DOCKING METHODS, LIGAND DESIGN, AND VALIDATING DATA SETS IN THE STRUCTURAL GENOMICS ERA

27 DOCKING METHODS, LIGAND DESIGN, AND VALIDATING DATA SETS IN THE STRUCTURAL GENOMICS ERA Natasja Brooijmans INTRODUCTION Since the beginning of civ...
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27 DOCKING METHODS, LIGAND DESIGN, AND VALIDATING DATA SETS IN THE STRUCTURAL GENOMICS ERA Natasja Brooijmans

INTRODUCTION Since the beginning of civilization, humans have searched for substances that can cure or alleviate the symptoms of disease. In the early stages, extracts from plants and animal parts were used to treat disease, and the discovery of such remedies was driven empirically. Starting in the early 1900s, drug discovery has increasingly focused on discovering and developing chemical entities that on their own have a desired pharmaceutical effect. Initially this was fueled by attempts to extract and identify the active component in extracts from natural products that were used. A number of developments, however, have resulted in the multidisciplinary science that drug discovery is today, including the traditional fields of chemistry and pharmacology, along with contributions from biochemistry, molecular biology, and biophysics. Increasingly, computational tools are used in the drug discovery process from target identification and validation to the designing of new molecules. The discoveries of several different concepts played a pivotal role in the development of drug discovery as an industry. In the nineteenth century, Paul Ehrlich developed the idea that different parasites, microorganisms, and cancer cells have dissimilar chemoreceptors resulting in different susceptibility to dyes. He hypothesized that these differences can be exploited therapeutically (Ehrlich, 1957). In 1905, Langley expanded the concept of receptors to being binding sites for different substances. Binding of the substrate to the binding site can result in either stimulation or blocking of the receptor depending on the substance used (Langley, 1905). Emil Fisher developed the ‘‘lock-and-key’’ concept for enzymes, which stipulates that the substrate has to fit exactly into the binding site for the enzyme to perform its function on Structural Bioinformatics, Second Edition Edited by Jenny Gu and Philip E. Bourne Copyright  2009 John Wiley & Sons, Inc.

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the substrate (Fischer, 1894). Koshland put a slightly more flexible point of view forward in the ‘‘induced-fit’’ model that states that both ligand and enzyme undergo conformational changes to fit optimally (Koshland, 1958). More recently, Foote and Milstein put forward the idea that optimally fitting conformers of both receptor and ligand exist in solution, and that in the presence of each other, there is a shift in the equilibrium toward the best-fitting conformers for both, resulting in binding (Foote and Milstein, 1994). The field of structure-based drug design (Chapter 34) most explicitly exploits the concept of three-dimensional binding sites that interact with ligands; computational methods and models play herein a critical role (Figure 27.1). In structure-based design, the known or predicted shape of the binding site is used to optimize the ligand to best fit the receptor. The driving forces of these specific interactions in biological systems are driven by complementarities in both shape and electrostatics of the binding site surfaces and the ligand or substrate. Van der Waals interactions thus play a role, in addition to Coulombic interactions and the formation of hydrogen bonds (Figure 27.2). Docking is one of the tools used to understand and explore the steric and electrostatic complementarity between the receptor and the ligand. The interactions between the receptor and ligand are quantum mechanical in nature, but due to the complexity of biological systems, quantum theory cannot be applied directly. Consequently, most methods used in docking and computational drug discovery are more empirical in nature and usually lack generality. Quantum mechanical phenomena, such as the formation of a covalent bond between the protein and the ligand upon binding during the transition state of the reaction, cannot be predicted and/or evaluated using these empirical methods.

Figure 27.1. Structure-based drug discovery cycle. Either an X-ray structure or a homology model can be used for binding mode predictions of known leads and virtual screening to find new leads. The known or predicted binding mode of leads can be used to design analogues with better and more interactions with the protein. Prioritized analogues have to be synthesized, experimentally tested, and the structure–activity relationships obtained can be used to further optimize the ligands.

INTRO DUCTION

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Figure 27.2. Streptavidin bound to biotin. Picture highlighting interactions between streptavidin (orange carbon atoms) and biotin. Amino acid residues that interact through hydrogen bonds with the ligand are shown with cyan-colored carbons, residues providing hydrophobic interactions have gray carbons. Figure also appears in Color Figure section.

The strength of the interaction between the ligand and the receptor can be measured experimentally and is often reported as the dissociation constant, Kd, or by the concentration of ligand that inhibits activity by 50%, the IC50. The binding free energy is the thermodynamic quantity that is of interest in computational structure-based design and is defined by Eq. 27.1: DGbind ¼ DGcomplex ðDGligand DGreceptor Þ:

ð27:1Þ

The relationship between the binding free energy DG and the experimentally determined Kd or IC50 is shown in Eq. 27.2: DGbind ¼ RT ln Keq ¼ RT ln Kd ¼ RT ln 1=IC50 :

ð27:2Þ

With Eq. 27.2, it is possible to calculate the binding free energy where the Kd is defined with respect to the standard state (Atkins, 1997), and measurements done in different laboratories can only be compared when performed under the same standard state, that is, experimental conditions. The pressure, 1 atm, and the activity of the solutions, namely 1 M, define standard state conditions. Modern drug discovery cycle usually starts with the identification of a biological target (Figure 27.3), most often a protein, known to play a critical role in a particular disease. Biological assays are developed that can measure inhibition (or activation) of the target of interest by small molecules in vitro or in vivo. If amenable to high-throughput screening, pharmaceutical companies usually screen their entire corporate collection of molecules in the assay against the target to identify possible leads. Often several hundred thousand molecules are tested in the high-throughput screening (HTS). During HTS each compound is generally tested once at a single dose yielding a percent inhibition value. Numerous hits are identified, based on statistical significance of the measured percent inhibition, and confirmed in subsequent assays. Follow-up tests include dose–response assays and confirmation that

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Figure 27.3. Modern drug discovery cycle. Modern drug discovery starts with identifying potential targets that play a role in disease. The target has to be validated, for example through gene-chip analyses, siRNA experiments, gene knockout experiments, and so on. Once the target has been validated, a biological assay has to be developed that can be used to identify potential leads. After lead identification through screening, hits are confirmed by reassaying, IC50 and Kd determinations, NMR experiments that measure protein binding of the hits, analogue testing, and so on. Once hits have been confirmed, analogues are synthesized to further optimize the lead series for potency and ADME/Tox properties.

the compounds inhibit the target by a specific mechanism rather than through nonspecific binding (McGovern et al., 2003). The HTS liquid samples of the hits are also tested on purity and integrity to ensure that the measured activity is due to the structure assumed to be in the sample. As the number of hits gets smaller, similarity searches will usually be performed and similar structures will be tested for activity. Analogue testing is a way to develop structure–activity relationships (SAR) early on and further validate the hits. The hits are narrowed to a limited number of different chemical lead series (usually

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