Identification and characterization of repurposed small molecule inhibitors of Mycobacterium tuberculosis caseinolytic protease B (ClpB) as anti-mycobacterials.

Mycobacterium tuberculosis (Mtb) caseinolytic protease B (ClpB) is a chaperone possessing a unique ability to resolubilize the aggregated proteins in vivo. ClpB has been shown to be important for the survival of Mtb within the host. Thus, it appears to be a promising target to develop new therapeutic molecules against tuberculosis. In this study, we have screened FDA approved compounds in silico to identify inhibitors against Mtb ClpB. In our screen, several compounds interacted with ClpB. The top four compounds, namely framycetin, gentamicin, ribostamycin and tobramycin showing the highest binding energy were selected for further investigation. MD simulations and tryptophan-based quenching of ClpB-drug complexes established that the selected inhibitors stably interacted with the target protein. The inhibitor and protein complexes were found to be stabilized by hydrogen bonding, and hydrophobic interactions. Although, the compounds did not affect the ATPase activity of ClpB significantly, the protein resolubilization activity of ClpB was remarkably reduced in their presence. All four compounds potently inhibited the growth of Mtb H37Ra. The antimycobacterial activity of the compounds appears to be due the inhibition of functional ClpB oligomer formation, in turn affecting its chaperonic activity.

Targeting of essential mycobacterial replication enzyme DnaG primase revealed Mitoxantrone and Vapreotide as novel mycobacterial growth inhibitors.

Tuberculosis (TB) is the second leading cause of mortality after COVID-19, with a global death toll of 1.6 million in 2021. The escalating situation of drug-resistant forms of TB has threatened the current TB management strategies. New therapeutics with novel mechanisms of action are urgently required to address the current global TB crisis. The essential mycobacterial primase DnaG with no structural homology to homo sapiens presents itself as a good candidate for drug targeting. In the present study, Mitoxantrone and Vapreotide, two FDA-approved drugs, were identified as potential anti-mycobacterial agents. Both Mitoxantrone and Vapreotide exhibit a strong Minimum Inhibitory Concentration (MIC) of ≤25µg/ml against both the virulent (M.tb-H37Rv) and avirulent (M.tb-H37Ra) strains of M.tb. Extending the validations further revealed the inhibitory potential drugs in ex vivo conditions. Leveraging the computational high-throughput multi-level docking procedures from the pool of ~2700 FDA-approved compounds, Mitoxantrone and Vapreotide were screened out as potential inhibitors of DnaG. Extensive 200 ns long all-atoms molecular dynamic simulation of DnaGDrugs complexes revealed that both drugs bind strongly and stabilize the DnaG during simulations. Reduced solvent exposure and confined motions of the active centre of DnaG upon complexation with drugs indicated that both drugs led to the closure of the active site of DnaG. From this study's findings, we propose Mitoxantrone and Vapreotide as potential anti-mycobacterial agents, with their novel mechanism of action against mycobacterial DnaG.

Unravelling the potential of Triflusal as an anti-TB repurposed drug by targeting replication protein DciA.

The increasing prevalence of drug-resistant Tuberculosis (TB) is imposing extreme difficulties in controlling the TB infection rate globally, making treatment critically challenging. To combat the prevailing situation, it is crucial to explore new anti-TB drugs with a novel mechanism of action and high efficacy. The Mycobacterium tuberculosis (M.tb)DciA is an essential protein involved in bacterial replication and regulates its growth. DciA interacts with DNA and provides critical help in binding other replication machinery proteins to the DNA. Moreover, the lack of any structural homology of M.tb DciA with human proteins makes it an appropriate target for drug development. In this study, FDA-approved drugs were virtually screened against M.tb DciA to identify potential inhibitors. Four drugs namely Lanreotide, Risedronate, Triflusal, and Zoledronic acid showed higher molecular docking scores. Further, molecular dynamics simulations analysis of DciA-drugs complexes reported stable interaction, more compactness, and reduced atomic motion. The anti-TB activity of drugs was further evaluated under in vitro and ex vivo conditions where Triflusal was observed to have the best possible activity with the MIC of 25 µg/ml. Our findings present novel DciA inhibitors and anti-TB activity of Triflusal. Further investigations on the use of Triflusal may lead to the discovery of a new anti-TB drug.

Molecular insights into the critical role of gallate moiety of green tea catechins in modulating prion fibrillation, cellular internalization, and neuronal toxicity.

Transmissible spongiform encephalopathies (TSEs) or prion diseases are fatal neurodegenerative diseases with no approved therapeutics. TSE pathology is characterized by abnormal accumulation of amyloidogenic and infectious prion protein conformers (PrPSc) in the central nervous system. Herein, we examined the role of gallate group in green tea catechins in modulating the aggregation of human prion protein (HuPrP) using two green tea constituents i.e., epicatechin 3-gallate (EC3G; with intact gallate ring) and epigallocatechin (EGC; without gallate ring). Molecular docking indicated distinct differences in hydrogen bonding and hydrophobic interactions of EC3G and EGC at the ß2-α2 loop of HuPrP. These differences were substantiated by 44-fold higher KD for EC3G as compared to EGC with the former significantly reducing Thioflavin T (ThT) binding aggregates of HuPrP. Conformational alterations in HuPrP aggregates were validated by particle sizing, AFM analysis and A11 and OC conformational antibodies. As compared to EGC, EC3G showed relatively higher reduction in toxicity and cellular internalization of HuPrP oligomers in Neuro-2a cells. Additionally, EC3G also displayed higher fibril disaggregating properties as observed by ThT kinetics and electron microscopy. Our observations were supported by molecular dynamics (MD) simulations that showed markedly reduced α2-α3 and ß2-α2 loop mobilities in presence of EC3G that may lead to constriction of HuPrP conformational space with lowered ß-sheet conversion. In totality, gallate moiety of catechins play key role in modulating HuPrP aggregation, and toxicity and could be a new structural motif for designing therapeutics against prion diseases and other neurodegenerative disorders.

Multifaceted role of drugs: a potential weapon to outsmart Mycobacterium tuberculosis resistance by targeting its essential ThyX.

Tuberculosis (TB) is one of the prominent cause of deaths across the world and multidrug-resistant and extensively drug-resistant TB continues to pose challenges for clinicians and public health centers. The risk of death is extremely high in individuals who have compromised immune systems, HIV infection, or diabetes. Research institutes and pharmaceutical companies have been working on repurposing existing drugs as effective therapeutic options against TB. The identification of suitable drugs with multi-target affinity profiles is a widely accepted way to combat the development of resistance. Flavin-dependent thymidylate synthase (FDTS), known as ThyX, is in the class of methyltransferases and is a possible target in the discovery of novel anti-TB drugs. In this study, we aimed to repurpose existing drugs approved by Food and Drug Administration (FDA) that could be used in the treatment of TB. An integrated screening was performed based on computational procedures: high-throughput molecular docking techniques, followed by molecular dynamics simulations of the target enzyme, ThyX. After performing in silico screening using a library of 3,967 FDA-approved drugs, the two highest-scoring drugs, Carglumic acid and Mesalazine, were selected as potential candidates that could be repurposed to treat TB.Communicated by Ramaswamy H. Sarma.

Insights into the mutations leading to capreomycin resistance in S-adenosyl-L-methionine binding motif in TlyA from Mycobacterium tuberculosis.

Capreomycin is a second line antibiotic used for the treatment of drug resistant Tuberculosis (TB), primary reason of death from a solo infectious organism, Mycobacterium tuberculosis (M.tb). Capreomycin targets the ribosome of bacteria and is known to bind at the interface where the large and small ribosomal subunits interact in M.tb using an S-Adenosyl Methionine (SAM) dependent methyltransferase, TlyA (Rv1794). Besides the methyltransferase activity, TlyA has also been found to show substantial haemolytic activity. The dual activity of TlyA highlights its crucial role in pathogenesis and virulence of M.tb. In the present study, docking and molecular dynamics (MD) simulations were carried out to explore the impact of mutations in a conserved SAM binding motif, 90GASTG94, on the affinity of TlyA enzyme for SAM. Two already reported mutations, A91E and S92L, and the remaining wild type residues, Gly90, Thr93, Gly94 mutated to alanine were taken into consideration resulting in a total of six systems, wild type + SAM, G90A + SAM, A91E + SAM, S92L + SAM, T93A + SAM and G94A + SAM that were subjected to 100 ns MD simulations. Docking scores and MD simulations analyses revealed that in contrast to wild type, mutants reduced the affinity of SAM for TlyA with most prominent effect observed in case of alanine mutants. Mutations also led to the loss of hydrogen bond and hydrophobic interactions and large-scale movement of atoms evident from the principal component analyses indicating their destabilizing impact on TlyA. The present study gives insights into influence of mutations on binding of SAM to TlyA in M.tb and promoting capreomycin resistance.Communicated by Ramaswamy H. Sarma.

Predictive Outcome Modeling of Preoperative Clinical Symptoms and Electrodiagnostic Data in Tarsal Tunnel Surgery.

Background The relationship between tarsal tunnel syndrome (TTS), electrodiagnostic (Edx) findings, and surgical outcome is unknown. Analysis of TTS surgical release outcome patient satisfaction and comparison to Edx nerve conduction studies (NCSs) is important to improve outcome prediction when deciding who would benefit from TTS release. Methods Retrospective study of 90 patients over 7 years that had tarsal tunnel (TT) release surgery with outcome rating and preoperative tibial NCS. Overall, 64 patients met study inclusion criteria with enough NCS data to be classified into one of the following three groups: (1) probable TTS, (2) peripheral polyneuropathy, or (3) normal. Most patients had preoperative clinical provocative testing including diagnostic tibial nerve injection, tibial Phalen's sign, and/or Tinel's sign and complaints of plantar tibial neuropathic symptoms. Outcome measure was percentage of patient improvement report at surgical follow-up visit. Results Patient-reported improvement was 92% in the probable TTS group ( n = 41) and 77% of the non-TTS group ( n = 23). Multivariate modeling revealed that three out of eight variables predicted improvement from surgical release, NCS consistent with TTS ( p = 0.04), neuropathic symptoms ( p = 0.045), and absent Phalen's test ( p = 0.001). The R 2 was 0.21 which is a robust result for this outcome measurement process. Conclusion The best predictors of improvement in patients with TTS release were found in patients that had preoperative Edx evidence of tibial neuropathy in the TT and tibial nerve plantar symptoms. Determining what factors predict surgical outcome will require prospective evaluation and evaluation of patients with other nonsurgical modalities.

An immunoinformatics approach to design a multi-epitope vaccine against Mycobacterium tuberculosis exploiting secreted exosome proteins.

Tuberculosis is one the oldest known affliction of mankind caused by the pathogen Mycobacterium tuberculosis. Till date, there is no absolute single treatment available to deal with the pathogen, which has acquired a great potential to develop drug resistance rapidly. BCG is the only anti-tuberculosis vaccine available till date which displays limited global efficacy due to genetic variation and concurrent pathogen infections. Extracellular vesicles or exosomes vesicle (EVs) lie at the frontier cellular talk between pathogen and the host, and therefore play a significant role in establishing pathogenesis. In the present study, an in-silico approach has been adopted to construct a multi-epitope vaccine from selected immunogenic EVs proteins to elicit a cellular as well as a humoral immune response. Our designed vaccine has wide population coverage and can effectively compensate for the genetic variation among different populations. For maximum efficacy and minimum adverse effects possibilities the antigenic, non-allergenic and non-toxic B-cell, HTL and CTL epitopes from experimentally proven EVs proteins were selected for the vaccine construct. TLR4 agonist RpfE served as an adjuvant for the vaccine construct. The vaccine construct structure was modelled, refined and docked on TLR4 immune receptor. The designed vaccine construct displayed safe usage and exhibits a high probability to elicit the critical immune regulators, like B cells, T-cells and memory cells as displayed by the in-silico immunization assays. Therefore, it can be further corroborated using in vitro and in vivo assays to fulfil the global need for a more efficacious anti-tuberculosis vaccine.

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins.

BACKGROUND: Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases-most commonly neurological disorders, Alzheimer's disease, and Parkinson's disease-thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features. METHODS: In the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models. RESULTS: The RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation. CONCLUSIONS: The results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.

Computational modeling and bioinformatic analyses of functional mutations in drug target genes in Mycobacterium tuberculosis.

Tuberculosis (TB) continues to be the leading cause of deaths due to its persistent drug resistance and the consequent ineffectiveness of anti-TB treatment. Recent years witnessed huge amount of sequencing data, revealing mutations responsible for drug resistance. However, the lack of an up-to-date repository remains a barrier towards utilization of these data and identifying major mutations-associated with resistance. Amongst all mutations, non-synonymous mutations alter the amino acid sequence of a protein and have a much greater effect on pathogenicity. Hence, this type of gene mutation is of prime interest of the present study. The purpose of this study is to develop an updated database comprising almost all reported substitutions within the Mycobacterium tuberculosis (M.tb) drug target genes rpoB, inhA, katG, pncA, gyrA and gyrB. Various bioinformatics prediction tools were used to assess the structural and biophysical impacts of the resistance causing non-synonymous single nucleotide polymorphisms (nsSNPs) at the molecular level. This was followed by evaluating the impact of these mutations on binding affinity of the drugs to target proteins. We have developed a comprehensive online resource named MycoTRAP-DB (Mycobacterium tuberculosis Resistance Associated Polymorphisms Database) that connects mutations in genes with their structural, functional and pathogenic implications on protein. This database is accessible at http://139.59.12.92. This integrated platform would enable comprehensive analysis and prioritization of SNPs for the development of improved diagnostics and antimycobacterial medications. Moreover, our study puts forward secondary mutations that can be important for prognostic assessments of drug-resistance mechanism and actionable anti-TB drugs.

Author Correction: Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Emerging genetic diversity among clinical isolates of SARS-CoV-2: Lessons for today.

Considering the current pandemic of COVID-19, it is imperative to gauge the role of molecular divergence in SARS-CoV-2 with time, due to clinical and epidemiological concerns. Our analyses involving molecular phylogenetics is a step toward understanding the transmission clusters that can be correlated to pathophysiology of the disease to gain insight into virulence mechanism. As the infections are increasing rapidly, more divergence is expected followed possibly by viral adaptation. We could identify mutational hotspots which appear to be major drivers of diversity among strains, with RBD of spike protein emerging as the key region involved in interaction with ACE2 and consequently a major determinant of infection outcome. We believe that such molecular analyses correlated with clinical characteristics and host predisposition need to be evaluated at the earliest to understand viral adaptability, disease prognosis, and transmission dynamics.

Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods generate a large amount of data which could be exploited in determining multi-drug resistant (MDR-TB) associated mutations. The present work is a computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in the genes rpoB, inhA, katG, pncA, gyrA and gyrB for the drugs rifampicin, isoniazid, pyrazinamide and fluoroquinolones. The single nucleotide variations were represented by several sequence and structural features that indicate the influence of mutations on the target protein coded by each gene. We used ML algorithms - naïve bayes, k nearest neighbor, support vector machine, and artificial neural network, to build the prediction models. The classification models had an average accuracy of 85% across all examined genes and were evaluated on an external unseen dataset to demonstrate their application. Further, molecular docking and molecular dynamics simulations were performed for wild type and predicted resistance causing mutant protein and anti-TB drug complexes to study their impact on the conformation of proteins to confirm the observed phenotype.

Long-range replica exchange molecular dynamics guided drug repurposing against tyrosine kinase PtkA of Mycobacterium tuberculosis.

Tuberculosis (TB) is a leading cause of death worldwide and its impact has intensified due to the emergence of multi drug-resistant (MDR) and extensively drug-resistant (XDR) TB strains. Protein phosphorylation plays a vital role in the virulence of Mycobacterium tuberculosis (M.tb) mediated by protein kinases. Protein tyrosine phosphatase A (MptpA) undergoes phosphorylation by a unique tyrosine-specific kinase, protein tyrosine kinase A (PtkA), identified in the M.tb genome. PtkA phosphorylates PtpA on the tyrosine residues at positions 128 and 129, thereby increasing PtpA activity and promoting pathogenicity of MptpA. In the present study, we performed an extensive investigation of the conformational behavior of the intrinsically disordered domain (IDD) of PtkA using replica exchange molecular dynamics simulations. Long-term molecular dynamics (MD) simulations were performed to elucidate the role of IDD on the catalytic activity of kinase core domain (KCD) of PtkA. This was followed by identification of the probable inhibitors of PtkA using drug repurposing to block the PtpA-PtkA interaction. The inhibitory role of IDD on KCD has already been established; however, various analyses conducted in the present study showed that IDDPtkA had a greater inhibitory effect on the catalytic activity of KCDPtkA in the presence of the drugs esculin and inosine pranobex. The binding of drugs to PtkA resulted in formation of stable complexes, indicating that these two drugs are potentially useful as inhibitors of M.tb.

Protective Effects of a Neurohypophyseal Hormone Analogue on Prion Aggregation, Cellular Internalization, and Toxicity.

Herein, we report novel neuroprotective activity of the neurohypophyseal hormone analogue desmopressin (DDAVP) against toxic conformations of human prion protein. Systematic analysis using biophysical techniques in conjunction with surface plasmon resonance, high-end microscopy, conformational antibodies, and cell-based assays demonstrated DDAVP's specific binding and potent antiaggregating effects on prion protein (rPrPres). In addition to subjugating conformational conversion of rPrPres into oligomeric forms, DDAVP also exhibits potent fibril modulatory effects. It eventually ameliorated neuronal toxicity of rPrPres oligomers by significantly reducing their cellular internalization. Molecular dynamics simulations showed that DDAVP prevents ß-sheet transitions in the N-terminal amyloidogenic region of prion and induces antagonistic mobilities in its α2-α3 and ß2-α2 loop regions. Collectively, our data proposes DDAVP as a new structural motif for rational drug discovery against prion diseases.

Machine Learning From Molecular Dynamics Trajectories to Predict Caspase-8 Inhibitors Against Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder in which the death of brain cells takes place leading to loss of memory and decreased cognitive ability. AD is a leading cause of death worldwide and is progressive in nature with symptoms worsening over time. Machine learning-based computational predictive models based on 2D and 3D descriptors have been effective in identifying potential active compounds. However, the use of data from molecular dynamics (MD) trajectories for training machine learning models still needs to be explored. In the present study, descriptors have been extracted from the MD trajectories of caspase-8 ligand complexes to train models using artificial neural networks and random forest algorithms. Caspase-8 plays a key role in causing AD by cleaving amyloid precursor proteins during apoptosis leading to increased formation of the amyloid-beta peptide. A total of 43 ligands were docked using the glide module of Schrodinger software, and short MD simulations of 10 ns were performed for the calculation of MD descriptors. The MD descriptors were also combined with the 2D and 3D descriptors of chemical compounds, and individual descriptor based as well as combination models were generated. This study demonstrated that MD descriptors could be effectively used for the characterization of bioactive compounds along with lead prioritization and optimization.

Computational models for the prediction of adverse cardiovascular drug reactions.

BACKGROUND: Predicting adverse drug reactions (ADRs) has become very important owing to the huge global health burden and failure of drugs. This indicates a need for prior prediction of probable ADRs in preclinical stages which can improve drug failures and reduce the time and cost of development thus providing efficient and safer therapeutic options for patients. Though several approaches have been put forward for in silico ADR prediction, there is still room for improvement. METHODS: In the present work, we have used machine learning based approach for cardiovascular (CV) ADRs prediction by integrating different features of drugs, biological (drug transporters, targets and enzymes), chemical (substructure fingerprints) and phenotypic (therapeutic indications and other identified ADRs), and their two and three level combinations. To recognize quality and important features, we used minimum redundancy maximum relevance approach while synthetic minority over-sampling technique balancing method was used to introduce a balance in the training sets. RESULTS: This is a rigorous and comprehensive study which involved the generation of a total of 504 computational models for 36 CV ADRs using two state-of-the-art machine-learning algorithms: random forest and sequential minimization optimization. All the models had an accuracy of around 90% and the biological and chemical features models were more informative as compared to the models generated using chemical features. CONCLUSIONS: The results obtained demonstrated that the predictive models generated in the present study were highly accurate, and the phenotypic information of the drugs played the most important role in drug ADRs prediction. Furthermore, the results also showed that using the proposed method, different drugs properties can be combined to build computational predictive models which can effectively predict potential ADRs during early stages of drug development.

Structural basis for drug resistance mechanisms against anaplastic lymphoma kinase.

Drug resistance to anaplastic lymphoma kinase (ALK) inhibitors (crizotinib and ceritinib) is caused by mutation in the region encoding kinase domain of ALK. Compounds with potential ability to inhibit all strains of ALK are a solution to tackle the problem of drug resistance. In this study, we delineated positions of residues possessing the ability to make ALK drug resistant upon mutation by assessing them using five parameters (conservation index, binding-site root-mean-square deviation, protein structure stability, change in ATP, and drug-binding affinity). Four residual positions (Leu 1122, Thr 1151, Phe 1245, and Gly 1269) were ascertained. This study will be beneficial for designing drugs with better proficiency against ALK and the issues of drug resistance. This study can be taken as a pipeline for investigating drug-resistant mutations in other diseases as well.