Aurijit Sarkar

Hydrophobicity - Shake Flasks, Protein Folding and Drug Discovery

Hydrophobic interactions are some of the most important interactions in nature. They are the primary driving force in a number of phenomena. This is mostly an entropic effect and can account for a number of biophysical events such as protein-protein or protein-ligand binding that are of immense importance in drug design. The earliest studies on this phenomenon can be dated back to the end of the 19(th) century when Meyer and Overton independently correlated the hydrophobic nature of gases to their anesthetic potency. Since then, significant progress has been made in this realm of science. This review briefly traces the history of hydrophobicity research along with the theoretical estimation of partition coefficients. Finally, the application of hydrophobicity estimation methods in the field of drug design and protein folding is discussed.

DrugPred: A Structure-Based Approach To Predict Protein Druggability Developed Using an Extensive Nonredundant Data Set

Judging if a protein is able to bind orally available molecules with high affinity, i.e. if a protein is druggable, is an important step in target assessment. In order to derive a structure-based method to predict protein druggability, a comprehensive, nonredundant data set containing crystal structures of 71 druggable and 44 less druggable proteins was compiled by literature search and data mining. This data set was subsequently used to train a structure-based druggability predictor (DrugPred) using partial least-squares projection to latent structures discriminant analysis (PLS-DA). DrugPred performed well in discriminating druggable from less druggable binding sites for both internal and external predictions. The method is robust against conformational changes in the binding site and outperforms previously published methods. The superior performance of DrugPred is likely due to the size and composition of the training set which, in contrast to most previously developed methods, only contains cavities that have evolved to bind a natural ligand.

Premature Activation of the Paramyxovirus Fusion Protein before Target Cell Attachment with Corruption of the Viral Fusion Machinery

Paramyxoviruses, including the childhood pathogen human parainfluenza virus type 3, enter host cells by fusion of the viral and target cell membranes. This fusion results from the concerted action of its two envelope glycoproteins, the hemagglutinin-neuraminidase (HN) and the fusion protein (F). The receptor-bound HN triggers F to undergo conformational changes that render it competent to mediate fusion of the viral and cellular membranes. We proposed that, if the fusion process could be activated prematurely before the virion reaches the target host cell, infection could be prevented. We identified a small molecule that inhibits paramyxovirus entry into target cells and prevents infection. We show here that this compound works by an interaction with HN that results in F-activation prior to receptor binding. The fusion process is thereby prematurely activated, preventing fusion of the viral membrane with target cells and precluding viral entry. This first evidence that activation of a paramyxovirus F can be specifically induced before the virus contacts its target cell suggests a new strategy with broad implications for the design of antiviral agents.

Structure–activity relationship (SAR) studies of 3-(2-amino-ethyl)-5-(4-ethoxy-benzylidene)-thiazolidine-2,4-dione: Development of potential substrate-specific ERK1/2 inhibitors

A series of analogs of 3-(2-amino-ethyl)-5-(4-ethoxy-benzylidene)-thiazolidine-2,4-dione, a putative substrate-specific ERK1/2 inhibitor, were synthesized and biologically characterized in human leukemia U937 cells to define its pharmacophore. It was discovered that shift of ethoxy substitution from the 4- to the 2-position on the phenyl ring significantly improved functional activities of inhibiting cell proliferation and inducing apoptosis. This may provide access to a new lead for developing ERK1/2 substrate-specific inhibitors.

A Simple Method for Discovering Druggable, Specific Glycosaminoglycan-Protein Systems. Elucidation of Key Principles from Heparin/Heparan Sulfate-Binding Proteins

Glycosaminoglycans (GAGs) affect human physiology and pathology by modulating more than 500 proteins. GAG-protein interactions are generally assumed to be ionic and nonspecific, but specific interactions do exist. Here, we present a simple method to identify the GAG-binding site (GBS) on proteins that in turn helps predict high specific GAG–protein systems. Contrary to contemporary thinking, we found that the electrostatic potential at basic arginine and lysine residues neither identifies the GBS consistently, nor its specificity. GBSs are better identified by considering the potential at neutral hydrogen bond donors such as asparagine or glutamine sidechains. Our studies also reveal that an unusual constellation of ionic and non-ionic residues in the binding site leads to specificity. Nature engineers the local environment of Asn45 of antithrombin, Gln255 of 3-O-sulfotransferase 3, Gln163 and Asn167 of 3-O-sulfotransferase 1 and Asn27 of basic fibroblast growth factor in the respective GBSs to induce specificity. Such residues are distinct from other uncharged residues on the same protein structure in possessing a significantly higher electrostatic potential, resultant from the local topology. In contrast, uncharged residues on nonspecific GBSs such as thrombin and serum albumin possess a diffuse spread of electrostatic potential. Our findings also contradict the paradigm that GAG-binding sites are simply a collection of contiguous Arg/Lys residues. Our work demonstrates the basis for discovering specifically interacting and druggable GAG-protein systems based on the structure of protein alone, without requiring access to any structure-function relationship data.

Allosteric inhibition of human factor XIa: discovery of monosulfated benzofurans as a class of promising inhibitors.

Factor XIa (fXIa) is being recognized as a prime target for developing safer anticoagulants. To discover synthetic, small, allosteric inhibitors of fXIa, we screened an in-house, unique library of 65 molecules displaying many distinct scaffolds and varying levels of sulfation. Of these, monosulfated benzofurans were the only group of molecules found to inhibit fXIa (∼100% efficacy) and led to the identification of monosulfated trimer 24 (IC50 0.82 μM) as the most potent inhibitor. Michaelis–Menten kinetics studies revealed a classic noncompetitive mechanism of action for 24. Although monosulfated, the inhibitors did not compete with unfractionated heparin alluding to a novel site of interaction. Fluorescence quenching studies indicated that trimer 24 induces major conformational changes in the active site of fXIa. Docking studies identified a site near Lys255 on the A3 domain of fXIa as the most probable site of binding for 24. Factor XIa devoid of the A3 domain displayed a major defect in the inhibition potency of 24 supporting the docking prediction. Our work presents the sulfated benzofuran scaffold as a promising framework to develop allosteric fXIa inhibitors that likely function through the A3 domain.

Allosteric competitive inhibitors of the glucose-1-phosphate thymidylyltransferase (RmlA) from Pseudomonas aeruginosa.

Glucose-1-phosphate thymidylyltransferase (RmlA) catalyzes the condensation of glucose-1-phosphate (G1P) with deoxy-thymidine triphosphate (dTTP) to yield dTDP-d-glucose and pyrophosphate. This is the first step in the l-rhamnose biosynthetic pathway. l-Rhamnose is an important component of the cell wall of many microorganisms, including Mycobacterium tuberculosis and Pseudomonas aeruginosa. Here we describe the first nanomolar inhibitors of P. aeruginosa RmlA. These thymine analogues were identified by high-throughput screening and subsequently optimized by a combination of protein crystallography, in silico screening, and synthetic chemistry. Some of the inhibitors show inhibitory activity against M. tuberculosis. The inhibitors do not bind at the active site of RmlA but bind at a second site remote from the active site. Despite this, the compounds act as competitive inhibitors of G1P but with high cooperativity. This novel behavior was probed by structural analysis, which suggests that the inhibitors work by preventing RmlA from undergoing the conformational change key to its ordered bi-bi mechanism.

Chemoenzymatically prepared heparan sulfate containing rare 2-O-sulfonated glucuronic acid residues.

The structural diversity of natural sulfated glycosaminoglycans (GAGs) presents major promise for discovery of chemical biology tools or therapeutic agents. Yet, few GAGs have been identified so far to exhibit this promise. We reasoned that a simple approach to identify such GAGs is to explore sequences containing rare residues, for example, 2-O-sulfonated glucuronic acid (GlcAp2S). Genetic algorithm-based computational docking and filtering suggested that GlcAp2S containing heparan sulfate (HS) may exhibit highly selective recognition of antithrombin, a key plasma clot regulator. HS containing only GlcAp2S and 2-N-sulfonated glucosamine residues, labeled as HS2S2S, was chemoenzymatically synthesized in just two steps and was found to preferentially bind antithrombin over heparin cofactor II, a closely related serpin. Likewise, HS2S2S directly inhibited thrombin but not factor Xa, a closely related protease. The results show that a HS containing rare GlcAp2S residues exhibits the unusual property of selective antithrombin activation and direct thrombin inhibition. More importantly, HS2S2S is also the first molecule to activate antithrombin nearly as well as the heparin pentasaccharide although being completely devoid of the critical 3-O-sulfonate group. Thus, this work shows that novel functions and mechanisms may be uncovered by studying rare GAG residues/sequences.

Designing “High-Affinity, High-Specificity” Glycosaminoglycan Sequences Through Computerized Modeling

The prediction of high-affinity and/or high-specificity protein-glycosaminoglycan (GAG) interactions is an inherently difficult task, due to several factors including the shallow nature of the typical GAG-binding site and the inherent size, flexibility, diversity, and polydisperse nature of the GAG molecules. Here, we present a generally applicable methodology termed Combinatorial Library Virtual Screening (CVLS) that can identify potential high-affinity, high-specificity protein-GAG interactions from very large GAG combinatorial libraries and a suitable GAG-binding protein. We describe the CVLS approach along with the rationale behind it and provide validation for the method using the well-known antithrombin-thrombin-heparin system.

Computational Analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure-Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q(2)=0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account--something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path toward further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins.