A search for sources of drug resistance by the 4D-QSAR analysis of a set of antimalarial dihydrofolate reductase inhibitors

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R², S², and R²cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q² (0.503), R² (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.

Discovery of Traditional Chinese Medicine Derived Compounds as Wild Type and Mutant Plasmodium falciparum Dihydrofolate Reductase Inhibitors: Induced Fit Docking and ADME Studies

BACKGROUND

In a bid to come up with effective compounds as inhibitors for antimalarial treatment, we built a library of 2,000 traditional Chinese medicine(TCM)-derived compounds retrieved from TCM Database@Taiwan.

METHODS

The active sites of both the wild type and mutant Plasmodium falciparum dihydrofolatereductase (pfDHFR) were explored using computational tools. pfDHFR, one of the prime drug targets in the prevention of malaria infection induced by the female anopheles mosquito has continued to offer resistance to drugs (antifolates) due to mutation in some of the key amino acid residues crucial for its inhibition.

RESULTS

We utilized virtual throughput screening and glide XP docking to screen the compounds, and 8 compounds were found to have promising docking scores with both the wild type and mutant pfDHFR. They were further subjected to Induce Fit Docking (IFD) to affirm their inhibitory potency. The ADME properties and biological activity spectrum of the compounds were also considered. The inhibition profile of the compounds revealed that a number of compounds formed intermolecular interactions with ASP54, ILE14, LEU164, SER108/ASN108, ARG122 and ASP58. Most of the compounds can be considered as drug candidates due to their antiprotozoal activities and accordance with the Lipinski's Rule of Five (ROF).

CONCLUSION

The outcome of the present study should further be investigated to attest the efficacy of these compounds as better drug candidates than the antifolates.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Exploring PfDHFR reaction surface: A combined molecular dynamics and QM/MM analysis

The substrate to the enzyme PfDHFR (Plasmodium falciparum Dihydrofolate Reductase) is a small molecule dihydrofolate (DHF), it gets converted to tetrahydrofolate (THF) in the active site of the enzyme. The PfDHFR reaction surface involves the protonation of DHF to DHFP as an initial step before the catalytic conversion. The binding affinities of all these species (DHF, DHFP and THF) contribute to the mechanism of DHFR catalytic action. Molecular dynamics (MD) simulations and Quantum Mechanics/Molecular Mechanics (QM/MM) analysis were performed to evaluate the binding affinity and molecular recognition interactions of the substrate DHF/DHFP and the product THF, in the active site of wild-type PfDHFR (wtPfDHFR). The binding affinities of the cofactor NADPH/NADP⁺ were also estimated in all the three complexes. The molecular dynamics (MD) simulations of the substrate, product and cofactor in the cavities of wtPfDHFR revealed the variation of the atomic level interactions during the course of the catalytic conversion. It was found that the DHFP binds very strongly to the PfDHFR active site and pulls the cofactor NADPH closer to itself. The QM/MM analysis revealed that the binding energy of DHFP (-59.82 kcal/mol) and NADPH (-100.24 kcal/mol) in DHFP-wtPfDHFR complex, is higher in comparison to the binding energy of DHF (-38.67 kcal/mol) and NADPH (-77.53 kcal/mol) in DHF-wtPfDHFR complex and the binding energy of THF (-30.72 kcal/mol) and NADP⁺ (-73.72 kcal/mol) in THF-wtPfDHFR complex. The hydride ion donor-acceptor distance (DAD) analysis was also carried out. This combined MD and QM/MM analysis revealed that the protonation of DHF increases the proximity between the substrate and the cofactor, thus facilitates the reaction profile of PfDHFR.

Multidimensional (3D/4D-QSAR) probability-guided pharmacophore mapping: investigation of activity profile for a series of drug absorption promoters

A hybrid approach that combines 3D and 4D-QSAR methods based on grid and neural paradigms with automated IVE-PLS procedure was examined to identify the pharmacophore pattern for cholic acid derivatives as potential drug absorption promoters.

Machine learning methods for property prediction in chemoinformatics: Quo Vadis?

This paper is focused on modern approaches to machine learning, most of which are as yet used infrequently or not at all in chemoinformatics. Machine learning methods are characterized in terms of the "modes of statistical inference" and "modeling levels" nomenclature and by considering different facets of the modeling with respect to input/ouput matching, data types, models duality, and models inference. Particular attention is paid to new approaches and concepts that may provide efficient solutions of common problems in chemoinformatics: improvement of predictive performance of structure-property (activity) models, generation of structures possessing desirable properties, model applicability domain, modeling of properties with functional endpoints (e.g., phase diagrams and dose-response curves), and accounting for multiple molecular species (e.g., conformers or tautomers).

Binding modes of 2,4-diaminoquinazoline and 2,4-diaminopteridine analogs to P. falciparum dihydrofolate reductase enzyme: Molecular docking studies

A molecular docking study was carried out on 28 compounds belonging to 2,4-diaminoquinazoline and 2,4-diaminopteridine analogs using Glide, FlexX and GOLD programs and the X-ray crystallographic structures of the quadruple mutant (1J3K:pdb) and wild type (1J3I:pdb) Plasmodium falciparum dihydrofolate reductase enzyme. The experimental conformation the bound ligand WR99210 was precisely reproduced by the docking procedures as demonstrated by low (<2.00 Å) root-mean-square deviations. The results indicated that most of the compounds dock into the active sites of both the wild type and quadruple mutant P. falciparum dihydrofolate reductase enzymes. Visual inspection of the binding modes also demonstrated that most of the compounds could form H-bond interactions with the key amino acid residues (Asp54, Ile14 and Leu/Ile164) and with better docking scores than the bound compound (5). Their long side chains orient in the hydrophobic portion of the active site which is occupied by trichloro aryloxy side chain of WR99210 (5). Thus, avoid potential steric clashes with Asn108 (mutated from Ser108). Such a clash is known to be responsible for the resistance of the P. falciparum to pyrimethamine and cycloguanil.

Quantitative structure-activity relationships for cycloguanil analogs as PfDHFR inhibitors using mathematical molecular descriptors

Computed molecular descriptors were used to develop quantitative structure-activity relationships (QSARs) for binding affinities (K(i)) for a set of 58 cycloguanil (2,4-diamino-1,6-dihydro-1,3,5-triazine) analogues for dihydrofolate reductase (DHFR) enzyme extracted from wild and A16V+S108T mutant type (a double mutation) malaria parasite Plasmodium falciparum (Pf). High-quality models were obtained in both cases. The results of statistical analyses show that ridge regression (RR) outperformed the two other modelling methods, principal component regression (PCR) and partial least squares (PLS). For both enzymes, recognition of the inhibitors was based on four broad categories of descriptors encoding information on: (1) the electronic character of the various atoms in the molecule, (2) the size and shape of the structure, (3) the degree of branching in the molecular skeleton, and (4) two to five atom molecular fragments with aliphatic carbon at one end and aliphatic or aromatic carbon or nitrogen at the other end. The subsets of influential descriptors underlying the QSARs for the wild versus the mutant DHFR are quite non-overlapping. This indicates that the two enzymes recognize the inhibitor molecules on the basis of mutually distinct structural attributes. Such differential QSARs can be useful in the design of novel drugs active against malaria parasites which are growing in resistant to existing chemotherapeutic agents.

Structure-activity relationship and comparative docking studies for cycloguanil analogs as PfDHFR-TS inhibitors

Drug resistance acquired by Plasmodium falciparum (Pf) is a major problem in the treatment and control of malaria. One of the major examples of drug resistance is that caused by mutations in the active site of dihydrofolate reductase (DHFR) of Pf (PfDHFR-TS). A double mutation, A16V+S108T, is specific for resistance to the marketed drug cycloguanil. In this study, we used 58 cycloguanil (2,4-diamino-1,6-dihydro-1,3,5-triazine) derivatives to explore the relationship between various physicochemical properties and reported binding affinity data on wild-type and mutant-type A16V+S108T. Using the Hansch 2D-quantitative structure-activity relationship method, we obtained a parabolic relationship of hydrophobicity of substituents at the N1-phenyl ring with the wild-type binding affinity data. Hydrophobicity being a key property for wild-type binding affinity data, we found steric factors to be crucial for A16V+S108T mutant resistance. We investigated FlexX, GOLD, Glide and Molegro virtual docking programs and 13 different scoring functions on 10 of the cycloguanil derivatives to evaluate which program was best for reproducing the experimental binding mode and correlating the docking scores with the reported binding affinity data. We identified GOLD, using its GoldScore fitness function, as the most accurate docking program for predicting binding affinity data of cycloguanil derivatives to DHFR and Molegro virtual docker, with its template docking algorithm and MolDock [GRID] scoring function, as most accurate for reproducing the experimental binding mode of a reference ligand that is structurally similar to the cycloguanil derivatives studied. We also report an interaction index which best describes the structure-activity relationships exhibited by these analogs in terms of PfDHFR-TS active site interactions.

Construction of 4D-QSAR models for use in the design of novel p38-MAPK inhibitors

The p38-mitogen-activated protein kinase (p38-MAPK) plays a key role in lipopolysaccharide-induced tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1) release during the inflammatory process, emerging as an attractive target for new anti-inflammatory agents. Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis [Hopfinger et al., J. Am. Chem. Soc., 119 (1997) 10509] was applied to a series of 33 (a training set of 28 and a test set of 5) pyridinyl-imidazole and pyrimidinyl-imidazole inhibitors of p38-MAPK, with IC50 ranging from 0.11 to 2100 nM [Liverton et al., J. Med. Chem., 42 (1999) 2180]. Five thousand conformations of each analogue were sampled from a molecular dynamics simulation (MDS) during 50 ps at a constant temperature of 303 K. Each conformation was placed in a 2 angstroms grid cell lattice for each of three trial alignments. 4D-QSAR models were constructed by genetic algorithm (GA) optimization and partial least squares (PLS) fitting, and evaluated by leave-one-out cross-validation technique. In the best models, with three to six terms, the adjusted cross-validated squared correlation coefficients, Q2adj, ranged from 0.67 to 0.85. Model D (Q2adj = 0.84) was identified as the most robust model from alignment 1, and it is representative of the other best models. This model encompasses new molecular regions as containing pharmacophore sites, such as the amino-benzyl moiety of pyrimidine analogs and the N1-substituent in the imidazole ring. These regions of the ligands should be further explored to identify better anti-inflammatory inhibitors of p38-MAPK.

Synthesis of Non-Nucleosides: 7- and 1,3-Substituents of New Pyrrolo[2,3-d]pyrimidin-4-ones on Antiviral Activity

A series of non-nucleosides 9-47 were synthesized. Compounds 1-4 were reacted with formic acid (85%) to afford compounds 5-8. Then, the latter compounds were reacted with alkyl halides a-f (2-bromopropane, 2-bromobutane, benzyl bromide, benzyl chloromethyl ether, chloromethyl ethyl ether, phenacyl bromide) in the presence of NaH in dry DMF to give the desired compounds 9-47, which were evaluated for activity against herpes simplex virus type-II (HSV-II).

Non-stochastic and stochastic linear indices of the ‘molecular pseudograph’s atom adjacency matrix’: application to ‘in silico’ studies for the rational discovery of new antimalarial compounds

Malaria is one of the most deadly diseases, affecting million of people especially in developing countries. Because of the rapidly increasing threat worldwide of malaria epidemics multidrugs resistant to therapies, there is an urgent global need to discover new classes of antimalarial compounds. In an effort to overcome this problem, we have investigated the use of structure-based classification models for the 'rational' selection/identification or design/optimization of new lead antimalarials from virtual combinatorial data sets. In this sense, TOpological MOlecular COMputer Design strategy (TOMOCOMD approach) has been introduced in order to obtain two quantitative models for the discrimination of antimalarials. A collected data set containing 597 antimalarial compounds is presented as a helpful tool not only for theoretical chemist but for other researchers in this area. The validated models (including non-stochastic and stochastic indices) classify correctly more than 90% of compounds in both training and external prediction data sets. They showed high Matthews' correlation coefficients; 0.87 and 0.82 for training and 0.86 and 0.79 for test set. The TOMOCOMD-CARDD approach implemented in this work was successfully compared with two of the most useful models for antimalarials selection reported so far. Thus we expect that these two QSAR models can be used in the identification of previously un-known antimalarials compounds.

Importance of substrate and cofactor polarization in the active site of dihydrofolate reductase

By using a combined quantum-mechanical and molecular-mechanical potential in molecular dynamics simulations, we have investigated the effects of the enzyme electric field of dihydrofolate reductase on the electronic polarization of its 5-protonated dihydrofolate substrate at various stages of the catalyzed hydride transfer reaction. Energy decomposition of the total electrostatic interaction energy between the ligands and the enzyme shows that the polarization effect is 4% of the total electrostatic interaction energy, and, significantly, it accounts for 9kcal/mol of transition state stabilization relative to the reactant state. Therefore it is essential to take account of substrate polarization for quantitative interpretation of enzymatic function and for calculation of binding free energies of inhibitors to a protein. Atomic polarizations are calculated as the differences in the average atomic charges on the atoms in gas phase and in molecular simulations of the enzyme; this analysis shows that the glutamate tail and the pterin ring are the highly polarized regions of the substrate. Electron density difference plots of the reactant and product complexes at instantaneous configurations in the enzyme active center confirm the inferences made on the basis of partial atomic charges.

Mutational analysis of Plasmodium falciparum dihydrofolate reductase: the role of aspartate 54 and phenylalanine 223 on catalytic activity and antifolate binding

The catalytic activity and ability to confer resistance to antifolates of Plasmodium falciparum dihydrofolate reductase (pfDHFR) through single and double mutations at Asp-54 and Phe-223 were investigated. A single Asp54Glu (D54E) mutation in the pfDHFR domain greatly decreased the catalytic activity of the enzyme and affected both the K(m) values for the substrate dihydrofolate and the K(i) values for pyrimethamine, cycloguanil and WR99210. The Phe223Ser (F223S) single mutant had unperturbed kinetics but had very poor affinity with the first two antifolates. The ability to confer high resistance to the antifolates of F223S enzyme was, however, abolished in the D54E+F223S double mutant enzyme. When D54E mutation was present together with the A16V+S108T double mutation, the effects on the K(m) values for the substrate dihydrofolate and the binding affinity of antifolates were much more pronounced. The severely impaired kinetics and poor activity observed in A16V+S108T+D54E enzyme could, however, be restored when F223S was introduced, while the binding affinities to the antifolates remained poor. The experimental findings can be explained with a model for substrate and inhibitor binding. Our data not only indicate the importance of Asp-54 of pfDHFR in catalysis and inhibitor binding, but also provide evidence that infer the potentially crucial function of the C-terminal portion of pfDHFR domain.

Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors

Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor binding models for a set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines. The molecular target ('receptor') used was a 3D-homology model of a specific mutant type of Plasmodium falciparum (Pf) dihydrofolate reductase (DHFR). The dependent variable of the 3D-QSAR models is the IC50 inhibition constant for the specific mutant type of PfDHFR. The independent variables of the 3D-QSAR models (the descriptors) are scaled energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model and a collection of 2D-QSAR descriptors often used in QSAR studies. Multiple temperature molecular dynamics simulation (MDS) and the genetic function approximation (GFA) were employed using partial least square (PLS) and multidimensional linear regressions as the fitting functions to develop FEFF 3D-QSAR models for the binding process. The significant FEFF energy terms in the best 3D-QSAR models include energy contributions of the direct ligand-receptor interaction. Some changes in conformational energy terms of the ligand due to binding to the enzyme are also found to be important descriptors. The FEFF 3D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the PfDHFR to current antimalarials. The FEFF 3D-QSAR models are also compared to receptor-independent (RI) 4D-QSAR models developed in an earlier study and subsequently refined using recently developed generalized alignment rules.