 40, particularly in those of African descent or carrying the BRCA1 mutation. TNBC is characterized by the absence of estrogen and progesterone receptors (ER, PR) and low or elevated HER2 expression. It represents a particularly aggressive form of breast cancer with limited therapeutic options and a poorer prognosis. In our study, we utilized the protein of TNBC collected from the Protein Data Bank (PDB) with the most stable configuration. We selected Scutellarein, a bioactive molecule renowned for its anti-cancer properties, and used its derivatives to design potential anti-cancer drugs employing computational tools. We applied and modified structural activity relationship methods to these derivatives and evaluated the probability of active (Pa) and inactive (Pi) outcomes using pass prediction scores. Furthermore, we employed in-silico approaches such as the assessment of absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters, and quantum calculations through density functional theory (DFT). Within the DFT calculations, we analyzed Frontier Molecular Orbitals, specifically the Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO). We then conducted molecular docking and dynamics against TNBC to ascertain binding affinity and stability. Our findings indicated that Scutellarein derivatives, specifically DM03 with a binding energy of -10.7 kcal/mol and DM04 with -11.0 kcal/mol, exhibited the maximum binding tendency against Human CK2 alpha kinase (PDB ID 7L1X). Molecular dynamic simulations were performed for 100 ns, and stability was assessed using root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) parameters, suggesting significant stability for our chosen compounds. Furthermore, these molecules met the pharmacokinetics requirements for potential therapeutic candidates, displaying non-carcinogenicity, minimal aquatic and non-aquatic toxicity, and greater aqueous solubility. Collectively, our computational data suggest that Scutellarein derivatives may serve as potential therapeutic agents for TNBC. However, further experimental investigations are needed to validate these findings.

Exploring biogenic chalcones as DprE1 inhibitors for antitubercular activity via in silico approach

Cases of drug-resistant tuberculosis (TB) have increased worldwide in the last few years, and it is a major threat to global TB control strategies and the human population. Mycobacterium tuberculosis is a common causative agent responsible for increasing cases of TB and as reported by WHO, approximately, 1.5 million death occurred from TB in 2020. Identification of new therapies against drug-resistant TB is an urgent need to be considered primarily. The current investigation aims to find the potential biogenic chalcone against the potential targets of drug-resistant TB via in silico approach. The ligand library of biogenic chalcones was screened against DprE1. Results of molecular docking and in silico ADMET prediction revealed that ZINC000005158606 has lead-like properties against the targeted protein. Pharmacophore modeling was done to identify the pharmacophoric features and their geometric distance present in ZINC000005158606. The binding stability study performed using molecular dynamics (MD) simulation of the DprE1-ZINC000005158606 complex revealed the conformational stability of the complex system over 100 ns with minimum deviation. Further, the in silico anti-TB sensitivity of ZINC000005158606 was found to be higher as compared to the standards against Mycobacterium tuberculosis. The overall in silico investigation indicated the potential of identified hit to act as a lead molecule against Mycobacterium tuberculosis.

In silico guided designing of optimized benzochalcones derivatives as potent CYP1B1 inhibitors: An integrated in vitro and ONIOM study

Cytochrome P4501B1 (CYP1B1) is reported to be overexpressed in various malignancies including ovarian, lung, lymph, and breast cancers. The overexpression of this enzyme is accountable for the biotransformation-based inactivation of some anti-cancer drugs i.e. Docetaxel, Paclitaxel, and Cisplatin. To circumvent solutions to this issue, the current study reports some optimized derivatives of benzochalcone as selective CYP1B1 inhibitors. The optimized derivatives were screened using some structure-based drug-designing approaches including molecular docking and molecular dynamics. The implemented approaches revealed that all the designed molecules demonstrated not only essential interactions with key amino acid residues but also maintained stability within the active site of CYP1B1. Furthermore, to validate the in-silico results and develop a SAR, the designed molecules were subsequently synthesized and tested for their ability to selectively inhibit CYP1B1 over CYP1A1 using well established EROD assay. This assay results suggested that compounds 1(c), 1(d), and 1(e) are eightfold more selective CYP1B1 inhibitors over CYP1A1 with IC₅₀ values ranging from 0.06 to 0.09 μM respectively. Among these, compound 1(d) manifested potent inhibitory activity i.e. IC₅₀ of 0.06 μM with 24 folds selectivity over 1A1. To have a better insight into the binding pattern of 1(d) within CYP1B1 and precisely compute binding affinity for 1(d)-CYP1B1 complex, one of the advanced QM/MM approaches i.e. ONIOM has been implemented. Where 1(d)-CYP1B1 complex conferred comparable binding affinity in terms of ΔG (kcal/mol) with that of ANF-CYP1B1 complex. This research could provide a suitable starting point for the development of more potent multi-functional compounds with CYP1B1 inhibitory activity.

Explicit molecular dynamics simulation studies to discover novel natural compound analogues as Mycobacterium tuberculosis inhibitors

Tuberculosis (TB) in one of the dreadful diseases present globally. This is caused by Mycobacterium tuberculosis. Mycobacterium tuberculosis dethiobiotin synthetase (MtDTBS) is an essential enzyme in biotin biosynthesis and is an ideal target to design and develop novel inhibitors. In order to effectively combat this disease six natural compound (butein) analogues were subjected to molecular docking to determine their binding mode and the binding affinities. The resultant complex structures were subjected to 500 ns simulation run to estimate their binding stabilities using GROMACS. The molecular dynamics simulation studies provided essential evidence that the systems were stable during the progression of 500 ns simulation run. The root mean square deviation (RMSD) of all the systems was found to be below 0.3 nm stating that the systems are well converged. The radius of gyration (Rg) profiles indicated that the systems were highly compact without any major fluctuations. The principle component analysis (PCA) and Gibbs energy landscape studies have revealed that the comp3, comp5 and comp11 systems navigated marginally through the PC2. The intermolecular interactions have further demonstrated that all the compounds have displayed key residue interactions, firmly holding the ligands at the binding pocket. The residue Lys37 was found consistently to interact with all the ligands highlighting its potential role in inhibiting the MtDTBS. Our investigation further put forth two novel compounds (comp10 and comp11) as putative antituberculosis agents. Collectively, we propose six compounds has plausible inhibitors to curtail TB and further can act as scaffolds in designing new compounds.

In Silico Design and Investigation of Novel Thiazetidine Derivatives as Potent Inhibitors of PrpR in Mycobacterium tuberculosis

Tuberculosis is one of the most life-threatening acute infectious diseases diagnosed in humans. In the present investigation, a series of 16 new disubstituted 1,3-thiazetidines derivatives is designed, and investigated via various in silico methods for their potential as anti-tubercular agent by evaluating their ability to block the active site of PrpR transcription factor protein of Mycobacterium tuberculosis. The efficacy of the molecules was initially assessed with the help of AutoDock Vina algorithm. Further Glide module is used to redock the previously docked complexes. The binding energies and other physiochemical properties of the designed molecules were evaluated using the Prime-MM/GBSA and the QikProp module, respectively. The results of docking revealed the nature, site of interaction and the binding affinity between the proposed candidates and the active site of PrpR. Further the inhibitory effect of the scaffolds was predicted and evaluated employing a machine learning-based algorithm and was used accordingly. Further, the molecular dynamics simulation studies ascertained the binding characteristics of the unique 13, when analysed across a time frame of 100 ns with GROMACS software. The results show that the proposed 1,3-thiazetidine derivatives such as 10, 11, 13 and 14 could be potent and selective anti-tubercular agents as compared to the standard drug Pyrazinamide. Finally, this study concludes that designed thiazetidines can be employed as anti-tubercular agents. Undeniably, the results may guide the experimental biologists to develop safe and non-toxic drugs against tuberculosis by demanding further in vivo and in vitro analyses.

Synthetic approaches to potent heterocyclic inhibitors of tuberculosis: A decade review

Tuberculosis (TB) continues to be a significant global health concern with about 1.5 million deaths annually. Despite efforts to develop more efficient vaccines, reliable diagnostics, and chemotherapeutics, tuberculosis has become a concern to world health due to HIV, the rapid growth of bacteria that are resistant to treatment, and the recently introduced COVID-19 pandemic. As is well known, advances in synthetic organic chemistry have historically enabled the production of important life-saving medications that have had a tremendous impact on patients' lives and health all over the world. Small-molecule research as a novel chemical entity for a specific disease target offers in-depth knowledge and potential therapeutic targets. In this viewpoint, we concentrated on the synthesis of a number of heterocycles reported in the previous decade and the screening of their inhibitory action against diverse strains of Mycobacterium tuberculosis. These findings offer specific details on the structure-based activity of several heterocyclic scaffolds backed by their in vitro tests as a promising class of antitubercular medicines, which will be further useful to build effective treatments to prevent this terrible illness.

In-silico molecular modelling, MM/GBSA binding free energy and molecular dynamics simulation study of novel pyrido fused imidazo[4,5-c]quinolines as potential anti-tumor agents

With an alarming increase in the number of cancer patients and a variety of tumors, it is high time for intensive investigation on more efficient and potent anti-tumor agents. Though numerous agents have enriched the literature, still there exist challenges, with the availability of different targets and possible cross-reactivity. Herein we have chosen the phosphoinositide 3-kinase (PI3K) as the target of interest and investigated the potential of pyrido fused imidazo[4,5-c]quinoline derivatives to bind strongly to the active site, thereby inhibiting the progression of various types of tumors. The AutoDock, Glide and the Prime-MM/GBSA analysis are used to execute the molecular docking investigation and validation for the designed compounds. The anti-tumor property evaluations were carried out by using PASS algorithm. Based on the GLIDE score, the binding affinity of the designed molecules towards the target PI3K was evaluated. The energetics associated with static interactions revealed 1j as the most potential candidate and the dynamic investigations including RMSD, RMSF, Rg, SASA and hydrogen bonding also supported the same through relative stabilization induced through ligand interactions. Subsequently, the binding free energy of the Wortmannin and 1j complex calculated using MM-PBSA analysis. Further evaluations with PASS prediction algorithm also supported the above results. The studies reveal that there is evidence for considering appropriate pyrido fused imidazo[4,5-c]quinoline compounds as potential anti-tumor agents.

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.

Machine Learning in Antibacterial Drug Design

Advances in computer hardware and the availability of high-performance supercomputing platforms and parallel computing, along with artificial intelligence methods are successfully complementing traditional approaches in medicinal chemistry. In particular, machine learning is gaining importance with the growth of the available data collections. One of the critical areas where this methodology can be successfully applied is in the development of new antibacterial agents. The latter is essential because of the high attrition rates in new drug discovery, both in industry and in academic research programs. Scientific involvement in this area is even more urgent as antibacterial drug resistance becomes a public health concern worldwide and pushes us increasingly into the post-antibiotic era. In this review, we focus on the latest machine learning approaches used in the discovery of new antibacterial agents and targets, covering both small molecules and antibacterial peptides. For the benefit of the reader, we summarize all applied machine learning approaches and available databases useful for the design of new antibacterial agents and address the current shortcomings.

Identification of novel inhibitors for Prp protein of Mycobacterium tuberculosis by structure based drug design, and molecular dynamics simulations

In this study, we assess the effective inhibition of a series of thiazolidine derivatives (1a-1q) were adopting structure-based drug design. Thiazolidine is a five-membered ring structure with thioether and amino groups at positions 1 and 3. Although, thiazolidine may bind to a wide range of protein targets, it is a major heterocyclic core in medicinal chemistry. Different scoring utilities including AutoDock Vina, Glide, and MM/GBSA analysis were performed to commensurate the improvement of screening progress. The evaluated binding affinities were validated by molecular dynamics simulations over a period of 20 ns for the interactions between the Mycobacterium tuberculosis protein PrpR with three novel scaffolds (1b, 1j, and 1k). All the scaffolds exhibited distinct stable interactions with the significant residues like Arg169, Arg197, Tyr248, Arg308, and Gly311 respectively. Further, the inhibitory activities of scaffolds were predicted and evaluated by machine learning based algorithm to rank the above proposed compounds. This study reveals the potential of 1k and 1j as effective inhibitor candidates for the treatment of tuberculosis.