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.

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.

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.

Computational Screening and Exploration of Disease-Associated Genes in Alzheimer's Disease.

Alzheimer's is a neurodegenerative disease affecting large populations worldwide characterized mainly by progressive loss of memory along with various other symptoms. The foremost cause of the disease is still unclear, however various mechanisms have been proposed to cause the disease that include amyloid hypothesis, tau hypothesis, and cholinergic hypothesis in addition to genetic factors. Various genes have been known to be involved which are APOE, PSEN1, PSEN2, and APP among others. In the present study, we have used computational methods to examine the pathogenic effects of non-synonymous single nucleotide polymorphisms (SNPs) associated with ABCA7, CR1, MS4A6A, CD2AP, PSEN1, PSEN2, and APP genes. The SNPs were obtained from dbSNP database followed by identification of deleterious SNPs and prediction of their functional impact. Prediction of disease-associated mutations was performed and the impact of the mutations on the stability of the protein was carried out. To study the structural significance of the computationally prioritized mutations on the proteins, molecular dynamics simulation studies were carried out. On analysis, the SNPs with IDs rs76282929 ABCA7; CR1 rs55962594; MS4A6A rs601172; CD2AP rs61747098; PSEN1 rs63750231, rs63750265, rs63750526, rs63750577, rs63750687, rs63750815, rs63750900, rs63751037, rs63751163, rs63751399; PSEN2 rs63749851; and APP rs63749964, rs63750066, rs63750734, and rs63751039 were predicted to be deleterious and disease-associated having significant structural impact on the proteins. The current study proposes a precise computational methodology for the identification of disease-associated SNPs. J. Cell. Biochem. 118: 1471-1479, 2017. © 2016 Wiley Periodicals, Inc.

Double Mutants in DNA Gyrase Lead to Ofloxacin Resistance in Mycobacterium tuberculosis.

Fluoroquinolones are among the most important classes of highly effective antibacterial drugs, exhibiting wide range of activity to cure infectious diseases. Ofloxacin is second generation fluoroquinolone approved by FDA for the treatment of tuberculosis by selectively inhibiting DNA gyrase. However, the emergence of drug resistance owing to mutations in DNA gyrase poses intimidating challenge for the effective therapy of this drug. The double mutants GyrAA90V GyrBD500N and GyrAA90V GyrBT539N are reported to be implicated in conferring higher levels of OFX resistance. The present study was designed to unravel the molecular principles behind development of resistance by the bug against fluoroquinolones. Our results highlighted that polar interactions play critical role in the development of drug resistance and highlight the significant correlation between the free energy calculations predicted by MM-PBSA and stability of the ligand-bound complexes. Modifications at the OFX binding pocket due to amino acid substitution leads to fewer hydrogen bonds in mutants DNA gyrase-OFX complex, which determined the low susceptibility of the ligand in inhibiting the mutant protein. This study provides a structural rationale to the mutation-based resistance to ofloxacin and will pave way for development potent fluoroquinolone-based resistant-defiant drugs. J. Cell. Biochem. 118: 2950-2957, 2017. © 2017 Wiley Periodicals, Inc.

Natural polyphenolic inhibitors against the antiapoptotic BCL-2.

The apoptotic mechanism is regulated by the BCL-2 family of proteins, such as BCL-2 or Bcl-xL, which block apoptosis while Bad, Bak, Bax, Bid, Bim or Hrk induce apoptosis. The overexpression of BCL-2 was found to be related to the progression of cancer and also providing resistance towards chemotherapeutic treatments. In the present study, we found that all polyphenols (apigenin, fisetin, galangin and luteolin) bind to the hydrophobic groove of BCL-2 and the interaction is stable throughout MD simulation run. Luteolin was found to bind with highest negative binding energy and thus, claimed highest potency towards BCL-2 inhibition followed by fisetin. The hydrophobic interactions were found to be critical for stable complex formation as revealed by the vdW energy and ligplot analysis. Finally, on the basis of data obtained during the study, it can be concluded that these polyphenols have the potential to be used as lead molecules for BCL-2 inhibition.

Novel group-based QSAR and combinatorial design of CK-1δ inhibitors as neuroprotective agents.

BACKGROUND: Tar DNA binding protein 43 (TDP-43) hyperphosphorylation, caused by Casein kinase 1 (CK-1) protein isoforms, is associated with the onset and progression of Amyotrophic Lateral Sclerosis (ALS). Among the reported isoforms and splice variants of CK-1 protein superfamily, CK-1δ is known to phosphorylate different serine and threonine sites on TDP-43 protein in vitro and thus qualifies as a potential target for ALS treatment. RESULTS: The developed GQSAR (group based quantitative structure activity relationship) model displayed satisfactory statistical parameters for the dataset of experimentally reported N-Benzothiazolyl-2-Phenyl Acetamide derivatives. A combinatorial library of molecules was also generated and the activities were predicted using the statistically sound GQSAR model. Compounds with higher predicted inhibitory activity were screened against CK-1δ that resulted in to the potential novel leads for CK-1δ inhibition. CONCLUSIONS: In this study, a robust fragment based QSAR model was developed on a congeneric set of experimentally reported molecules and using combinatorial library approach, a series of molecules were generated from which we report two top scoring, CK-1δ inhibitors i.e., CHC (6-benzyl-2-cyclopropyl-4-{[(4-cyclopropyl-6-ethyl-1,3-benzothiazol-2-yl)carbamoyl]methyl}j-3-fluorophenyl hydrogen carbonate) and DHC (6-benzyl-4-{[(4-cyclopropyl-6-ethyl-1,3-benzothiazol-2-yl)carbamoyl]methyl}-2-(decahydronaphthalen-1-yl)-3-hydroxyphenyl hydrogen carbonate) with binding energy of -6.11 and -6.01 kcal/mol, respectively.

Molecular modeling and lead design of substituted zanamivir derivatives as potent anti-influenza drugs.

BACKGROUND: Influenza virus spreads infection by two main surface glycoproteins, namely hemagglutinin (HA) and neuraminidase (NA). NA cleaves the sialic acid receptors eventually releasing newly formed virus particles which then invade new cells. Inhibition of NA could limit the replication of virus to one round which is insufficient to cause the disease. RESULTS: An experimentally reported series of acylguanidine zanamivir derivatives was used to develop GQSAR model targeting NA in different strains of influenza virus, H1N1 and H3N2. A combinatorial library was developed and their inhibitory activities were predicted using the GQSAR model. CONCLUSION: The top leads were analyzed by docking which revealed the binding modes of these inhibitors in the active site of NA (150-loop). The top compound (AMA) was selected for carrying out molecular dynamics simulations for 15 ns which provided insights into the time dependent dynamics of the designed leads. AMA possessed a docking score of -8.26 Kcal/mol with H1N1 strain and -7.00 Kcal/mol with H3N2 strain. Ligand-bound complexes of both H1N1 and H3N2 were observed to be stable for 11 ns and 7 ns respectively. ADME descriptors were also calculated to study the pharmacokinetic properties of AMA which revealed its drug-like properties.

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. RESULTS: In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. CONCLUSIONS: To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Structural studies on molecular mechanisms of Nelfinavir resistance caused by non-active site mutation V77I in HIV-1 protease.

BACKGROUND: The human immunodeficiency virus (HIV-1) is a retrovirus causing acquired immunodeficiency syndrome (AIDS), which has become a serious problem across the world and has no cure reported to date. Human immunodeficiency virus (HIV-1) protease is an attractive target for antiviral treatment and a number of therapeutically useful inhibitors have been designed against it. The emergence of drug resistant mutants of HIV-1 poses a serious problem for conventional therapies that have been used so far. Until now, thirteen protease inhibitors (PIs), major mutation sites and many secondary mutations have been listed in the HIV Drug Resistance Database. In this study, we have studied the effect of the V77I mutation in HIV-PR along with the co-occurring mutations L33F and K20T through multi-nanosecond molecular dynamics simulations. V77I is known to cause Nelfinavir (NFV) resistance in the subtype B population of HIV-1 protease. We have for the first time reported the effect of this clinically relevant mutation on the binding of Nelfinavir and the conformational flexibility of the protease. RESULTS: Two HIV-PR mutants have been considered in this study - the Double Mutant Protease (DBM) V77I-L33F and Triple Mutant Protease (TPM) V77I-K20T-L33F. The molecular dynamics simulation studies were carried out and the RMSD trajectories of the unliganded wild type and mutated protease were found to be stable. The binding affinity of NFV with wild type HIV-PR was very high with a Glide XP docking score of -9.3 Kcal/mol. NFV showed decreased affinity towards DBM with a docking score of -8.0 Kcal/mol, whereas its affinity increased towards TPM (Glide XP score: -10.3). Prime/MM-GBSA binding free energy of the wild type, DBM and TPM HIV-PR docked structures were calculated as -38.9, -11.1 and -42.6 Kcal/mol respectively. The binding site cavity volumes of wild type, DBM and TPM protease were 1186.1, 1375.5 and 1042.5 Å3 respectively. CONCLUSION: In this study, we have studied the structural roles of the two HIV-PR mutations by conducting molecular dynamics simulation studies of the wild type and mutant HIV-1 PRs. The present study proposes that DBM protease showed greater flexibility and the flap separation was greater with respect to the wild type protease. The cavity size of the MD-stabilized DBM was also found to be increased, which may be responsible for the decreased interaction of Nelfinavir with the cavity residues, thus explaining the decreased binding affinity. On the other hand, the binding affinity of NFV for TPM was found to be enhanced, accounted for by the decrease in cavity size of the mutant which facilitated strong interactions with the flap residues. The flap separation of TPM was less than the wild type protease and the decreased cavity size may be responsible for its lower resistance, and hence, may be the reason for its lower clinical relevance.

Structural investigations of T854A mutation in EGFR and identification of novel inhibitors using structure activity relationships.

BACKGROUND: The epidermal growth factor receptor (EGFR) is a member of the ErbB family that is involved in a number of processes responsible for cancer development and progression such as angiogenesis, apoptosis, cell proliferation and metastatic spread. Malfunction in activation of protein tyrosine kinases has been shown to result in uncontrolled cell growth. The EGFR TK domain has been identified as suitable target in cancer therapy and tyrosine kinase inhibitors such as erlotinib have been used for treatment of cancer. Mutations in the region of the EGFR gene encoding the tyrosine kinase (TK) domain causes altered responses to EGFR TK inhibitors (TKI). In this paper we perform molecular dynamics simulations and PCA analysis on wild-type and mutant (T854A) structures to gain insight into the structural changes observed in the target protein upon mutation. We also report two novel inhibitors identified by combined approach of QSAR model development. RESULTS: The wild-type and mutant structure was observed to be stable for 26 ns and 24 ns respectively. In PCA analysis, the mutant structure proved to be more flexible than wild-type. We developed a 3D-QSAR model using 38 thiazolyl-pyrazoline compounds which was later used for prediction of inhibitory activity of natural compounds of ZINC library. The 3D-QSAR model was proved to be robust by the statistical parameters such as r2 (0.9751), q2(0.9491) and pred_r2(0.9525). CONCLUSION: Analysis of molecular dynamics simulations results indicate stability loss and increased flexibility in the mutant structure. This flexibility results in structural changes which render the mutant protein drug resistant against erlotinib. We report two novel compounds having high predicted inhibitory activity to EGFR TK domain with both wild-type and mutant structure.

Abrogation of AuroraA-TPX2 by novel natural inhibitors: molecular dynamics-based mechanistic analysis.

INTRODUCTION: Cancer is characterized by uncontrolled cell growth and genetic instabilities. The human Aurora-A kinase protein plays a crucial role in spindle assembly during mitosis and is activated by another candidate oncogene, targeting protein for Xklp2 (TPX2). It has been proposed that dissociation of Aurora A-TPX2 complex leads to disruption of mitotic spindle apparatus, thereby preventing cell division and further tumor growth. MATERIALS AND METHODS: A large natural compound library was docked against the active site of Aurora A-TPX2 complex. The protein-ligand complexes were subjected to molecular dynamics simulation to ascertain their binding stability. The drug properties of the compounds were analyzed to observe their drug-like properties. RESULTS: The virtual screening of natural compound library yielded two high scoring compounds, the first compound CTOM [ZINC ID: 38143674] (Glide score: -9.49) was stable for 17 ns while the second TTOM (Glide score: -9.07) was stable for 15 ns. While CTOM interacted with His280, Thr288 of Aurora A and Tyr34, Lys38 of TPX2, TTOM interacted with Arg285 and Arg286 in addition to the residues involved with CTOM. CONCLUSIONS: We report two natural compounds as potential drugs leads for the disruption of this complex. These ligands show a preferable docking score and have many drugs like properties within in the range of 95% of known drugs. The study provides evidence that CTOM and TTOM can efficiently inhibit the TPX2-mediated activation of Aurora A. Thus, it paves way for an elaborate investigation and establishes the importance of computational approaches as time- and cost-effective techniques.

Cheminformatics models for inhibitors of Schistosoma mansoni thioredoxin glutathione reductase.

Schistosomiasis is a neglected tropical disease caused by a parasite Schistosoma mansoni and affects over 200 million annually. There is an urgent need to discover novel therapeutic options to control the disease with the recent emergence of drug resistance. The multifunctional protein, thioredoxin glutathione reductase (TGR), an essential enzyme for the survival of the pathogen in the redox environment has been actively explored as a potential drug target. The recent availability of small-molecule screening datasets against this target provides a unique opportunity to learn molecular properties and apply computational models for discovery of activities in large molecular libraries. Such a prioritisation approach could have the potential to reduce the cost of failures in lead discovery. A supervised learning approach was employed to develop a cost sensitive classification model to evaluate the biological activity of the molecules. Random forest was identified to be the best classifier among all the classifiers with an accuracy of around 80 percent. Independent analysis using a maximally occurring substructure analysis revealed 10 highly enriched scaffolds in the actives dataset and their docking against was also performed. We show that a combined approach of machine learning and other cheminformatics approaches such as substructure comparison and molecular docking is efficient to prioritise molecules from large molecular datasets.

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.

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.

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.

Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana.

BACKGROUND: Leishmaniasis is a neglected tropical disease which affects approx. 12 million individuals worldwide and caused by parasite Leishmania. The current drugs used in the treatment of Leishmaniasis are highly toxic and has seen widespread emergence of drug resistant strains which necessitates the need for the development of new therapeutic options. The high throughput screen data available has made it possible to generate computational predictive models which have the ability to assess the active scaffolds in a chemical library followed by its ADME/toxicity properties in the biological trials. RESULTS: In the present study, we have used publicly available, high-throughput screen datasets of chemical moieties which have been adjudged to target the pyruvate kinase enzyme of L. mexicana (LmPK). The machine learning approach was used to create computational models capable of predicting the biological activity of novel antileishmanial compounds. Further, we evaluated the molecules using the substructure based approach to identify the common substructures contributing to their activity. CONCLUSION: We generated computational models based on machine learning methods and evaluated the performance of these models based on various statistical figures of merit. Random forest based approach was determined to be the most sensitive, better accuracy as well as ROC. We further added a substructure based approach to analyze the molecules to identify potentially enriched substructures in the active dataset. We believe that the models developed in the present study would lead to reduction in cost and length of clinical studies and hence newer drugs would appear faster in the market providing better healthcare options to the patients.

Predictive modeling of anti-malarial molecules inhibiting apicoplast formation.

BACKGROUND: Malaria is a major healthcare problem worldwide resulting in an estimated 0.65 million deaths every year. It is caused by the members of the parasite genus Plasmodium. The current therapeutic options for malaria are limited to a few classes of molecules, and are fast shrinking due to the emergence of widespread resistance to drugs in the pathogen. The recent availability of high-throughput phenotypic screen datasets for antimalarial activity offers a possibility to create computational models for bioactivity based on chemical descriptors of molecules with potential to accelerate drug discovery for malaria. RESULTS: In the present study, we have used high-throughput screen datasets for the discovery of apicoplast inhibitors of the malarial pathogen as assayed from the delayed death response. We employed machine learning approach and developed computational predictive models to predict the biological activity of new antimalarial compounds. The molecules were further evaluated for common substructures using a Maximum Common Substructure (MCS) based approach. CONCLUSIONS: We created computational models using state-of-the-art machine learning algorithms. The models were evaluated based on multiple statistical criteria. We found Random Forest based approach provides for better accuracy as assessed from ROC curve analysis. We further evaluated the active molecules using a substructure based approach to identify common substructures enriched in the active set. We argue that the computational models generated could be effectively used to screen large molecular datasets to prioritize them for phenotypic screens, drastically reducing cost while improving the hit rate.

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.