Shape- and Chemical Feature-Based 3D-Pharmacophore Model Generation and Virtual Screening: Identification of Potential Leads forP. falciparumDHFR Enzyme Inhibition

Design, synthesis and biological evaluation of 4-aminoquinoline-guanylthiourea derivatives as antimalarial agents

Guanylthiourea (GTU) has been identified as an important antifolate antimalarial pharmacophore unit, whereas, 4-amino quinolones are already known for antimalarial activity. In the present work molecules carrying 4-aminoquinoline and GTU moiety have been designed using molecular docking analysis with PfDHFR enzyme and heme unit. The docking results indicated that the necessary interactions (Asp54 and Ile14) and docking score (-9.63 to -7.36 kcal/mmol) were comparable to WR99210 (-9.89 kcal/mol). From these results nine molecules were selected for synthesis. In vitro analysis of these synthesized compounds reveal that out of the nine molecules, eight show antimalarial activity in the range of 0.61-7.55 μM for PfD6 strain and 0.43-8.04 μM for PfW2 strain. Further, molecular dynamics simulations were performed on the most active molecule to establish comparative binding interactions of these compounds and reference ligand with Plasmodium falciparum dihydrofolate reductase (PfDHFR).

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.

Molecular dynamics of interactions between rigid and flexible antifolates and dihydrofolate reductase from pyrimethamine-sensitive and pyrimethamine-resistant Plasmodium falciparum

Currently, the usefulness of antimalarials such as pyrimethamine (PYR) is drastically reduced due to the emergence of resistant Plasmodium falciparum (Pf) caused by its dihydrofolate reductase (PfDHFR) mutations, especially the quadruple N51I/C59R/S108N/I164L mutations. The resistance was due to the steric conflict of PYR with S108N. WR99210 (WR), a dihydrotriazine antifolate with a flexible side chain that can avoid such conflict, can overcome this resistance through tight binding with the mutant. To understand factors contributing to different binding affinities of PYR/WR to the wild type (WT) and quadruple mutant (QM), we performed simulations on WR-WT, WR-QM, PYR-WT, and PYR-QM complexes and found that Ile14 and Asp54 were crucial for PYR/WR binding to PfDHFR due to strong hydrogen bonds. The quadruple mutations cause PYR to form, on average, fewer hydrogen bonds with Ile14 and Leu164, and to be displaced from its optimal orientation for Asp54 interaction. The predicted binding affinity ranking (WR-QM ≈ WR-WT ≈ PYR-WT >> PYR-QM) reasonably agrees with the inhibition constant (K(i)) ranking. Our results reveal important residues for tight binding of PYR/WR to WT/QM, which may be used to evaluate the inhibition effectiveness of antimalarials and to provide fundamental information for designing new drugs effective against drug-resistant P. falciparum.

Identification of SRC as a potent drug target for asthma, using an integrative approach of protein interactome analysis and in silico drug discovery

Network-biology inspired modeling of interactome data and computational chemistry have the potential to revolutionize drug discovery by complementing conventional methods. We consider asthma, a complex disease characterized by intricate molecular mechanisms, for our study. We aim to integrate prediction of potent drug targets using graph-theoretical methods and subsequent identification of small molecules capable of modulating activity of the best target. In this work, we construct the protein interactome underlying this disease: Asthma Protein Interactome (API). Using a strategy based on network analysis of the interactome, we identify a set of potential drug targets for asthma. Topologically and dynamically, v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC) emerges as the most central target in API. SRC is known to play an important role in promoting airway smooth muscle cell growth and facilitating migration in airway remodeling. From interactome analysis, and with the reported role in respiratory mechanisms, SRC emerges as a promising drug target for asthma. Further, we proceed to identify leads for SRC from a public database of small molecules. We predict two potential leads for SRC using ligand-based virtual screening methodology.

Discovery of potent, novel, non-toxic anti-malarial compounds via quantum modelling, virtual screening and in vitro experimental validation

Background

Developing resistance towards existing anti-malarial therapies emphasize the urgent need for new therapeutic options. Additionally, many malaria drugs in use today have high toxicity and low therapeutic indices. Gradient Biomodeling, LLC has developed a quantum-model search technology that uses quantum similarity and does not depend explicitly on chemical structure, as molecules are rigorously described in fundamental quantum attributes related to individual pharmacological properties. Therapeutic activity, as well as toxicity and other essential properties can be analysed and optimized simultaneously, independently of one another. Such methodology is suitable for a search of novel, non-toxic, active anti-malarial compounds.

Methods

A set of innovative algorithms is used for the fast calculation and interpretation of electron-density attributes of molecular structures at the quantum level for rapid discovery of prospective pharmaceuticals. Potency and efficacy, as well as additional physicochemical, metabolic, pharmacokinetic, safety, permeability and other properties were characterized by the procedure. Once quantum models are developed and experimentally validated, the methodology provides a straightforward implementation for lead discovery, compound optimizzation and de novo molecular design.

Results

Starting with a diverse training set of 26 well-known anti-malarial agents combined with 1730 moderately active and inactive molecules, novel compounds that have strong anti-malarial activity, low cytotoxicity and structural dissimilarity from the training set were discovered and experimentally validated. Twelve compounds were identified in silico and tested in vitro; eight of them showed anti-malarial activity (IC50 ≤ 10 μM), with six being very effective (IC50 ≤ 1 μM), and four exhibiting low nanomolar potency. The most active compounds were also tested for mammalian cytotoxicity and found to be non-toxic, with a therapeutic index of more than 6,900 for the most active compound.

Conclusions

Gradient's metric modelling approach and electron-density molecular representations can be powerful tools in the discovery and design of novel anti-malarial compounds. Since the quantum models are agnostic of the particular biological target, the technology can account for different mechanisms of action and be used for de novo design of small molecules with activity against not only the asexual phase of the malaria parasite, but also against the liver stage of the parasite development, which may lead to true causal prophylaxis.

Identification of new antimalarial leads by use of virtual screening against cytochrome bc₁

Cytochrome bc(1) is a validated drug target in malaria parasites. The spread of Plasmodium falciparum strains resistant to multiple antimalarials emphasizes the urgent need for new drugs. We screened in silico the ZINC and MOE databases, using ligand- and structure-based approaches, to identify new leads for development. The most active compound presented an IC(50) value against cultured P. falciparum of 2 μM and a docking pose consistent with its activity.

Antimalarial drugs: modes of action and mechanisms of parasite resistance

Malaria represents one of the most serious threats to human health worldwide, and preventing and curing this parasitic disease still depends predominantly on the administration of a small number of drugs whose efficacy is continually threatened and eroded by the emergence of drug-resistant parasite populations. This has an enormous impact on the mortality and morbidity resulting from malaria infection, especially in sub-Saharan Africa, where the lethal human parasite species Plasmodium falciparum accounts for approximately 90% of deaths recorded globally. Successful treatment of uncomplicated malaria is now highly dependent on artemisinin-based combination therapies. However, the first cases of artemisinin-resistant field isolates have been reported recently and potential replacement antimalarials are only in the developmental stages. Here, we summarize recent progress in tackling the problem of parasite resistance and discuss the underlying molecular mechanisms that confer resistance to current antimalarial agents as far as they are known, understanding of which should assist in the rational development of new drugs and the more effective deployment of older ones.

Identification of attractive drug targets in neglected-disease pathogens using an in silico approach

Background

The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.

Methodology/Principal Findings

Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.

Conclusions/Significance

Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

In cell-based drug development, researchers attempt to create drugs that kill a pathogen without necessarily understanding the details of how the drugs work. In contrast, target-based drug development entails the search for compounds that act on a specific intracellular target—often a protein known or suspected to be required for survival of the pathogen. The latter approach to drug development has been facilitated greatly by the sequencing of many pathogen genomes and the incorporation of genome data into user-friendly databases. The present paper shows how the database TDRtargets.org can identify proteins that might be considered good drug targets for diseases such as African sleeping sickness, Chagas disease, parasitic worm infections, tuberculosis, and malaria. These proteins may score highly in searches of the database because they are dissimilar to human proteins, are structurally similar to other “druggable” proteins, have functions that are easy to measure, and/or fulfill other criteria. Researchers can use the lists of high-scoring proteins as a basis for deciding which potential drug targets to pursue experimentally.