A novel conformation optimization model and algorithm for structure-based drug design

In this paper, we present a multi-scale optimization model and an entropy-based genetic algorithm for molecular docking. In this model, we introduce to the refined docking design a concept of residue groups based on induced-fit and adopt a combination of conformations in different scales. A new iteration scheme, in conjunction with multi-population evolution strategy, entropy-based searching technique with narrowing down space and the quasi-exact penalty function, is developed to address the optimization problem for molecular docking. A new docking program that accounts for protein flexibility has also been developed. The docking results indicate that the method can be efficiently employed in structure-based drug design.     

Computer-aided drug design: lead discovery and optimization

Over the past decade, there have been remarkable advances in the area of computer-aided drug design (CADD), which has been applied at almost all stages in the drug discovery pipeline. The generation of initial lead compounds and the subsequent optimization aimed at improving potency and pharmacological properties are the core activities among all. The development in these aspects over the past years will be the focus of this review.     

Identification of protoberberine alkaloids as novel histone methyltransferase G9a inhibitors by structure-based virtual screening

The protein lysine methyltransferase G9a, which controls gene expression by epigenetic regulation of H3K9 methylation, is related to various human diseases, including cancer, drug addiction, and mental retardation. In recent years, genetic, biological, and physiological evidence has established G9a inhibitors as potential chemotherapeutic agents for cancer treatment. In this study, we identified protoberberine alkaloid pseudodehydrocorydaline (CT13) as a novel G9a inhibitor, by structure-based virtual screening of in-house library containing natural product compounds. The activity of CT13 was determined by biophysical analyses involving MALDI-TOF mass spectrometry and western blot analysis. CT13 showed selective inhibitory activity against G9a and suppressed the level of H3K9me2 in MCF7 human breast cancer cells. Molecular docking analysis suggested the binding mode of CT13 which occupies the binding site of histone H3 substrate. CT13 provides a novel scaffold for further development of analogous synthetic G9a inhibitors.

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AstexViewer: a visualisation aid for structure-based drug design

AstexViewer is a Java molecular graphics program that can be used for visualisation in many aspects of structure-based drug design. This paper describes its functionality, implementation and examples of its use. The program can run as an Applet in a web browser allowing structures to be displayed without installing additional software. Applications of its use are described for visualisation and as part of a structure based design platform. The software is being made freely available to the community and may be downloaded from http://www.astex-technology.com/AstexViewer.     

The process of structure-based drug design

The field of structure-based drug design is a rapidly growing area in which many successes have occurred in recent years. The explosion of genomic, proteomic, and structural information has provided hundreds of new targets and opportunities for future drug lead discovery. This review summarizes the process of structure-based drug design and includes, primarily, the choice of a target, the evaluation of a structure of that target, the pivotal questions to consider in choosing a method for drug lead discovery, and evaluation of the drug leads. Key principles in the field of structure-based drug design will be illustrated through a case study that explores drug design for AmpC beta-lactamase.     

In search of new lead compounds for trypanosomiasis drug design: A protein structure-based linked-fragment approach

Summary A modular method for pursuing structure-based inhibitor design in the framework of a design cycle is presented. The approach entails four stages: (1) a design pathway is defined in the three-dimensional structure of a target protein; (2) this pathway is divided into subregions; (3) complementary building blocks, also called fragments, are designed in each subregion; complementarity is defined in terms of shape, hydrophobicity, hydrogen bond properties and electrostatics; and (4) fragments from different subregions are linked into potential lead compounds. Stages (3) and (4) are qualitatively guided by force-field calculations. In addition, the designed fragments serve as entries for retrieving existing compounds from chemical databases. This linked-fragment approach has been applied in the design of potentially selective inhibitors of triosephosphate isomerase from Trypanosoma brucei, the causative agent of sleeping sickness.     

Rational structure-based design of a novel carboxypeptidase R inhibitor

A novel carboxypeptidase R (CPR) inhibitor, related to potato carboxypeptidase inhibitor (PCI), was designed using rational structure-based strategies, incorporating two principle facts: CPR has a strong affinity for basic amino acids, and the two lysine and arginine residues of PCI are orientated in the same direction and held in close spatial proximity by three disulfide bonds. Initially, a disulfide-bonded fragment of PCI was synthesized showing weak competitive inhibitory activity against CPR. Subsequently, a smaller linear 9-mer peptide, designated CPI-2KR, was designed/synthesized and found to be a more efficient competitive inhibitor of CPR, without affecting the activity of the other plasma carboxypeptidase, carboxypeptidase N. In vitro studies showed that, together with tissue plasminogen activator, CPI-2KR synergistically accelerated fibrinolysis, representing a lead compound for the design of smaller organic molecules for use in thrombolytic therapy.     

A structure-based design approach for the identification of novel inhibitors: application to an alanine racemase

We report a new structure-based strategy for the identification of novel inhibitors. This approach has been applied to Bacillus stearothermophilus alanine racemase (AlaR), an enzyme implicated in the biosynthesis of the bacterial cell wall. The enzyme catalyzes the racemization of l- and d-alanine using pyridoxal 5-phosphate (PLP) as a cofactor. The restriction of AlaR to bacteria and some fungi and the absolute requirement for d-alanine in peptidoglycan biosynthesis make alanine racemase a suitable target for drug design. Unfortunately, known inhibitors of alanine racemase are not specific and inhibit the activity of other PLP-dependent enzymes, leading to neurological and other side effects.This article describes the development of a receptor-based pharmacophore model for AlaR, taking into account receptor flexibility (i.e. a `dynamic' pharmacophore model). In order to accomplish this, molecular dynamics (MD) simulations were performed on the full AlaR dimer from Bacillus stearothermophilus (PDB entry, 1sft) with a d-alanine molecule in one active site and the non-covalent inhibitor, propionate, in the second active site of this homodimer. The basic strategy followed in this study was to utilize conformations of the protein obtained during MD simulations to generate a dynamic pharmacophore model using the property mapping capability of the LigBuilder program. Compounds from the Available Chemicals Directory that fit the pharmacophore model were identified and have been submitted for experimental testing.The approach described here can be used as a valuable tool for the design of novel inhibitors of other biomolecular targets.     

PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritised for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.     

Development of a protease production platform for structure-based drug design

Structure-based drug design (SBDD) has played an integral role in the development of highly specific, potent protease inhibitors resulting in a number of drugs in clinical trials and on the market. Possessing biochemical assays and structural information on both the target protease and homologous family members helps ensure compound selectivity. We have redesigned the path from clone to protein eliminating many of the traditional bottlenecks associated with protein production to ensure a constant supply to feed many diverse protease drug discovery programs. The process was initiated with the design of a multi-system vector, capable of expression in both eukaryotic and prokaryotic hosts; this vector also facilitated high-throughput cloning, expression and purification. When combined into an expression screen, supplemented with salvage screens for detergent extraction and refolding, a route for protein production was established rapidly. Using this process-orientated approach we have successfully expressed and purified all mechanistic classes of active human and viral proteases for enzymatic assays and crystallization studies. While exploiting recent developments in high-throughput biochemistry, we still employ classical biophysical techniques such as light-scattering and analytical ultracentrifugation, to ensure the highest quality protein enters crystallization trials. We have drawn on examples from our own research programs to illustrate how these strategies have been successfully used in the production of proteases for SBDD.     

Development of KiBank, a database supporting structure-based drug design

KiBank is a database of inhibition constant (K_i) values with 3D structures of target proteins and chemicals. K_i values were accumulated from peer-reviewed literature searched via PubMed. The 3D structure files of target proteins were originally from Protein Data Bank (PDB), while the 2D structure files of the chemicals were collected together with the K_i values and then converted into 3D ones. In KiBank, the chemical and protein 3D structures with hydrogen atoms were optimized by energy minimization and stored in MDL MOL and PDB format, respectively.

KiBank is designed to support structure-based drug design. It provides structure files of proteins and chemicals ready for use in virtual screening through automated docking methods, while the K_i values can be applied for tests of docking/scoring combinations, program parameter settings, and calibration of empirical scoring functions. Additionally, the chemical structures and corresponding K_i values in KiBank are useful for lead optimization based on quantitative structure–activity relationship (QSAR) techniques.

KiBank is updated on a daily basis and is freely available at http://kibank.iis.u-tokyo.ac.jp/. As of August 2004, KiBank contains 8000 K_i values, over 6000 chemicals and 166 proteins covering the subtypes of receptors and enzymes.     

Isothermal titration calorimetry: application to structure-based drug design

The road to market for drug compounds is a treacherous one, generally involving a huge temporal and financial investment. The role of structure-based drug design or lead optimisation ranges wildly in importance in different pharmaceutical companies. The adoption of these aids to provide routes to high affinity ligands has not received widespread acceptance. This is based on a number of factors, from the perceived failings of such methods, to the belief that rapid screening of compound libraries alone is the most effective way to discover drugs.

The panacea of being able to take a computer generated representation of the structure of a target site of a given biomolecule and rationally design an high affinity inhibiting compound has proved seemingly unreachable for three major reasons: (1) current capabilities in computing; (2) the requirement for atomic resolution structural detail; and (3) determination of how structural features can be related to the thermodynamics of interactions. It is the last of these points that this review seeks to address. In particular the use of isothermal titration calorimetry is discussed in the light of the accumulation of accurate thermodynamic data and examples are given where this has been applied to understand the structural aspects of formation of drug–biomolecular complexes.     

An integrated approach to ligand- and structure-based drug design: development and application to a series of serine protease inhibitors

A novel approach was developed to rationally interface structure- and ligand-based drug design through the rescoring of docking poses and automated generation of molecular alignments for 3D quantitative structure-activity relationship investigations. The procedure was driven by a genetic algorithm optimizing the value of a novel fitness function, accounting simultaneously for best regressions among binding-energy docking scores and affinities and for minimal geometric deviations from properly established crystal-based binding geometry. The GRID/CPCA method, as implemented in GOLPE, was used to feature molecular determinants of ligand binding affinity for each molecular alignment. In addition, unlike standard procedures, a novel multipoint equation was adopted to predict the binding affinity of ligands in the prediction set. Selectivity was investigated through square plots reporting experimental versus recalculated binding affinities on the targets under examination. The application of our approach to the modeling of affinity data of a large series of 3-amidinophenylalanine inhibitors of thrombin, trypsin, and factor Xa generated easily interpretable and independent models with robust statistics. As a further validation study, our approach was successfully applied to a series of 3,4,7-substituted coumarins, acting as selective MAO-B inhibitors.     

Mini-review: computational structure-based design of inhibitors that target protein surfaces

Finding drugs that inhibit protein-protein interactions is usually difficult. While computer-aided design is used widely to facilitate the drug discovery process for protein targets with well-defined binding pockets, its application to the design of inhibitors targeting a protein surface is very limited. In this mini-review we address two aspects of this issue: firstly, we overview the current state of design methodology for inhibitors specifically targeting protein surfaces, and secondly, we briefly outline recent advances in computational methods for structure-based drug design. These methods are closely related to protein docking and protein recognition, the difference being that in ligand design, ligands are built on a fragment-by-fragment basis. A novel scheme of computational combinatorial ligand design developed for the design of inhibitors that interfere with protein-protein interaction is described in detail. Current applications and limitations of this methodology, as well as its future prospects, are discussed.     

The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design

Fragment-based ligand design approaches, such as the multi-copy simultaneous search (MCSS) methodology, have proven to be useful tools in the search for novel therapeutic compounds that bind pre-specified targets of known structure. MCSS offers a variety of advantages over more traditional high-throughput screening methods, and has been applied successfully to challenging targets. The methodology is quite general and can be used to construct functionality maps for proteins, DNA, and RNA. In this review, we describe the main aspects of the MCSS method and outline the general use of the methodology as a fundamental tool to guide the design of de novo lead compounds. We focus our discussion on the evaluation of MCSS results and the incorporation of protein flexibility into the methodology. In addition, we demonstrate on several specific examples how the information arising from the MCSS functionality maps has been successfully used to predict ligand binding to protein targets and RNA.     

Hit diffusion: limitations to drug discovery and structure-based design

Journal of Computer-Aided Molecular Design - Modern drug discovery employs a ‘screening funnel’ to pick compounds worthy of advancing to the clinic, a multi-step process linking a...     

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.     

MM-GB/SA rescoring of docking poses in structure-based lead optimization

The critical issues in docking include the prediction of the correct binding pose and the accurate estimation of the corresponding binding affinity. Different docking methodologies have all been successful in reproducing the crystallographic binding modes but struggle when predicting the corresponding binding affinities. The aim of this work is to evaluate the performance of the MM-GB/SA rescoring of docking poses in structure-based lead optimization. To accomplish that, a diverse set of pharmaceutically relevant targets, including CDK2, FactorXa, Thrombin, and HIV-RT were selected. The correlation between the MM-GB/SA results and experimental data in all cases is remarkable. It even qualifies this approach as a more attractive alternative for rank-ordering than the Free Energy Perturbation and Thermodynamic Integration methodologies because, while as accurate, it can handle more structurally dissimilar ligands and provides results at a fraction of the computational cost. On the technical side, the benefit of performing a conformational analysis and having an ensemble of conformers to represent each ligand in the unbound state during the MM-GB/SA rescoring procedure was investigated. In addition, the estimation of conformational entropy penalties for the ligands upon binding, computed from the Boltzmann distribution in water, was evaluated and compared to a commonly used approach employed by many docking scoring functions.     

QXP: Powerful, rapid computer algorithms for structure-based drug design

New methods for docking, template fitting and building pseudo-receptors are described. Full conformational searches are carried out for flexible cyclic and acyclic molecules. QXP (quick explore) search algorithms are derived from the method of Monte Carlo perturbation with energy minimization in Cartesian space. An additional fast search step is introduced between the initial perturbation and energy minimization. The fast search produces approximate low-energy structures, which are likely to minimize to a low energy. For template fitting, QXP uses a superposition force field which automatically assigns short-range attractive forces to similar atoms in different molecules. The docking algorithms were evaluated using X-ray data for 12 protein–ligand complexes. The ligands had up to 24 rotatable bonds and ranged from highly polar to mostly nonpolar. Docking searches of the randomly disordered ligands gave rms differences between the lowest energy docked structure and the energy-minimized X-ray structure, of less than 0.76 Å for 10 of the ligands. For all the ligands, the rms difference between the energy-minimized X-ray structure and the closest docked structure was less than 0.4 Å, when parts of one of the molecules which are in the solvent were excluded from the rms calculation. Template fitting was tested using four ACE inhibitors. Three ACE templates have been previously published. A single run using QXP generated a series of templates which contained examples of each of the three. A pseudo-receptor, complementary to an ACE template, was built out of small molecules, such as pyrrole, cyclopentanone and propane. When individually energy minimized in the pseudo-receptor, each of the four ACE inhibitors moved with an rms of less than 0.25 Å. After random perturbation, the inhibitors were docked into the pseudo-receptor. Each lowest energy docked structure matched the energy-minimized geometry with an rms of less than 0.08 Å. Thus, the pseudo-receptor shows steric and chemical complementarity to all four molecules. The QXP program is reliable, easy to use and sufficiently rapid for routine application in structure-based drug design.     

Identification of cylin-dependent kinase 1 inhibitors of a new chemical type by structure-based design and database searching

We have selected cyclin-dependent kinase 1 (CDK1), an enzyme participating in the regulation of the cell cycle, as a target in our efforts to discover new antitumor agents. By exploiting available structural information, we designed an ATP-site directed ligand scaffold that allowed us to identify 4-(3-methyl-1,4-dioxo-1,4-dihydro-naphthalen-2-ylamino)-benzenesulfonamide as a new potent inhibitor of CDK1 in a subsequent database search. The synthesis and testing of some analogues confirmed the interest of this class of compounds as novel CDK1 inhibitors.