Current Pharmaceutical Design - Volume 19, Issue 26, 2013

Multiple approaches have been devised and evaluated to computationally estimate binding free energies. Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method suggest that this method has the potential to provide the most accurate estimation of binding affinities to date. The method treats ligands/inhibitors using QM while using MM for the rest of the system. The method has been applied and validated for a structurally diverse set of fructose 1,6- bisphosphatase (FBPase) inhibitors suggesting that the approach has the potential to be used as an integral part of drug discovery for both lead identification lead optimization, where there is a structure available. In addition, this QM/MM-based FEP method was shown to accurately replicate the anomalous hydration behavior exhibited by simple amines and amides suggesting that the method may also prove useful in predicting physical properties of molecules. While the method is about 5-fold more computationally demanding than conventional FEP, it has the potential to be less demanding on the end user since it avoids development of MM force field parameters for novel ligands and thereby eliminates this time-consuming step that often contributes significantly to the inaccuracy of binding affinity predictions using conventional FEP methods. The QM/MM-based FEP method has been extensively tested with respect to important considerations such as the length of the simulation required to obtain satisfactory convergence in the calculated relative solvation and binding free energies for both small and large structural changes between ligands. Future automation of the method and parallelization of the code is expected to enhance the speed and increase its use for drug design and lead optimization.

These are exciting times for bioinformaticians, computational biologists and drug designers with the genome and proteome sequences and related structural databases growing at an accelerated pace. The post-genomic era has triggered high expectations for a rapid and successful treatment of diseases. However, in this biological information rich and functional knowledge poor scenario, the challenges are indeed grand, no less than the assembly of the genome of the whole organism. These include functional annotation of genes, identification of druggable targets, prediction of three-dimensional structures of protein targets from their amino acid sequences, arriving at lead compounds for these targets followed by a transition from bench to bedside. We propose here a “Genome to Hits In Silico” strategy (called Dhanvantari) and illustrate it on Chikungunya virus (CHIKV). “Genome to hits” is a novel pathway incorporating a series of steps such as gene prediction, protein tertiary structure determination, active site identification, hit molecule generation, docking and scoring of hits to arrive at lead compounds. The current state of the art for each of the steps in the pathway is high-lighted and the feasibility of creating an automated genome to hits assembly line is discussed.

This review highlights some recent advances in the design and development of matrix metalloproteinase inhibitors, especially those targeting MMP-2, MMP-9, and MMP-13. Various zinc-binding groups and non-zinc-binding groups are discussed. Interactions between residues in the critical S1' specificity pocket and MMP inhibitors are given special attention. The influence of ionization states of hydroxamates and retrohydroxamates on the docking outcome and the presence of zinc ions in the active site are explored in light of enhancing enrichment factors for docking studies. Details are given to structural factors for the development of more selective and more potent MMP inhibitors.

Kinases are one of the most popular classes of drug targets as they are involved in signal transduction pathways, which are wired through a phosphotransfer cascade and elicit a number of important and essential physiological responses. Kinase specificity has emerged as one of the major issues to be addressed in drug discovery approaches. In most kinases the active site is the ATP binding site and finding suitable hits which maximize the affinity of binding has been traditionally important to obtain the type I inhibitors. While type I inhibitors have effective binding affinity more often than not they encounter side-effects usually associated with specificity. Therefore in recent times it has become indispensable to optimize specificity for developing effective kinase inhibitors. The review presents an overview of kinase drug discovery and the different strategies used to date for the design of kinase leads accounting for their success and failure. A number of strategies exploiting different aspects of kinases like allosteric site, size of the gatekeeper residue, DFG-loop, chemotype selectivity, non-covalent interactions, salt-bridge, solvation, etc. have been explored to circumvent the specificity problem in kinases. The probable hot-spots in kinases having a propensity to bring in specificity have been delineated with special emphasis on the design of type II inhibitors with increased specificity from existing type I using fragment tailoring approach. In this review we illustrate the current strategies by taking p38 MAP kinase as a model and expect that such strategies are general and can be extended to the other members of the kinase family.

This article reviews some of our experiences on applying computational techniques to aid the design of drugs targeting protein kinases and phosphatases. It is not a comprehensive review. Rather, it focuses on several less explored approaches or ideas that we have experiences on. It reviews some recent improvements on the Poisson-Boltzmann/Surface Area model for calculating binding affinity and discusses ways to perform calculations that are more tolerant to statistical and systematic errors. Several new ways to incorporate protein flexibility in molecular docking and estimating binding affinity are also discussed. Its discussions also go beyond binding affinity to considering drug-binding kinetics, not only on investigating protein-ligand interactions in isolation, but also on accounting for upstream and downstream influences that can occur in cells, through kinetic modeling of cell signaling. This review also describes a quick molecular simulation method for understanding drug-binding kinetics at the molecular level, with the hope of generating guiding principles for designing drugs with the desired kinetic properties. Sources of drug-binding selectivity that appear obvious but often overlooked are also discussed.

Glycogen Synthase Kinase-3 (GSK-3) is a constitutively acting multifunctional serine/threonine kinase, a role of which has been marked in several physiological pathways, making it a potential target for the treatment of many diseases, including Type-II diabetes and Alzheimer’s. Design of GSK-3β selective inhibitor was the key challenge which led to the use of rational approaches like structure based methods (molecular docking), and ligand based methods (QSAR, pharmacophore mapping) studies. These methods provide insights into the enzyme–ligand interactions and structure activity relationship of different sets of compounds for the design of promising GSK-3 inhibitors. Molecular dynamic simulation studies have additionally been performed to address key issues like the unique requirement of prime phosphorylation of its substrate at P+4 by GSK-3β. An allosteric site has also been reported, where the binding of the peptide leads to the stabilization of the activation loop, resulting in the enhancement of the catalysis of enzymes. These studies are becoming useful in the design of therapeutically active discriminatory GSK-3 inhibitors. In this article, we present a review of recent efforts and future opportunities for the design of selective GSK-3β inhibitors.

Viruses have been found to exhibit protein kinase activity associated with their purified viral particles. HIV-1 virus particles possess a novel 72 kD protein, Topoisomerase II beta kinase (Topo IIβKHIV) activity. The enzyme, isolated and purified from PEGprecipitated HIV-1 particles, is insensitive against a diverse set of known kinase inhibitors. The pyridine derivatives were found to be active against both Topo IIβKHIV activity and HIV-1 replication. For both kinase antagonism and anti-HIV-1 activity the Comparative Molecular Field Analysis (CoMFA) models were proposed. The CoMFA model was also evaluated independently with a set of test molecules for their anti-viral activity. The kinase inhibition and anti-viral activities for these inhibitors, tested in an in vitro kinase agree with the CoMFA model (cross-validated r2 (q2) value of 0.642 with six principal components), lower acceptable results are obtained with anti- HIV-1 activity (cross-validated r2 (q2) value of 0.358 with four principal components) and also correlate with relative solvation free energy calculations. The predictive power of the models was evaluated with 2 test molecules each and tends to lie within 1 log unit. An in cell validation of the model with a representative inhibitor, 2-methoxypyridine shows its ability to inhibit Topo IIβ phosphorylation during acute HIV-1 infection. Close correlation of molecular fields of inhibitory domains of kinase and HIV-1 inhibitors suggests specificity of action of pyridine derivatives in affecting HIV-1 replication through inhibition of Kinase activity. These investigations suggest that Topo IIβKHIV is a potential target for an effective control of HIV-1 replication that would help in developing new anti-retroviral molecules.

The ease of access to virtual screening (VS) software in recent years has resulted in a large increase in literature reports. Over 300 publications in the last year report the use of virtual screening techniques to identify new chemical matter or present the development of new virtual screening techniques. The increased use is accompanied by a corresponding increase in misuse and misinterpretation of virtual screening results. This review aims to identify many of the common difficulties associated with virtual screening and allow researchers to better assess the reliability of their virtual screening effort.