An improved strategy for the crystallization of Leishmania mexicana pyruvate kinase

Experimental structure based drug design (SBDD) applications for anti-leishmanial drugs: A paradigm shift?

Leishmaniasis is a group of neglected tropical diseases caused by at least 20 species of Leishmania protozoa, which are spread by the bite of infected sandflies. There are three main forms of the disease: cutaneous leishmaniasis (CL, the most common), visceral leishmaniasis (VL, also known as kala-azar, the most serious), and mucocutaneous leishmaniasis. One billion people live in areas endemic to leishmaniasis, with an annual estimation of 30,000 new cases of VL and more than 1 million of CL. New treatments for leishmaniasis are an urgent need, as the existing ones are inefficient, toxic, and/or expensive. We have revised the experimental structure-based drug design (SBDD) efforts applied to the discovery of new drugs against leishmaniasis. We have grouped the explored targets according to the metabolic pathways they belong to, and the key achieved advances are highlighted and evaluated. In most cases, SBDD studies follow high-throughput screening campaigns and are secondary to pharmacokinetic optimization, due to the majoritarian belief that there are few validated targets for SBDD in leishmaniasis. However, some SBDD strategies have significantly contributed to new drug candidates against leishmaniasis and a bigger number holds promise for future development.

PYK-SubstitutionOME: an integrated database containing allosteric coupling, ligand affinity and mutational, structural, pathological, bioinformatic and computational information about pyruvate kinase isozymes

Interpreting changes in patient genomes, understanding how viruses evolve and engineering novel protein function all depend on accurately predicting the functional outcomes that arise from amino acid substitutions. To that end, the development of first-generation prediction algorithms was guided by historic experimental datasets. However, these datasets were heavily biased toward substitutions at positions that have not changed much throughout evolution (i.e. conserved). Although newer datasets include substitutions at positions that span a range of evolutionary conservation scores, these data are largely derived from assays that agglomerate multiple aspects of function. To facilitate predictions from the foundational chemical properties of proteins, large substitution databases with biochemical characterizations of function are needed. We report here a database derived from mutational, biochemical, bioinformatic, structural, pathological and computational studies of a highly studied protein family-pyruvate kinase (PYK). A centerpiece of this database is the biochemical characterization-including quantitative evaluation of allosteric regulation-of the changes that accompany substitutions at positions that sample the full conservation range observed in the PYK family. We have used these data to facilitate critical advances in the foundational studies of allosteric regulation and protein evolution and as rigorous benchmarks for testing protein predictions. We trust that the collected dataset will be useful for the broader scientific community in the further development of prediction algorithms. Database URL https://github.com/djparente/PYK-DB.

Atomic Details of Carbon-Based Nanomolecules Interacting with Proteins

Since the discovery of fullerene, carbon-based nanomolecules sparked a wealth of research across biological, medical and material sciences. Understanding the interactions of these materials with biological samples at the atomic level is crucial for improving the applications of nanomolecules and address safety aspects concerning their use in medicine. Protein crystallography provides the interface view between proteins and carbon-based nanomolecules. We review forefront structural studies of nanomolecules interacting with proteins and the mechanism underlying these interactions. We provide a systematic analysis of approaches used to select proteins interacting with carbon-based nanomolecules explored from the worldwide Protein Data Bank (wwPDB) and scientific literature. The analysis of van der Waals interactions from available data provides important aspects of interactions between proteins and nanomolecules with implications on functional consequences. Carbon-based nanomolecules modulate protein surface electrostatic and, by forming ordered clusters, could modify protein quaternary structures. Lessons learned from structural studies are exemplary and will guide new projects for bioimaging tools, tuning of intrinsically disordered proteins, and design assembly of precise hybrid materials.

Structure Elucidation of Helical Aromatic Foldamer-Protein Complexes with Large Contact Surface Areas

The development of large synthetic ligands could be useful to target the sizeable surface areas involved in protein-protein interactions. Herein, we present long helical aromatic oligoamide foldamers bearing proteinogenic side chains that cover up to 450 Ų of the human carbonic anhydrase II (HCA) surface. The foldamers are composed of aminoquinolinecarboxylic acids bearing proteinogenic side chains and of more flexible aminomethyl-pyridinecarboxylic acids that enhance helix handedness dynamics. Crystal structures of HCA-foldamer complexes were obtained with a 9- and a 14-mer both showing extensive protein-foldamer hydrophobic contacts. In addition, foldamer-foldamer interactions seem to be prevalent in the crystal packing, leading to the peculiar formation of an HCA superhelix wound around a rod of stacked foldamers. Solution studies confirm the positioning of the foldamer at the protein surface as well as a dimerization of the complexes.

The Potential of Secondary Metabolites from Plants as Drugs or Leads against Protozoan Neglected Diseases-Part III: In-Silico Molecular Docking Investigations

Malaria, leishmaniasis, Chagas disease, and human African trypanosomiasis continue to cause considerable suffering and death in developing countries. Current treatment options for these parasitic protozoal diseases generally have severe side effects, may be ineffective or unavailable, and resistance is emerging. There is a constant need to discover new chemotherapeutic agents for these parasitic infections, and natural products continue to serve as a potential source. This review presents molecular docking studies of potential phytochemicals that target key protein targets in Leishmania spp., Trypanosoma spp., and Plasmodium spp.

Protein Camouflage: Supramolecular Anion Recognition by Ubiquitin

Progress in the field of bio-supramolecular chemistry, the bottom-up assembly of protein-ligand systems, relies on a detailed knowledge of molecular recognition. To address this issue, we have characterised complex formation between human ubiquitin (HUb) and four supramolecular anions. The ligands were: pyrenetetrasulfonic acid (4PSA), p-sulfonato-calix[4]arene (SCLX4), bisphosphate tweezers (CLR01) and meso-tetrakis (4-sulfonatophenyl)porphyrin (TPPS), which vary in net charge, size, shape and hydrophobicity. All four ligands induced significant changes in the HSQC spectrum of HUb. Chemical shift perturbations and line-broadening effects were used to identify binding sites and to quantify affinities. Supporting data were obtained from docking simulations. It was found that these weakly interacting ligands bind to extensive surface patches on HUb. A comparison of the data suggests some general indicators for the protein-binding specificity of supramolecular anions. Differences in binding were observed between the cavity-containing and planar ligands. The former had a preference for the arginine-rich, flexible C terminus of HUb.

Molecular tweezers target a protein–protein interface and thereby modulate complex formation

Supramolecular binders select few residues on a protein surface and by their unique complexation mode disrupt a critical protein–protein interaction.

Selection of molecular targets for drug development against trypanosomatids

Trypanosomatid parasites are a group of flagellated protozoa that includes the genera Leishmania and Trypanosoma, which are the causative agents of diseases (leishmaniases, sleeping sickness and Chagas disease) that cause considerable morbidity and mortality, affecting more than 27 million people worldwide. Today no effective vaccines for the prevention of these diseases exist, whereas current chemotherapy is ineffective, mainly due to toxic side effects of current drugs and to the emergence of drug resistance and lack of cost effectiveness. For these reasons, rational drug design and the search of good candidate drug targets is of prime importance. The search for drug targets requires a multidisciplinary approach. To this end, the completion of the genome project of many trypanosomatid species gives a vast amount of new information that can be exploited for the identification of good drug candidates with a prediction of "druggability" and divergence from mammalian host proteins. In addition, an important aspect in the search for good drug targets is the "target identification" and evaluation in a biological pathway, as well as the essentiality of the gene in the mammalian stage of the parasite, which is provided by basic research and genetic and proteomic approaches. In this chapter we will discuss how these bioinformatic tools and experimental evaluations can be integrated for the selection of candidate drug targets, and give examples of metabolic and signaling pathways in the parasitic protozoa that can be exploited for rational drug design.

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.

The trypanocidal drug suramin and other trypan blue mimetics are inhibitors of pyruvate kinases and bind to the adenosine site

Ehrlich's pioneering chemotherapeutic experiments published in 1904 (Ehrlich, P., and Shiga, K. (1904) Berlin Klin. Wochenschrift 20, 329-362) described the efficacy of a series of dye molecules including trypan blue and trypan red to eliminate trypanosome infections in mice. The molecular structures of the dyes provided a starting point for the synthesis of suramin, which was developed and used as a trypanocidal drug in 1916 and is still in clinical use. Despite the biological importance of these dye-like molecules, the mode of action on trypanosomes has remained elusive. Here we present crystal structures of suramin and three related dyes in complex with pyruvate kinases from Leishmania mexicana or from Trypanosoma cruzi. The phenyl sulfonate groups of all four molecules (suramin, Ponceau S, acid blue 80, and benzothiazole-2,5-disulfonic acid) bind in the position of ADP/ATP at the active sites of the pyruvate kinases (PYKs). The binding positions in the two different trypanosomatid PYKs are nearly identical. We show that suramin competitively inhibits PYKs from humans (muscle, tumor, and liver isoenzymes, K(i) = 1.1-17 μM), T. cruzi (K(i) = 108 μM), and L. mexicana (K(i) = 116 μM), all of which have similar active sites. Synergistic effects were observed when examining suramin inhibition in the presence of an allosteric effector molecule, whereby IC(50) values decreased up to 2-fold for both trypanosomatid and human PYKs. These kinetic and structural analyses provide insight into the promiscuous inhibition observed for suramin and into the mode of action of the dye-like molecules used in Ehrlich's original experiments.

EDULISS: a small-molecule database with data-mining and pharmacophore searching capabilities

We present the relational database EDULISS (EDinburgh University Ligand Selection System), which stores structural, physicochemical and pharmacophoric properties of small molecules. The database comprises a collection of over 4 million commercially available compounds from 28 different suppliers. A user-friendly web-based interface for EDULISS (available at http://eduliss.bch.ed.ac.uk/) has been established providing a number of data-mining possibilities. For each compound a single 3D conformer is stored along with over 1600 calculated descriptor values (molecular properties). A very efficient method for unique compound recognition, especially for a large scale database, is demonstrated by making use of small subgroups of the descriptors. Many of the shape and distance descriptors are held as pre-calculated bit strings permitting fast and efficient similarity and pharmacophore searches which can be used to identify families of related compounds for biological testing. Two ligand searching applications are given to demonstrate how EDULISS can be used to extract families of molecules with selected structural and biophysical features.