Structure-directed combinatorial library design

Computationally Guided Identification of Novel Mycobacterium tuberculosis GlmU Inhibitory Leads, Their Optimization, and in Vitro Validation

Mycobacterium tuberculosis (Mtb) infections are causing serious health concerns worldwide. Antituberculosis drug resistance threatens the current therapies and causes further need to develop effective antituberculosis therapy. GlmU represents an interesting target for developing novel Mtb drug candidates. It is a bifunctional acetyltransferase/uridyltransferase enzyme that catalyzes the biosynthesis of UDP-N-acetyl-glucosamine (UDP-GlcNAc) from glucosamine-1-phosphate (GlcN-1-P). UDP-GlcNAc is a substrate for the biosynthesis of lipopolysaccharide and peptidoglycan that are constituents of the bacterial cell wall. In the current study, structure and ligand based computational models were developed and rationally applied to screen a drug-like compound repository of 20,000 compounds procured from ChemBridge DIVERSet database for the identification of probable inhibitors of Mtb GlmU. The in vitro evaluation of the in silico identified inhibitor candidates resulted in the identification of 15 inhibitory leads of this target. Literature search of these leads through SciFinder and their similarity analysis with the PubChem training data set (AID 1376) revealed the structural novelty of these hits with respect to Mtb GlmU. IC50 of the most potent identified inhibitory lead (5810599) was found to be 9.018 ± 0.04 μM. Molecular dynamics (MD) simulation of this inhibitory lead (5810599) in complex with protein affirms the stability of the lead within the binding pocket and also emphasizes on the key interactive residues for further designing. Binding site analysis of the acetyltransferase pocket with respect to the identified structural moieties provides a thorough analysis for carrying out the lead optimization studies.

Chlorination pattern effect on thermodynamic parameters and environmental degradability for C₁₀-SCCPs: Quantum chemical calculation based on virtual combinational library

Short-chain chlorinated paraffins (SCCPs) are still controversial candidates for inclusion in the Stockholm Convention. The inherent mixture nature of SCCPs makes it rather difficult to explore their environmental behaviors. A virtual molecule library of 42,720 C10-SCCP congeners covering the full structure spectrum was constructed. We explored the structural effects on the thermodynamic parameters and environmental degradability of C10-SCCPs through semi-empirical quantum chemical calculations. The thermodynamic properties were acquired using the AM1 method, and frontier molecular orbital analysis was carried out to obtain the E(HOMO), E(LUMO) and E(LUMO)-E(HOMO) for degradability exploration at the same level. The influence of the chlorination degree (N(Cl)) on the relative stability and environmental degradation was elucidated. A novel structural descriptor, μ, was proposed to measure the dispersion of the chlorine atoms within a molecule. There were significant correlations between thermodynamic values and N(Cl), while the reported N(Cl)-dependent pollution profile of C10-SCCPs in environmental samples was basically consistent with the predicted order of formation stability of C10-SCCP congeners. In addition, isomers with large μ showed higher relative stability than those with small μ. This could be further verified by the relationship between μ and the reactivity of nucleophilic substitution and OH attack respectively. The C10-SCCP congeners with less Cl substitution and lower dispersion degree are susceptible to environmental degradation via nucleophilic substitution and hydroxyl radical attack, while direct photolysis of C10-SCCP congeners cannot readily occur due to the large E(LUMO)-E(HOMO) values. The chlorination effect and the conclusions were further checked with appropriate density functional theory (DFT) calculations.

Computational protein design methods for synthetic biology

Computational protein design, a process that searches for mutants with desired improved properties, plays a central role in the conception of many synthetic biology devices including biosensors, bioproduction, or regulation circuits. To that end, a rational workflow for computational protein design is described here consisting of (a) searching in the sequence, structure or chemical spaces for the desired function and associated protein templates; (b) finding the list of potential hot regions to mutate in the parent proteins; and (c) performing in silico screening of mutants with predicted improved properties.

Rapid two-step synthesis of benzimidazo[1',2':1,5]pyrrolo[2,3-c]isoquinolines by a three-component coupling reaction

A two-step, three-component coupling reaction on ionic liquid supported 2-cyanomethylbenzimidazoles, methyl 2-formylbenzoate, and isocyanides under microwave activation is explored. Knoevenagel condensation of 2-cyanomethylbenzimidazole with methyl-2-formylbenzoate in the presence of piperidine catalyst is followed by [4+1] cycloaddition with an isocyanide in the next step. Consequent intramolecular δ-lactam formation allows rapid construction of novel aza-pentacycles, benzimidazo[1',2':1,5]pyrrolo[2,3-c]isoquinolines.

Toward rational fragment-based lead design without 3D structures

Fragment-based lead discovery (FBLD) has become a prime component of the armamentarium of modern drug design programs. FBLD identifies low molecular weight ligands that weakly bind to important biological targets. Three-dimensional structural information about the binding mode is provided by X-ray crystallography or NMR spectroscopy and is subsequently used to improve the lead compounds. Despite tremendous success rates, FBLD relies on the availability of high-resolution structural information, still a bottleneck in drug discovery programs. To overcome these limitations, we recently demonstrated that the meta-structure approach provides an alternative route to rational lead identification in cases where no 3D structure information about the biological target is available. Combined with information-rich NMR data, this strategy provides valuable information for lead development programs. We demonstrate with several examples the feasibility of the combined NMR and meta-structure approach to devise a rational strategy for fragment evolution without resorting to highly resolved protein complex structures.

Biomolecularmodeling and simulation: a field coming of age

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field’s history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right.

Methods for combinatorial and parallel library design

Diversity has historically played a critical role in design of combinatorial libraries, screening sets and corporate collections for lead discovery. Large library design dominated the field in the 1990s with methods ranging anywhere from purely arbitrary through property based reagent selection to product based approaches. In recent years, however, there has been a downward trend in library size. This was due to increased information about the desirable targets gleaned from the genomics revolution and to the ever growing availability of target protein structures from crystallography and homology modeling. Creation of libraries directed toward families of receptors such as GPCRs, kinases, nuclear hormone receptors, proteases, etc., replaced the generation of libraries based primarily on diversity while single target focused library design has remained an important objective. Concurrently, computing grids and cpu clusters have facilitated the development of structure based tools that screen hundreds of thousands of molecules. Smaller "smarter" combinatorial and focused parallel libraries replaced those early un-focused large libraries in the twenty-first century drug design paradigm. While diversity still plays a role in lead discovery, the focus of current library design methods has shifted to receptor based methods, scaffold hopping/bio-isostere searching, and a much needed emphasis on synthetic feasibility. Methods such as "privileged substructures based design" and pharmacophore based design still are important methods for parallel and small combinatorial library design. This chapter discusses some of the possible design methods and presents examples where they are available.

Docking methods for structure-based library design

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

The Interweaving of Cheminformatics and HTS

The aim of this chapter is to describe the stages of early drug discovery that can be assisted by techniques commonly used in the field of cheminformatics. In fact, cheminformatics tools can be applied all the way from the design of compound libraries and the analysis of HTS results, to the discovery of functional relationships between compounds and their targets.

The multiple roles of computational chemistry in fragment-based drug design

Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, e.g. by NMR or X-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like hit or lead compound can result in a final compound with too high molecular weight and lipophilicity. The evolution of a lower molecular weight fragment hit therefore represents an attractive alternative approach to optimisation as it allows better control of compound properties. Computational chemistry can play an important role both prior to a fragment screen, in producing a target focussed fragment library, and post-screening in the evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure. We will review many of the current developments in the area and illustrate with some recent examples from successful FBDD discovery projects that we have conducted.

Kinetic and structural studies of phosphodiesterase-8A and implication on the inhibitor selectivity

Cyclic nucleotide phosphodiesterase-8 (PDE8) is a family of cAMP-specific enzymes and plays important roles in many biological processes, including T-cell activation, testosterone production, adrenocortical hyperplasia, and thyroid function. However, no PDE8 selective inhibitors are available for trial treatment of human diseases. Here we report kinetic properties of the highly active PDE8A1 catalytic domain prepared from refolding and its crystal structures in the unliganded and 3-isobutyl-1-methylxanthine (IBMX) bound forms at 1.9 and 2.1 A resolutions, respectively. The PDE8A1 catalytic domain has a K(M) of 1.8 microM, V(max) of 6.1 micromol/min/mg, a k(cat) of 4.0 s(-1) for cAMP, and a K(M) of 1.6 mM, V(max) of 2.5 micromol/min/mg, a k(cat) of 1.6 s(-1) for cGMP, thus indicating that the substrate specificity of PDE8 is dominated by K(M). The structure of the PDE8A1 catalytic domain has similar topology as those of other PDE families but contains two extra helices around Asn685-Thr710. Since this fragment is distant from the active site of the enzyme, its impact on the catalysis is unclear. The PDE8A1 catalytic domain is insensitive to the IBMX inhibition (IC(50) = 700 microM). The unfavorable interaction of IBMX in the PDE8A1-IBMX structure suggests an important role of Tyr748 in the inhibitor binding. Indeed, the mutation of Tyr748 to phenylalanine increases the PDE8A1 sensitivity to several nonselective or family selective PDE inhibitors. Thus, the structural and mutagenesis studies provide not only insight into the enzymatic properties but also guidelines for design of PDE8 selective inhibitors.