A feature based pharmacophore for Candida albicans MyristoylCoA: protein N-myristoyltransferase inhibitors

Selective Bias Virtual Screening for Discovery of Promising Antimalarial Candidates targeting Plasmodium N-Myristoyltransferase

Malaria remains a significant public health challenge, with Plasmodium vivax being the species responsible for the most prevalent form of the disease. Given the limited therapeutic options available, the search for new antimalarials against P. vivax is urgent. This study aims to identify new inhibitors for P. vivax N-myristoyltransferase (PvNMT), an essential drug target against malaria. Through a validated virtual screening campaign, we prioritized 23 candidates for further testing. In the yeast NMT system, seven compounds exhibit a potential inhibitor phenotype. In vitro antimalarial phenotypic assays confirmed the activity of four candidates while demonstrating an absence of cytotoxicity. Enzymatic assays reveal LabMol-394 as the most promising inhibitor, displaying selectivity against the parasite and a strong correlation within the yeast system. Furthermore, molecular dynamics simulations shed some light into its binding mode. This study constitutes a substantial contribution to the exploration of a selective quinoline scaffold and provides valuable insights into the development of new antimalarial candidates.

Computational Drug Repurposing Resources and Approaches for Discovering Novel Antifungal Drugs against Candida albicans N-Myristoyl Transferase

Candida albicans is a yeast that is an opportunistic fungal pathogen and also identified as ubiquitous polymorphic species that is mainly linked with major fungal infections in humans, particularly in the immunocompromised patients including transplant recipients, chemotherapy patients, HIV-infected patients as well as in low-birth-weight infants. Systemic Candida infections have a high mortality rate of around 29 to 76%. For reducing its infection, limited drugs are existing such as caspofungin, fluconazole, terbinafine, and amphotericin B, etc. which contain unlikable side effects and also toxic. This review intends to utilize advanced bioinformatics technologies such as Molecular docking, Scaffold hopping, Virtual screening, Pharmacophore modeling, Molecular dynamics (MD) simulation for the development of potentially new drug candidates with a drug-repurpose approach against Candida albicans within a limited time frame and also cost reductive.

Pharmacophore modeling and 3D-QSAR studies of 2,4-disubstituted pyrimidine derivatives as Janus kinase 3 inhibitors

A robust pharmacophore model was developed and the structure-activity relationship was analyzed using 71 pyrimidine derivatives reported for covalent Janus Kinase 3 (JAK3) inhibition. Pharmacophore modeling developed a five featured pharmacophore: one H-bond acceptor, two H-bond donors, one hydrophobic, and one aromatic ring features. The atom-based three-dimensional QSAR models with statistical significance were generated using the training set of 52 compounds. The excellent predictive correlation coefficients were obtained for 3D models determined using a test set of 19 molecules. The generated QSAR model implies that the hydrophobic character is important for the JAK3 inhibitory activity of these compounds. Additionally, electron-withdrawing and hydrogen bond donor groups at specific positions positively contribute to the JAK3 inhibition potency. These results provided essential three-dimensional structural requirements and the crucial binding features of 2,4-disubstituted pyrimidine derivatives, which may direct for the design and discovery of novel potent JAK3 inhibitors.

Identification of Essential 2D and 3D Chemical Features for Discovery of the Novel Tubulin Polymerization Inhibitors

Background:

Tubulin polymerization inhibitors interfere with microtubule assembly and their functions lead to mitotic arrest, therefore they are attractive target for design and development of novel anticancer compounds.

Objective:

The proposed novel and effective structures following the use of three-dimensionalquantitative structure activity relationship (3D-QSAR) pharmacophore based virtual screening clearly demonstrate the high efficiency of this method in modern drug discovery.

Method:

Combined computational approach was applied to extract the essential 2D and 3D features requirements for higher activity as well as identify new anti-tubulin agents.

Results:

The best quantitative pharmacophore model, Hypo1, exhibited good correlation of 0.943 (RMSD=1.019) and excellent predictive power in the training set compounds. Generated model AHHHR, was well mapped to colchicine site and three-dimensional spatial arrangement of their features were in good agreement with the vital interactions in the active site. Total prediction accuracy (0.92 for training set and 0.86 for test set), enrichment factor (4.2 for training set and 4.5 for test set) and the area under the ROC curve (0.86 for training set and 0.94 for the test set), the developed model using Extended Class FingerPrints of maximum diameter 4 (ECFP_4) was chosen as the best model.

Conclusion:

Developed computational platform provided a better understanding of requirement features for colchicine site inhibitors and we believe the results of this study might be useful for the rational design and optimization of new inhibitors.

Virtual screening to identify Leishmania braziliensis N-myristoyltransferase inhibitors: pharmacophore models, docking, and molecular dynamics

Leishmaniasis is caused by several protozoa species belonging to genus Leishmania that are hosted by humans and other mammals. Millions of new cases are recorded every year and the drugs available on the market do not show satisfactory efficacy and safety. A hierarchical virtual screening approach based on the pharmacophore model, molecular docking, and molecular dynamics was conducted to identify possible Leishmania braziliensis N-misristoyltransferase (LbNMT) inhibitors. The adopted pharmacophore model had three main features: four hydrophobic centers, four hydrogen-bond acceptor atoms, and one positive nitrogen center. The molecules (n=15,000) were submitted to alignment with the pharmacophore model and only 27 molecules aligned to model. Six molecules were submitted to molecular docking, using receptor PDB ID 5A27. After docking, the ZINC35426134 was a top-ranked molecule (- 64.61 kcal/mol). The molecule ZINC35426134 shows hydrophobic interactions with Phe82, Tyr209, Val370, and Leu391 and hydrogen bonds with Asn159, Tyr318, and Val370. Molecular dynamics simulations were performed with the protein in its APO and HOLO forms for 37 ns in order to assess the stability of the protein-ligand complex. Results showed that the HOLO form was more stable than the APO one, and it suggests that the ZINC35426134 binding stabilizes the enzyme. Therefore, the selected molecule has the potential to meet the herein proposed target.

Exploration of potential EGFR inhibitors: a combination of pharmacophore-based virtual screening, atom-based 3D-QSAR and molecular docking analysis

Epidermal growth factor receptor (EGFR) protein tyrosine kinases are over expressed in several human cancers and considered as a promising target for developing novel anticancer drugs. In this study, the ligand-based pharmacophore mapping and atom-based 3D-QSAR approach was carried out on a series of 40 novel pyrrolo[3, 2-d]pyrimidine derivatives acting as EGFR inhibitors. The best pharmacophore hypothesis AAADRR.295 was selected and an atom-based 3D-QSAR model was generated by applying partial least-squares algorithm. The developed model was validated and used as a 3D query in sequential virtual screening study to filter five chemical databases. The obtained compounds were further filtered according to Lipinski rule of five and fitness score. Subsequently, a multistep molecular docking study was employed on the retrieved hits and finally, 12 compounds were prioritized as potential leads against EGFR, which exhibited high docking scores, correlated binding mode to experimentally proven compounds and constructive drug-like properties. The results of this study provide detailed structural insights and emphasize the important binding features of these compounds, which may assists in the design and development of novel EGFR inhibitors.

Pharmacophore modeling and docking studies on some nonpeptide-based caspase-3 inhibitors

Neurodegenerative disorders are major consequences of excessive apoptosis caused by a proteolytic enzyme known as caspase-3. Therefore, caspase-3 inhibition has become a validated therapeutic approach for neurodegenerative disorders. We performed pharmacophore modeling on some synthetic derivatives of caspase-3 inhibitors (pyrrolo[3,4-c]quinoline-1,3-diones) using PHASE 3.0. This resulted in the common pharmacophore hypothesis AAHRR.6 which might be responsible for the biological activity: two aromatic rings (R) mainly in the quinoline nucleus, one hydrophobic (H) group (CH₃), and two acceptor (A) groups (–C=O). After identifying a valid hypothesis, we also developed an atom-based 3D-QSAR model applying the PLS algorithm. The developed model was statistically robust (q² = 0.53; pred_r² = 0.80). Additionally, we have performed molecular docking studies, cross-validated our results, and gained a deeper insight into its molecular recognition process. Our developed model may serve as a query tool for future virtual screening and drug designing for this particular target.

Discovery of new antifungal leads via pharmacophore modeling and QSAR analysis of fungal N-myristoyl transferase inhibitors followed by in silico screening

N-Myristoyl transferase is an essential enzyme for fungal growth and survival. The continuous interest in the development of new antifungal agents prompted recent interest in developing new potent inhibitors of fungal N-myristoyl transferase. In this context, we combined pharmacophore and QSAR modeling to explore the structural requirements for potent N-myristoyl transferase inhibitors employing 55 known N-myristoyl transferase ligands. Four binding pharmacophore models emerged in the optimal QSAR equations (R(2)(44) = 0.81-0.83, F-statistic = 47.89-58.83, r(2)(L00)= 0.77-0.80, against 11 external test inhibitors = 0.61-0.71). The successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve profiles. The QSAR equations and their associated pharmacophore models were validated by the identification and experimental evaluation of new promising antifungal leads retrieved from the NCI database and our in-house-built database of established drugs and agrochemicals.

Discovery of new β-D-glucosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening

Glycosidases, including β-D-glucosidase, are involved in a variety of metabolic disorders such as diabetes, viral or bacterial infections and cancer. Accordingly, we were prompted to find new β-D-glucosidase inhibitors. Towards this end we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Three pharmacophores emerged in the QSAR equations, suggesting the existence of more than one binding mode accessible to ligands within the β-D-glucosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic (ROC) curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were established experimentally by the identification of several β-D-glucosidase inhibitors retrieved via in silico search of two structural databases, namely the National Cancer Institute (NCI) list of compounds, and our in-house structural database of established drugs and agrochemicals (DAC).

Discovery of new β-D-galactosidase inhibitors via pharmacophore modeling and QSAR analysis followed by in silico screening

Glycosidases, including β-D-galactosidase, are involved in a variety of metabolic disorders, such as diabetes, viral or bacterial infections, and cancer. Accordingly, we were prompted to find new β-D-galactosidase inhibitors. Towards this end, we scanned the pharmacophoric space of this enzyme using a set of 41 known inhibitors. Genetic algorithm and multiple linear regression analyses were used to select an optimal combination of pharmacophoric models and physicochemical descriptors to yield self-consistent and predictive quantitative structure-activity relationship (QSAR). Five pharmacophores emerged in the QSAR equations suggesting the existence of more than one binding mode accessible to ligands within β-D-galactosidase pocket. The successful pharmacophores were complemented with strict shape constraints in an attempt to optimize their receiver-operating characteristic curve profiles. The validity of the QSAR equations and the associated pharmacophoric models were experimentally established by the identification of several β-D-galactosidase inhibitors retrieved via in silico search of two structural databases: the National Cancer Institute list of compounds and our in house built structural database of established drugs and agrochemicals.

A Selective Pharmacophore Model for beta(2)-Adrenoceptor Agonists

β₂-Adrenoceptor selectivity is an important consideration in drug design in order to minimize the possibility of side effects. A selective pharmacophore model was developed based on a series of selective β₂-adrenoceptor agonists. The best pharmacophore hypothesis consisted of five chemical features (one hydrogen-bond acceptor, one hydrogen-bond donor, two ring aromatic and one positive ionizable feature). The result was nearly in accordance with the reported interactions between the β₂-adrenoceptor and agonists, and it shared enough similar features with the result of field point patterns by FieldTemplater, which mainly validated the pharmacophore model. Moreover, the pharmacophore could predict the selectivity over the β₁-adrenoceptor. These results might provide guidance for the rational design of novel potent and selective β₂-adrenoceptor agonists.

Pharmacophore generation and atom-based 3D-QSAR of novel 2-(4-methylsulfonylphenyl)pyrimidines as COX-2 inhibitors

Cyclooxygenase-2 (COX-2) inhibitors are widely used for the treatment of pain and inflammatory disorders such as rheumatoid arthritis and osteoarthritis. A series of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives has been reported as COX-2 inhibitors. In order to understand the structural requirement of these COX-2 inhibitors, a ligand-based pharmacophore and atom-based 3D-QSAR model have been developed. A five-point pharmacophore with four hydrogen bond acceptors (A) and one hydrogen bond donor (D) was obtained. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square (PLS) statistics results. The training set correlation is characterized by PLS factors (r (2) = 0.642, SD = 0.65, F = 82.7, P = 7.617 e - 12). The test set correlation is characterized by PLS factors (Q (2) (ext) = 0.841, RMSE = 0.24,Pearson-R = 0.91). A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Arg513, Val523, Phe518, Ser530, Tyr355, His90) of COX-2 enzyme. The results of ligand-based pharmacophore hypothesis and atom-based 3D-QSAR give detailed structural insights as well as highlights important binding features of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives as COX-2 inhibitors which can provide guidance for the rational design of novel potent COX-2 inhibitors.

Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening

Dipeptidyl peptidase IV (DPP IV) deactivates the natural hypoglycemic incretin hormones. Inhibition of this enzyme should restore glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, the pharmacophoric space of DPP IV was explored using a set of 358 known inhibitors. Thereafter, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that yield selfconsistent and predictive quantitative structure-activity relationships (QSAR) (r(2) (287)=0.74, F-statistic=44.5, r(2) (BS)=0.74, r(2) (LOO)=0.69, r(2) (PRESS) against 71 external testing inhibitors=0.51). Two orthogonal pharmacophores (of cross-correlation r(2)=0.23) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within the DPP IV binding pocket. Docking experiments supported the binding modes suggested by QSAR/pharmacophore analyses. The validity of the QSAR equation and the associated pharmacophore models were established by the identification of new low-micromolar anti-DPP IV leads retrieved by in silico screening. One of our interesting potent anti-DPP IV hits is the fluoroquinolone gemifloxacin (IC(50)=1.12 muM). The fact that gemifloxacin was recently reported to potently inhibit the prodiabetic target glycogen synthase kinase 3beta (GSK-3beta) suggests that gemifloxacin is an excellent lead for the development of novel dual antidiabetic inhibitors against DPP IV and GSK-3beta.

Pharmacophore modeling, quantitative structure-activity relationship analysis, and in silico screening reveal potent glycogen synthase kinase-3beta inhibitory activities for cimetidine, hydroxychloroquine, and gemifloxacin

The pharmacophoric space of glycogen synthase kinase-3beta (GSK-3beta) was explored using two diverse sets of inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combination of pharmacophores and physicochemical descriptors that access self-consistent and predictive quantitative structure-activity relationship (QSAR) against 132 training compounds ( r (2) 123 = 0.663, F = 24.6, r (2) LOO = 0.592, r (2) PRESS against 29 external test inhibitors = 0.695). Two orthogonal pharmacophores emerged in the QSAR, suggesting the existence of at least two distinct binding modes accessible to ligands within GSK-3beta binding pocket. The validity of the QSAR equation and the associated pharmacophores was established by the identification of three nanomolar GSK-3beta inhibitors retrieved from our in-house-built structural database of established drugs, namely, hydroxychloroquine, cimetidine, and gemifloxacin. Docking studies supported the binding modes suggested by the pharmacophore/QSAR analysis. In addition to being excellent leads for subsequent optimization, the anti-GSK-3beta activities of these drugs should have significant clinical implications.

Exploring QSTR and toxicophore of hERG K+ channel blockers using GFA and HypoGen techniques

Predictive quantitative structure-toxicity and toxicophore models were developed for a diverse series of hERG K+ channel blockers, acting as anti-arrhythmic agents using QSAR+ module in Cerius2 and HypoGen module in Catalyst software, respectively. The 2D-QSTR analysis has been performed on a dataset of 68 molecules carefully selected from literature for which IC50 values measured on hERG K+ channels expressed in mammalian cells lines using the voltage patch clamp assay technique were reported. Their biological data, expressed as IC50, spanned from 7.0nM to 1.4mM, with 7 orders difference. Several types of descriptors including electrotopological, thermodynamic, ADMET, graph theoretical (topological and information content) were used to derive a quantitative relationship between the channel blockers and its physico-chemical properties. Statistically significant QSTR model was obtained using genetic function approximation methodology, having seven descriptors, with a correlation coefficient (r2) of 0.837, cross-validated correlation coefficient (q2) of 0.776 and predictive correlation coefficient (r2 pred) of 0.701, indicating the robustness of the model. Toxicophore model generated using HypoGen module in Catalyst, on these datasets, showed three important features for hERG K+ channel blockers, (i) hydrophobic group (HP), (ii) ring aromatic group (RA) and (iii) hydrogen bond acceptor lipid group (HBAl). The most predictive hypothesis (Hypo 1), consisting of these three features had a best correlation coefficient of 0.820, a low rms deviation of 1.740, and a high cost difference of 113.50, which represents a true correlation and a good predictivity. The hypothesis, Hypo 1 was validated by a test set consisting of 12 molecules and by a cross-validation of 95% confidence level. Accordingly, our 2D-QSTR and toxicophore model has strong predictivity to identify structurally diverse hERG K+ channel blockers with desired biological activity. These models provide a useful framework for understanding binding, and gave structural insight into the specific protein-ligand interactions responsible for affinity, and how one might modify any given structure to mitigate binding.

Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening

A pharmacophoric model was developed for human protein tyrosine phosphatase 1B (h-PTP 1B) inhibitors utilizing the HipHop-REFINE module of CATALYST software. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of physicochemical descriptors and pharmacophore hypothesis that yield consistent QSAR equation of good predictive potential (r = 0.87,F-statistic = 69.13,r(BS)2 = 0.76,r(LOO)2 = 0.68). The validity of the QSAR equation and the associated pharmacophoric hypothesis was experimentally established by the identification of five new h-PTP 1B inhibitors retrieved from the National Cancer Institute (NCI) database.

Discovery of potent inhibitors of pseudomonal quorum sensing via pharmacophore modeling and in silico screening

HipHop-Refine was employed to derive a binding hypothesis for pseudomonal quorum sensing (QS) antagonists. The model was employed as 3D search query to screen the National Cancer Institute (NCI) database. One of the hits illustrated nanomolar QS inhibitory activity. The fact that this compound contained tetravalent lead (Pb) prompted us to evaluate the activities of phenyl mercuric nitrate and thimerosal, both fit the binding pharmacophore. The two mercurials illustrated nanomolar to low micromolar IC50 inhibitory values against pseudomonal QS. The three compounds represent a new class of QS inhibitors.

Current state of three-dimensional characterisation of antifungal targets and its use for molecular modelling in drug design

The alarming rise in life-threatening systemic fungal infections due to the emergence of drug-resistant fungal strains had produced an increased demand for new antimycotics, especially those targeting novel antifungal structures. Drug discovery has developed from screening natural products and chemical synthesis to a modern approach, namely structure-based drug design. Whilst many antifungal agents currently in use were discovered more than 30 years ago, characterisation of various drug targets has only been achieved recently, contributing immensely to understanding the structure-activity relationships of antifungals and their targets. Three-dimensional characterisation has become a well established tool for modern antifungal drug research and should play an important role in investigations for new antifungal agents.

Ligand-Based Molecular Modeling Study on a Chemically Diverse Series of Cholecystokinin-B/Gastrin Receptor Antagonists: Generation of Predictive Model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.

Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling

The flexibility of activated factor X (fXa) binding site was assessed employing ligand-based pharmacophor modeling combined with genetic algorithm (GA)-based QSAR modeling. Four training subsets of wide structural diversity were selected from a total of 199 direct fXa inhibitors and were employed to generate different fXa pharmacophoric hypotheses using CATALYST software over two subsequent stages. In the first stage, high quality binding models (hypotheses) were identified. However, in the second stage, these models were refined by applying variable feature weight analysis to assess the relative significance of their features in the ligand-target affinity. The binding models were validated according to their coverage (capacity as a three-dimensional (3D) database search queries) and predictive potential as three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Subsequently, GA and multiple linear regression (MLR) analysis were employed to construct different QSAR models from high quality pharmacophores and explore the statistical significance of combination models in explaining bioactivity variations across 199 fXa inhibitors. Three orthogonal pharmacophoric models emerged in the optimal QSAR equation suggesting they represent three binding modes accessible to ligands in the binding pocket within fXa.

Chemical Function Based Pharmacophore Generation of Endothelin-A Selective Receptor Antagonists

Both quantitative and qualitative chemical function based pharmacophore models of endothelin-A (ET(A)) selective receptor antagonists were generated by using the two algorithms HypoGen and HipHop, respectively, which are implemented in the Catalyst molecular modeling software. The input for HypoGen is a training set of 18 ET(A) antagonists exhibiting IC(50) values ranging between 0.19 nM and 67 microM. The best output hypothesis consists of five features: two hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI) function. The highest scoring Hip Hop model consists of six features: three hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI). It is the result of an input of three highly active, selective, and structurally diverse ET(A) antagonists. The predictive power of the quantitative model could be approved by using a test set of 30 compounds, whose activity values spread over 6 orders of magnitude. The two pharmacophores were tested according to their ability to extract known endothelin antagonists from the 3D molecular structure database of Derwent's World Drug Index. Thereby the main part of selective ET(A) antagonistic entries was detected by the two hypotheses. Furthermore, the pharmacophores were used to screen the Maybridge database. Six compounds were chosen from the output hit lists for in vitro testing of their ability to displace endothelin-1 from its receptor. Two of these are new potential lead compounds because they are structurally novel and exhibit satisfactory activity in the binding assay.

The genesis of high-throughput structure-based drug discovery using protein crystallography

Over the past 12 years, drugs have been developed using structure-based drug design relying upon traditional crystallographic methods. Established successes, such as the drugs designed against HIV-1 protease and neuraminidase, demonstrate the utility of a structure-based approach in the drug-discovery process. However, the approach has historically lacked throughput and reliability capabilities; these bottlenecks are being overcome by breakthroughs in high-throughput structural biology. Recent technological innovations such as submicroliter high-throughput crystallization, high-performance synchrotron beamlines and rapid binding-site analysis of de novo targets using virtual ligand screening and small molecule co-crystallization have resulted in a significant advance in structure-based drug discovery.

Antifungals targeted to protein modification: focus on protein N-myristoyltransferase

Invasive fungal infections have increased dramatically in recent years to become important causes of morbidity and mortality in hospitalised patients. Currently available antifungal drugs for such infections essentially have three molecular targets: 14 alpha demethylase (azoles), ergosterol (polyenes) and beta-1,3-glucan synthase (echinocandins). The first is a fungistatic target vulnerable to resistance development; the second, while a fungicidal target, is not sufficiently different from the host to ensure high selectivity; the third, a fungistatic (Aspergillus) or fungicidal (Candida) target, has limited activity spectrum (gaps: Cryptococcus, emerging fungi) and potential host toxicity that might preclude dose escalation. Drugs aimed at totally new targets are thus needed to increase our chemotherapeutic options and to forestall, alone or in combination chemotherapy, the emergence of drug resistance. Protein N-myristoylation, the cotranslational transfer of the 14-carbon saturated fatty acid myristate from CoA to the amino-terminal glycine of several fungal proteins such as the ADP-ribosylation factor (ARF), presents such an attractive new target. The reaction, catalysed by myristoyl-CoA:protein N-myristoyltransferase (NMT), is essential for viability, is biochemically tractable and has proven potential for selectivity. In the past five years, a number of selective inhibitors of the fungal enzyme, some with potent, broad spectrum antifungal activity, have been reported: myristate analogues, myristoylpeptide derivatives, histidine analogues (peptidomimetics), aminobenzothiazoles, quinolines and benzofurans. A major development has been the publication of the crystal structure of Candida albicans and Saccharomyces cerevisiae NMTs, which has allowed virtual docking of inhibitors on the enzyme and refinement of structure-activity relationships of lead compounds.

Aspergillus nidulans swoF encodes an N-myristoyl transferase

Polar growth is a fundamental process in filamentous fungi and is necessary for disease initiation in many pathogenic systems. Previously, swoF was identified in Aspergillus nidulans as a single-locus, temperature-sensitive (ts) mutant aberrant in both polarity establishment and polarity maintenance. The swoF gene was cloned by complementation of the ts phenotype and sequenced. The derived protein sequence had high identity with N-myristoyl transferases (NMTs) found in fungi, plants, and animals. In addition, wild-type growth at restrictive temperature was partially restored by the addition of myristic acid to the growth medium. Sequencing revealed that the mutation in swoF changes the conserved aspartic acid 369 to a tyrosine. The predicted A. nidulans SwoF protein, SwoFp, was homology modeled based on crystal structures of NMTs from Saccharomyces cerevisiae and Candida albicans. The D369Y swoF mutation is on the opposite face of the protein, distal to the myristoyl coenzyme A and peptide substrate binding sites. In wild-type NMTs, D369 appears to stabilize a structural beta-strand bend through two hydrogen bonds and an ionic interaction. These stabilizing bonds are abolished in the D369Y mutant. We hypothesize that a substrate of SwoFp must be myristoylated for proper polarity establishment and maintenance. The mutation prevents the proper function of SwoFp at restrictive temperature and thus blocks polar growth.

Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase

Pharmacophore hypotheses were developed for a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex (MAC) and human dihydrofolate reductase (hDHFR). Training sets consisting of 20 inhibitors were selected in each case on the basis of the information content of the structures and activity data as required by the HypoGen program in the Catalyst software. In the case of MAC DHFR inhibitors, the best pharmacophore in terms of statistics and predictive value consisted of four features: two hydrogen bond acceptors (HA), one hydrophobic (HY) feature, and one ring aromatic (RA) feature. The selected pharmacophore hypothesis yielded an rms deviation of 0.730 and a correlation coefficient of 0.967 with a cost difference (null cost minus total cost) of approximately 52. The pharmacophore was validated on a large set of test inhibitors. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. This classification scheme is more practical than actual estimated values because these values have no meaning for compounds yet to be tested except that they indicate whether the compounds will be active or inactive in a biological assay. For the training set, the success rate for predicting active and inactive compounds was 100%. For the test set, the success rate in predicting active compounds was greater than 92% while about 7% of the inactive compounds were predicted to be active. This successful prediction was further validated on three structurally diverse compounds active against MAC DHFR. Two compounds mapped well onto three of the four features of the pharmacophore. The third compound was mapped to all four features of the pharmacophore. This validation study provided confidence for the usefulness of the selected pharmacophore model to identify compounds with diverse structures from a database search. Comparison of pharmacophores for inhibitors of human and MAC DHFR is expected to reveal fundamental differences between these two pharmacophores that may be effectively exploited to identify and design compounds with high selectivity for MAC DHFR.