Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking

Docking and scoring in virtual screening for drug discovery: methods and applications

Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.

Scoring functions for protein-ligand interactions: a critical perspective

Scoring functions play an essential role in structure-based virtual screening. They are required to guide the docking of candidate compounds to structures of receptor binding sites, to select probable binding modes, and to discriminate binders from non-binders. Although many scoring functions have successfully been used to identify novel ligands for a wide variety of targets, much work remains to be done to avoid incorrect prediction of binding modes and high numbers of false positives. This review gives an overview of the current state of the field and outlines key issues for the further development of scoring functions.:

Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors

A method of easily finding ligands, with a variety of core structures, for a given target macromolecule would greatly contribute to the rapid identification of novel lead compounds for drug development. We have developed an efficient method for discovering ligand candidates from a number of flexible compounds included in databases, when the three-dimensional (3D) structure of the drug target is available. The method, named ADAM&EVE, makes use of our automated docking method ADAM, which has already been reported. Like ADAM, ADAM&EVE takes account of the flexibility of each molecule in databases, by exploring the conformational space fully and continuously. Database screening has been made much faster than with ADAM through the tuning of parameters, so that computational screening of several hundred thousand compounds is possible in a practical time. Promising ligand candidates can be selected according to various criteria based on the docking results and characteristics of compounds. Furthermore, we have developed a new tool, EVE-MAKE, for automatically preparing the additional compound data necessary for flexible docking calculation, prior to 3D database screening. Among several successful cases of lead discovery by ADAM&EVE, the finding of novel acetylcholinesterase (AChE) inhibitors is presented here. We performed a virtual screening of about 160 000 commercially available compounds against the X-ray crystallographic structure of AChE. Among 114 compounds that could be purchased and assayed, 35 molecules with various core structures showed inhibitory activities with IC(50) values less than 100 microM. Thirteen compounds had IC(50) values between 0.5 and 10 microM, and almost all their core structures are very different from those of known inhibitors. The results demonstrate the effectiveness and validity of the ADAM&EVE approach and provide a starting point for development of novel drugs to treat Alzheimer's disease.

High-throughput docking as a source of novel drug leads

Receptor-based virtual screening has become a viable source of novel leads in the pharmaceutical industry. The rapidly growing availability of structural information across protein families, the accessibility to increased computational power at affordable cost, as well as an improved understanding on how to effectively apply virtual screening technologies has contributed to their emergence. Nonetheless, continued improvement in the accuracy of scoring functions and a greater understanding of protein mobility is critical to advance the technology further.

Automated protein structure homology modeling: a progress report

Understanding the molecular function of proteins is greatly enhanced by insights gained from their three-dimensional structures. Since experimental structures are only available for a small fraction of proteins, computational methods for protein structure modeling play an increasingly important role. Comparative protein structure modeling is currently the most accurate method, yielding models suitable for a wide spectrum of applications, such as structure-guided drug development or virtual screening. Stable and reliable automated prediction pipelines have been developed to apply large-scale comparative modeling to whole genomes or entire sequence databases. Model repositories give access to these annotated and evaluated models. In this review, we will discuss recent developments in automated comparative modeling and provide selected examples illustrating the use of homology models.

A detailed comparison of current docking and scoring methods on systems of pharmaceutical relevance

A thorough evaluation of some of the most advanced docking and scoring methods currently available is described, and guidelines for the choice of an appropriate protocol for docking and virtual screening are defined. The generation of a large and highly curated test set of pharmaceutically relevant protein-ligand complexes with known binding affinities is described, and three highly regarded docking programs (Glide, GOLD, and ICM) are evaluated on the same set with respect to their ability to reproduce crystallographic binding orientations. Glide correctly identified the crystallographic pose within 2.0 A in 61% of the cases, versus 48% for GOLD and 45% for ICM. In general Glide appears to perform most consistently with respect to diversity of binding sites and ligand flexibility, while the performance of ICM and GOLD is more binding site-dependent and it is significantly poorer when binding is predominantly driven by hydrophobic interactions. The results also show that energy minimization and reranking of the top N poses can be an effective means to overcome some of the limitations of a given docking function. The same docking programs are evaluated in conjunction with three different scoring functions for their ability to discriminate actives from inactives in virtual screening. The evaluation, performed on three different systems (HIV-1 protease, IMPDH, and p38 MAP kinase), confirms that the relative performance of different docking and scoring methods is to some extent binding site-dependent. GlideScore appears to be an effective scoring function for database screening, with consistent performance across several types of binding sites, while ChemScore appears to be most useful in sterically demanding sites since it is more forgiving of repulsive interactions. Energy minimization of docked poses can significantly improve the enrichments in systems with sterically demanding binding sites. Overall Glide appears to be a safe general choice for docking, while the choice of the best scoring tool remains to a larger extent system-dependent and should be evaluated on a case-by-case basis.

The Role of Virtual Screening in Computer Aided Structure-Based Drug Design

The pharmaceutical industry has embraced computational methods to improve the successful negotiation of hits and leads into drugs in the clinic. This review examines the current status of in silico screening methods and aspects of compound library design.

Biochemical and three-dimensional-structural study of the specific inhibition of protein kinase CK2 by [5-oxo-5,6-dihydroindolo-(1,2-a)quinazolin-7-yl]acetic acid (IQA)

IQA [[5-oxo-5,6-dihydro-indolo(1,2-a)quinazolin-7-yl]acetic acid] is a novel ATP/GTP site-directed inhibitor of CK2 ('casein kinase 2'), a pleiotropic and constitutively active protein kinase whose activity is abnormally high in transformed cells. The K (i) value of IQA (0.17 microM) is lower than those of other CK2 inhibitors reported so far. Tested at 10 microM concentration in the presence of 100 microM ATP, IQA almost suppresses CK2 activity in vitro, whereas it is ineffective or weakly effective on a panel of 44 protein kinases and on phosphoinositide 3-kinase. In comparison, other CK2 inhibitors, notably apigenin and quercetin, are more promiscuous. The in vivo efficacy of IQA has been assessed by using the fact that treatment of Jurkat cells with IQA inhibits endogenous CK2 in a dose-dependent manner. IQA has been co-crystallized with maize CK2alpha, which is >70% identical with its human homologue, and the structure of the complex has been determined at 1.68 A (1 A=0.1 nm) resolution. The inhibitor lies in the same plane occupied by the purine moiety of ATP with its more hydrophobic side facing the hinge region. Major contributions to the interaction are provided by hydrophobic forces and non-polar interactions involving the aromatic portion of the inhibitor and the hydrophobic residues surrounding the ATP-binding pocket, with special reference to the side chains of V53 (Val53), I66, M163 and I174. Consequently, mutants of human CK2alpha in which either V66 (the homologue of maize CK2alpha I66) or I174 is replaced by alanine are considerably less sensitive to IQA inhibition when compared with wild-type. These results provide new tools for deciphering the enigmatic role of CK2 in living cells and may pave the way for the development of drugs depending on CK2 activity.