Drug Discovery by Polypharmacology- Based Interaction Profiling

Zoltán Simon Drug Discovery by Polypharmacology- based Interaction Profiling PhD thesis Supervisor: András Málnási-Csizmadia DSc. Structural Biochemistry Doctoral Program Doctoral School in Biology Program Leader: Prof. László Gráf DSc. Head of the School: Prof. Anna Erdei DSc. Eötvös Loránd University Budapest, Hungary 2010 A tudomány és művészet hazája nem a lét, az „esse”, hanem a lehetőség, a „posse”, s ha a létben megnyilvánul, attól a lét lesz gazdagabb; a tudomány és művészet részéről végtelen alázat, hogy a létben magát megnyilatkoztatni engedi, hiszen minden alakot-öltése fogyatékos. WE ÖRES SÁNDOR Preface Since the first Venus statues carved by the prehistoric man, artists attempt to reach the divine and bring it from the heaven down to the earth, to the community. Similarly, science aspires to get acquainted with the phenomena of the world and to understand their nature. As no artist has ever been able to capture and artificially reproduce the ultimate Beauty, scientists have never found the Truth in its entirety. Art and science, as stated by Sándor Weöres in its work entitled Towards completeness, are necessarily incomplete and deficient in our world. Nevertheless, science struggles to understand the complex nexus of things for several thousand years. Recent methodical improvements, especially the rapidly increasing computational capacity and mathematical systems theory enable us to find answers or at least approximations for complex questions in a new, complex manner. Prediction of the behavior of drug molecules in the human body is one of the most complex questions. It is not surprising that pharmacology has not yet solved the problem of capturing the entire bioactivity profiles of small-molecule compounds. Consequently, major side effects might remain hidden and endanger the patients‟ health – or, on the other hand, potentially beneficent alternative uses of drugs were rarely recognized. The early pharmacologic theories explained drug-effect associations in a mechanistic manner, implying that a drug selectively acts on a specific biological target and affects its function like a magic bullet (Ehrlich). Now the tide seems to turn: drugs are recognized as affecters of complex biological networks. Systems-based approaches are gathering larger and larger ground, bringing a holistic view into drug research and giving an opportunity to reveal the full effect profiles of drugs. 1 A similar shift of scientific viewpoint occurred in a much smaller but not simpler system: a protein. In Emil Fischer‟s time, proteins were considered as static objects whose interactions with other molecules could be described as the interaction of two complementary shapes, a key and a lock. Now, proteins ceased to be purely mechanical objects; they become dynamic, “breathing” particles. In contrast with the “molecular machinery” approach, it is now revealed that proteins show more flexibility than anything engineered by a human being. The Department of Biochemistry (and its ancestor), where my PhD work was carried out, has a great tradition of the paradigm of flexibility and the handling of complex problems, from Albert Szent-Györgyi to the present work of László Gráf [1]. In my thesis, I assess these two levels of complexity. First, I present a newly developed approach that attempts to predict hidden effects on known drugs. I also show that protein dynamics can be approached with internal viscosity, a specific measure of protein flexibility in an interdomain conformational rearrangement. The two topics are related not only in complexity but also in the similar treatment needed to process them. In pharmacological effect prediction, complex drug-protein interaction patterns and bioactivity profiles must be handled that mean an enormous amount of information. Dimension reduction and capturing of the important factors are the keys to solve the main problems of pharmacology. On the other hand, a single protein possesses an almost infinite complexity. For example, human trypsin 4 contains 216 amino acid residues. Consider only two conformers for each residue – this will result in 2216 possible conformations of this single protein [2]. This practically infinite conformational space makes it impossible to understand protein flexibility; dimension reduction is needed to find the smaller system in which flexibility can be studied. The activation of a human trypsin isoform, fine-tuned by a single residue at a specific position in the protein, offers us an ideal model system. Therefore, my thesis consists of two parts. In the first part, I present the holistic approach which lead to the development of Drug Profile Matching method which is able to systematically capture the bioactivity profiles of drug molecules in their entirety. I give a short overview of the different branches of in silico pharmacology, highlighting their advantages and disadvantages. After that, I present a recent paradigm called polypharmacology, i.e., the observation that many drugs affect multiple targets. Polypharmacology can bring the long-awaited breakthrough in drug discovery: it is a key to understand and catch the full spectrum of pharmacological actions of a compound in the human body. I review the latest attempts to exploit polypharmacology in bioactivity prediction and protein binding site description. Then, I present our starting hypothesis that 2 interaction profiles of drugs, even if generated in silico, correlate with their bioactivity profiles. I present different ways of effect prediction based on polypharmacology: a one- dimensional method and a more sophisticated, multidimensional one, the so-called Drug Profile Matching. Both approaches justify our starting hypothesis and are able to predict full effect profiles of drugs with high confidence. I point to our secondary finding that binding site geometry plays a minor role in the determination of affinity profiles in general; however, there are certain drug categories for which binding site shape is a crucial feature. I prove that in silico interaction profiles serve sufficient information for reliable bioactivity prediction without the consideration of the interactions of drugs with known targets in vivo. Results of in-house developed and independently performed in vitro and cell culture tests of certain effect predictions will be discussed. I summarize the recent and the possible future applications of Drug Profile Matching, i.e., drug repositioning predictions and bioactivity prediction of drug candidates, respectively. In the second part, I describe the study of the effect of point mutations on the rate of a specific conformational rearrangement. Mutations were introduced in a hinge region playing a major role in the activation of human trypsin 4. I prove that the rate of the conformational transition of the trypsin mutants is inversely proportional to the solvent viscosity. This phenomenon is interpreted in terms of the Kramers‟ theory. I conclude that the rate of the conformational change during activation is determined by the internal viscosity around this hinge site and the flexibility of a protein regarding this specific conformational transition can be affected by point mutations at the hinge region. This work is the first study that points to the effects of internal friction on the energy barrier of an enzymatic transformation. Since a new methodology was needed to study enzymatic reactions in a wide temperature range, we developed and applied a novel transient kinetic equipment called heat-jump/stopped flow. Based on our recent experiments, we propose that friction compresses the complex features of an enzymatic reaction, i.e., the inherent flexibility of a protein and the roughness of the potential energy landscape, into a one-dimensional parameter, the internal viscosity. In summary, my PhD work comprises the issues of multiparameter systems and their common methodical problem, i.e., the experimental selection of the relevant features. 3 Contents Preface 1 Contents 4 Acknowledgements 8 Abbreviations 10 Glossary 11 Part I: Drug Discovery by Polypharmacology-based Interaction Profiling 13 Introduction 13 1. Drug development: an overview 13 1.1 Current scientific problems 15 1.2 Current problems from the viewpoint of the pharmacological industry 17 2. In silico pharmacology 19 2.1 Quantitative Structure-Activity Relationships 20 2.2 Virtual Ligand Screening 20 2.2.1 Ligand-based VLS strategies 21 2.2.2 Target-based VLS strategies 21 2.3 Virtual Affinity Profiling 24 2.3.1 Ligand-based VAP strategies 24 2.3.2 Target-based VAP strategies 28 2.4 In silico pharmacology: a short summary 29 3. Polypharmacology 30 4. Protein binding site description 37 5. Starting hypothesis and applied methodology 38 5.1 Risk analysis of the initial hypothesis 40 Methods 41 1. Development of the Interaction Profile database 41 1.1 Data Collection 41 1.2 Docking Preparations 41 1.3 Docking 42 1.4 Filtering, normalization and centralization 43 2. Generation of the Effect Profile matrix 43 3. Generation of PocketPicker shape descriptor matrix 44 4 4. One-dimensional analyses assessing the relation between binding affinity patterns and bioactivity profiles 46 4.1 IP-based Drug-Drug Similarity Calculations 46 4.2 Validation of effect prediction 46 4.2.1 Randomization of the effect dataset 47 4.2.2 Leave-one-out cross-validation 47 4.3 Effect Prediction 48 4.3.1 Neighbor-focused Prediction Method 48 4.3.2 Effect-focused Prediction Method 48 5. Multidimensional analyses 49 5.1 Principal Component Analysis 49 5.2 Canonical

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