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.     

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.     

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.     

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.     

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.     

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.     

Impact of mobility on structure-based drug design for the MMPs

Structure-based approaches for drug design generally do not incorporate solvent effects and dynamic information to predict inhibitor-binding affinity because of practical limitations. The matrix metalloproteinases (MMPs) have previously been demonstrated to exhibit significant mobility in their active sites. This dynamic characteristic significantly complicates the drug design process based on static structures, which was clearly observed for a class of hydroxamic acids containing a butynyl moiety. Compound 1 was expected to be selective against MMP-1 based on predicted steric clashes between the butynyl P1' group and the S1' pocket, but the observation of complex inhibitor dynamics in the NMR structure of MMP-1:1 provides an explanation for the low nanomolar binding to MMP-1.     

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...     

Structural biology, protein conformations and drug designing

Structure based drug designing is now a popular technique used for increasing the speed of drug designing process. This was made possible by the availability of many protein structures which helped in developing tools to understand the structure function relationships, automated docking and virtual screening. Knowledge of structure based functional properties of a drug target is very essential for a successful in silico designing of drugs. However, some problems associated with the structure determination process and lack of knowledge of conformational freedom associated with available protein structures are the hurdles involved in structure based drug designing. Docking and virtual screening processes depend on the active site structure of the receptor molecule and subtle differences in the conformations of these molecules due to flexibility pose a serious threat to the drug designing process. In this review problems associated with the conformations of proteins and homology models was reviewed.     

Application and limitations of X-ray crystallographic data in structure-based ligand and drug design

Structure-based design usually focuses upon the optimization of ligand affinity. However, successful drug design also requires the optimization of many other properties. The primary source of structural information for protein-ligand complexes is X-ray crystallography. The uncertainties introduced during the derivation of an atomic model from the experimentally observed electron density data are not always appreciated. Uncertainties in the atomic model can have significant consequences when this model is subsequently used as the basis of manual design, docking, scoring, and virtual screening efforts. Docking and scoring algorithms are currently imperfect. A good correlation between observed and calculated binding affinities is usually only observed only when very large ranges of affinity are considered. Errors in the correlation often exceed the range of affinities commonly encountered during lead optimization. Some structure-based design approaches now involve screening libraries by using technologies based on NMR spectroscopy and X-ray crystallography to discover small polar templates, which are used for further optimization. Such compounds are defined as leadlike and are also sought by more traditional high-throughput screening technologies. Structure-based design and HTS technologies show important complementarity and a degree of convergence.     

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.     

HIV-1 gp120 V3 loop for structure-based drug design

HIV-1 cell entry is mediated by sequential interactions of the envelope protein gp120 with the receptor CD4 and a coreceptor, usually CCR5 or CXCR4, depending on the individual virion. Considerable efforts on exploiting the HIV coreceptors as drug targets have led to the new class of coreceptor antagonists. While these antiretroviral drugs aim at preventing virus/coreceptor interaction by binding to host proteins, neutralizing antibodies directed against the coreceptor-binding sites on gp120 have attracted attention as possible vaccine candidates. However, both approaches are complicated by the multiple protective mechanisms of gp120 which allow for rapid escape from selective pressures exerted by drugs or antibodies. Thus, advances in rational drug and vaccine design rely heavily on improved insights into the relation between genotype and phenotype, the evolution of coreceptor usage, and, ultimately the structural biology of coreceptor usage and inhibition. The third variable (V3) loop of gp120, crucially involved in all these aspects, will be a major focus of this review.     

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.     

Use of the tubulin bound paclitaxel conformation for structure-based rational drug design

A new computational docking protocol has been developed and used in combination with conformational information inferred from REDOR-NMR experiments on microtubule bound 2-(p-fluorobenzoyl)paclitaxel to delineate a unique tubulin binding structure of paclitaxel. A conformationally constrained macrocyclic taxoid bearing a linker between the C-14 and C-3'N positions has been designed and synthesized to enforce this "REDOR-taxol" conformation. The novel taxoid SB-T-2053 inhibits the growth of MCF-7 and LCC-6 human breast cancer cells (wild-type and drug resistant) on the same order of magnitude as paclitaxel. Moreover, SB-T-2053 induces in vitro tubulin polymerization at least as well as paclitaxel, which directly validates our drug design process. These results open a new avenue for drug design of next generation taxoids and other microtubule-stabilizing agents based on the refined structural information of drug-tubulin complexes, in accordance with typical enzyme-inhibitor medicinal chemistry precepts.     

Combining dyad protonation and active site plasticity in BACE-1 structure-based drug design

The ability of the BACE-1 catalytic dyad to adopt multiple protonation states and the conformational flexibility of the active site have hampered the reliability of computational screening campaigns carried out on this drug target for Alzheimer's disease. Here, we propose a protocol that, for the first time, combining quantum mechanical calculations, molecular dynamics, and conformational ensemble virtual ligand screening addresses these issues simultaneously. The encouraging results prefigure this approach as a valuable tool for future drug discovery campaigns.     

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.     

Expanded interaction fingerprint method for analyzing ligand binding modes in docking and structure-based drug design

An expanded interaction fingerprint method has been developed for analyzing the binding modes of ligands in docking and structure-based design methods. Taking the basic premise of representing a ligand in terms of a binary string that denotes its interactions with a target protein, we have expanded the method to include additional interaction-specific information. By considering the hydrogen-bonding strength and/or accessibility of the hydrogen bonding groups within a binding site as well as their geometric arrangement we aim to provide a better representation of a ligand-protein interaction. These expanded methods have been applied to the postprocessing of binding poses generated in a docking study for 220 different proteins and to the analysis of ligands generated by an automated ligand-generation algorithm for the anthrax oedema factor. In the docking study, the application of the interaction fingerprint method as a postprocessing tool resulted in an increased success rate in identifying the crystallographic binding mode. In the analysis of the ligands generated for the anthrax oedema factor, the incorporation of additional interaction-specific information resulted in a more intuitive and comprehensive analysis of automated ligand-generation output.     

Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.     

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.