Protein Structure Network-based Drug Design.

Infectious diseases, caused by bacteria, viruses, fungi, protozoa, or parasites are among the leading causes of death worldwide. The efficacy of all current anti-infectives is threatened by the spread of drug resistance factors, with some already made ineffective, and, as a result, there is a pressing need for a new pipeline of robust anti-infective drugs. In silico approaches, such as virtual high throughput screening and de novo structure-based rational drug design, have been established as powerful tools in drug discovery. In this review, we explore the exciting opportunities for antimalarial, antiviral, and antibacterial drug discovery arising from the new paradigm of structure-based drug design.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

Computational approaches are widely used to help discover and develop new drugs, in particular, to understand how these molecules interact with their biomolecular targets. There are well-optimised and validated force field parameters available to describe interactions of common biomolecules, such as proteins, lipids and nucleotides. However, these force fields are not designed to represent heteromolecular ligands such as substrates, inhibitors, co-factors and potential drug molecules. Errors in parameters may result in incorrect predictions of ligand structure, orientation and conformation, which in turn can lead to the failure of computational drug design efforts. With over 25% of all the structures in the Protein Data Bank (PDB) containing ligands and over a million ligand molecules of potential interest in drug design in other databases, ligand validation and parameterisation represents a significant scientific challenge. While several automated parameterisation protocols have been proposed to generate the parameters, none of the current procedures are properly validated. As a response to the high demand for interaction parameters for ligands compatible with the GROMOS force field, a web accessible Automated force field Topology Builder (ATB; http://atb.uq.edu.au/) and Repository was developed. The ATB and Repository is intended to facilitate the development of molecular force fields and generates parameters that can be used in X-ray refinement, structure-based drug design and study of biomolecule-ligand complexes. In this thesis, molecular dynamics (MD) simulations were used to calculate the thermodynamic properties for the validation and refinement of ATB force fields parameters. A fully automated validation protocol of the ATB force field parameters for small organic molecules based on thermodynamic and structural information was developed and incorporated into the ATB. A novel integration and convergence protocol which increases the efficiency of the TI method for free energy calculations was also proposed. The validation has shown good overall agreement between the experimental and computed values and indicated problematic functional groups with parameters to be refined. The generation of parameters for novel molecules that are compatible with a given biomolecular force field can be tedious, time-consuming and error-prone. Here, a novel method to refine parameters in classical force fields in an automated manner that can be extended to the parameterisation of all other atom types is also presented. Single-step perturbation protocols were developed for small halogenated molecules to establish alternative van der Waals parameters which describe the interactions of these molecules with high precision. Parameters were successfully refined against experimental hydration free energies, densities and heats of vaporisation. As a result, alternative van der Waals interactions parameters for chlorine and bromine were proposed. Properties calculated with these proposed parameters closely matched the experiment data. The quality of the ATB force field parameters in validating the X-ray structures and investigating binding of ligands to the protein endothiapepsin was also investigated. Analysis of the MD trajectories showed that the protein structure remained relatively stable after 10 ns of simulations. The results for the validation of the ligands were mixed. While in some cases the simulations reproduced the binding mode and the interactions between the protein and the ligand with high accuracy suggesting the ATB parameters performed well, in other cases the complexes were unstable. This was despite the fact that the electron density for all ligands was well defined and the parameters for the ligand were generated using the same procedure. Endothiapepsin contains a catalytic dyad consisting of two aspartates that are generally assumed to share a proton. We demonstrated that the protonation of both the ligand and the residues with the binding site were critical to the ability to reproduce the crystal complex. To conclude, the work presented in this thesis provided insight into the development and validation of the force field parameters for drug-like molecules and their applications in structure-based drug design.

With the advancement of "proteomics" data and systems biology, new techniques are needed to meet the new era of drug discovery. Network theory is increasingly applied to describe complex biological systems, thus implying its essential roles in system-based drug design. In this review, we first summarized general network parameters used in describing biological systems, and then gave some recent applications of these network parameters as topological indices in drug design in terms of Protein Structure Networks (PSNs), Protein-Protein Interaction Networks (PPINs) including related structural PPINs, and Elastic Network Models (ENMs). These network models have enabled the development of new drugs relying on allosteric effects, describing anti-cancer targets, targeting hot spots and key proteins at the protein-protein interfaces and PPINs, and helped drug design by modulating conformational flexibility. Accordingly, we highlighted the integration of network models bringing new paradigms into the next-generation target-based drug discovery.

INTRODUCTION. Malignant glioma is one of the most lethal diseases in adulthood and remains refractory to the current therapies including chemoradiotherapies. The cancer stem cell theory dictates that brain tumor stem-like cells (BTSC) account for such intractability of malignant glioma. Recently, several molecularly-targeted therapies have demonstrated a varying degree of success in treatment of cancers including leukemia and breast cancer. In this study, we sought to determine the key kinases that regulate survival, self-renewal, and proliferation of BTSC, and to design novel small molecules by using the structure-based computantional modeling system. RESULTS. With the neurosphere cultures derived from our glioma patients, we identified 3 kinases that play vital roles in survival and/or proliferation of BTSC in vitro; Fibroblast Growth Factor Receptor 1, Aurora kinase B, and Maternal Embryonic Leucine-zipper Kinase. Structure-based computational modeling allowed us to perform in silico docking of various small molecule candidates to these kinases. As a result, four novel small molecules demonstrated highly specific targeting of the key structural elements of these kinases. We then synthesized these identified candidate compounds and confirmed their in vitro inhibition of the targeting kinase activities. Finally, we will also present our data about the treatment effect with these compounds on the in vitro BTSC growth and the in vivo tumor growth. CONCLUSION. Structure-based drug design will likely open up a new avenue to discover novel anti-cancer stem cell agents that can selectively target the key kinases in BTSC, leading to the growth arrest of malignant glioma. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3298. doi:10.1158/1538-7445.AM2011-3298

Structure-based drug design is emerging as one of the key components in drug discovery, with many approved drugs tracing, at least part of their origins, to the use of structural information from X-ray, NMR, surface plasmon resonance, differential thermal denaturation, fluorescence polarization and other techniques for analysis of protein targets and their ligand-bound complexes. Furthermore, in silico structure-based drug design approach has enabled millions of possible structures for a given protein sequence to be evaluated rapidly helping to fast forward drug discovery as well as reduce drug discovery costs. Structure-based drug design is now arguably an essential contributor to addressing the need to improve research and development productivity faced by the pharmaceutical industry. The purpose of this meeting is to highlight the impact of the intersection of structural biology with chemistry and biology particularly on how the structures of relevant drug targets can serve as a starting point for drug design and development and provide the maximal synergy between target validation, structure determination, and hit-to-lead development. Some thoughts will be proposed with regard to the future of structure-based drug design and where emphasis could be placed to further increase the utilization of this approach on drug discovery.

Structure-based design and optimization of potent renin inhibitors on 5- or 7-azaindole-scaffolds

Structure-based drug design has played a role in the discovery of many drugs from many different disease areas, and it is now arguably an essential contributor to addressing the need to improve research and development productivity faced by the pharmaceutical industry. The purpose of this review is to highlight the impact of protein structures solved by X-ray and NMR techniques, as well as other techniques such as isothermal titration calorimetry, on structure-based discovery. The importance of these methods in drug discovery will be underscored by examples of drugs approved up until the end of 2009, which were discovered utilising structural information. These examples will highlight that structure-based drug design has moved on from just using structural knowledge to optimise potency to using it also with selectivity, pharmacokinetic and pharmaceutical properties in mind. Following on from this, a medicinal chemist's perspective aims to highlight the benefits and advantages of structure-based drug design but also to provide an opinion on the pitfalls and hype surrounding this technology. In particular, this section will draw on examples to highlight its impact and limitations on assessing druggability, as well as factors to consider when using structure-based drug design in lead generation and the optimisation of leads for potency, selectivity and pharmacokinetic properties. Finally, some forward looking thoughts will be proposed with regard to the future of structure-based drug design and where emphasis could be placed to increase the impact of this approach on drug discovery productivity.

Allosteric modulators, by targeting the less-conserved allosteric sites, represent an innovative strategy in drug discovery. These modulators have a distinctive advantage over orthosteric ligands that attach to the conserved, functional orthosteric sites. However, in structure-based drug design, it remains unclear whether allosteric protein structures determined without orthosteric ligand binding are suitable for allosteric drug screening. In this study, we performed large-scale conformational samplings of six representative allosteric proteins uncomplexed ( apo) and complexed ( holo) with orthosteric ligands to explore the effect of orthosteric site binding on the conformational dynamics of allosteric sites. The results, coupled with the redocking evaluation of allosteric modulators to their apo and holo proteins using their MD trajectories, indicated that orthosteric site binding had an effect on the dynamics of the allosteric sites and allosteric modulators preferentially bound to their holo proteins. According to the analysis data, we constructed a new correlation model for quantifying the allosteric site change driven by substrate binding to the orthosteric site. These results highlight the strong demand to select holo allosteric proteins as initial inputs in structure-based allosteric drug screening when the distance between orthosteric and allosteric sites in the protein is below 5 Å, which is expected to contribute to allosteric drug discovery.

Structure-based drug design

We have developed a receptor-based pharmacophore method which utilizes a collection of protein structures to account for inherent protein flexibility in structure-based drug design. Several procedures were systematically evaluated to derive the most general protocol for using multiple protein structures. Most notably, incorporating more protein flexibility improved the performance of the method. The pharmacophore models successfully discriminate known inhibitors from drug-like non-inhibitors. Furthermore, the models correctly identify the bound conformations of some ligands. We used unliganded HIV-1 protease to develop and validate this method. Drug design is always initiated with a protein-ligand structure, and such success with unbound protein structures is remarkable - particularly in the case of HIV-1 protease, which has a large conformational change upon binding. This technique holds the promise of successful computer-based drug design before bound crystal structures are even discovered, which can mean a jump-start of 1-3 years in tackling some medically relevant systems with computational methods.

Modern drug discovery relies heavily on a detailed understanding of the nature of complexes formed and the thermodynamics associated with the binding of a ligand to a target protein. Despite this, studies have suggested that up to 70% of ligands in the Protein Data Bank were refined using inappropriate geometric restraints and in 25% of cases, the specific interactions claimed to be associated with binding may be misleading. The problem is that current refinement and validation procedures do not properly account for alternative protonation and tautomeric states, nor the uncertainties in the stereochemistry, orientation and/or conformation of the bound ligand. This thesis addresses the question of whether computational approaches can be used to identify and address problems in protein:ligand complexes and the extent to which free energy calculations can be used to validate the parameters used in computational drug design. Using a series of high-quality co-crystal structures of the aspartic protease Endothiapepsin, Chapter 2 examines whether it is possible to discriminate between alternative protonation states of the ligands and the catalytic dyad using all-atom refinement that incorporates the electrostatic terms. In Chapter 3, molecular dynamic simulations are employed using the same system to examine a range of alternative complexed states to determine which of these states were thermodynamically most stable and thus potentially physically the most realistic. The interaction of different tautomeric forms of simple ligands bound to Trypsin is examined in Chapter 4. Ultimately, to predict how a drug binds to a target protein we must be able to determine the binding free energy with high accuracy. Despite a rigorous theoretical framework and the potential for high accuracy calculations, free energy perturbation or integration methods have not found routine use in structure-based drug design. One of the main challenges is transferring methodology that has been optimised for small, generally rigid ligands carrying a single charged group to that of larger (> 40 atoms), flexible ligands containing a range of diverse chemical functionality; the types of molecules increasingly of interest in structure-based drug design. The final chapter (Chapter 5) of my thesis will investigate a range of protocols involving single and dual topologies, various soft-core potentials and restraints. With the desire to develop automated high throughput protocols that can efficiently exploit advances in hardware. A dataset used by OPLS and AMBER is employed to validate the methodology.

The use of structure in drug design has become widespread, mainly thanks to recent advances in crystallography. Nevertheless, biological macromolecules are intrinsically flexible and it is increasingly evident that their function depends critically on both their structure and dynamics. In this review the authors discuss the implications of protein flexibility for drug design and review recent progress in incorporating protein flexibility into docking and structure-based drug design.

Parasite diseases are a huge burden on human health causing significant morbidity and mortality. However, parasite structure based drug discovery programmes have been hindered by a lack of high resolution structural information from parasite derived proteins and have often relied upon homology models from mammalian systems. The recent renaissance in electron microscopy (EM) has caused a dramatic rise in the number of structures being determined at high resolution and subsequently enabled it to be thought of as a tool in drug discovery. In this review, we discuss the challenges associated with the structural determination of parasite proteins including the difficulties in obtaining sufficient quantities of protein. We then discuss the reasons behind the resurgence in EM, how it may overcome some of these challenges and provide examples of EM derived parasite protein structures. Finally, we discuss the challenges which EM needs to overcome before it is used as a mainstream technique in anti-parasite drug discovery. This review reports the progress that has been made in obtaining sufficient quantities of proteins for structural studies and the role EM may play in future structure based drug design programs. The outlook for future structure based drug design programs against some of the most devastating parasite diseases looks promising.

Structure-based design has emerged as a new tool in medicinal chemistry. A prerequisite for this new approach is an understanding of the principles of molecular recognition in protein-ligand complexes. If the three-dimensional structure of a given protein is known, this information can be directly exploited for the retrieval and design of new ligands. Structure-based ligand design is an iterative approach. First of all, it requires the crystal structure or a model derived from the crystal structure of a closely related homolog of the target protein, preferentially complexed with a ligand. This complex unravels the binding mode and conformation of a ligand under investigation and indicates the essential aspects determining its binding affinity. It is then used to generate new ideas about ways of improving an existing ligand or of developing new alternative bonding skeletons. Computational methods supplemented by molecular graphics are applied to assist this step of hypothesis generation. The features of the protein binding pocket can be translated into queries used for virtual computer screening of large compound libraries or to design novel ligands de novo. These initial proposals must be confirmed experimentally. Subsequently they are optimized toward higher affinity and better selectivity. The latter aspect is of utmost importance in defining and controlling the pharmacological profile of a ligand. A prerequisite to tailoring selectivity by rational design is a detailed understanding of molecular parameters determining selectivity. Taking examples from current drug development programs (HIV proteinase, t-RNA transglycosylase, thymidylate synthase, thrombin and, related serine proteinases), we describe recent advances in lead discovery via computer screening, iterative design, and understanding of selectivity discrimination.