Dynamics of protein-drug interactions inferred from structural ensembles and physics-based models

The conformational flexibility of target proteins is a major challenge in understanding and modeling protein-drug interactions. A fundamental issue, yet to be clarified, is whether the observed conformational changes are controlled by the protein, or induced by the inhibitor. While the concept of induced fit has been widely adopted for describing the structural changes that accompany ligand binding, there is growing evidence in support of the dominance of proteins' intrinsic dynamics, which has been evolutionarily optimized to accommodate its functional interactions. The wealth of structural data for target proteins in the presence of different ligands now permits us to make a critical assessment of the balance between these two effects in selecting the bound forms. We focused on three widely studied drug targets, HIV-1 reverse transcriptase, p38 MAP kinase, and cyclin-dependent kinase 2. A total of 292 structures determined for these enzymes in the presence of different inhibitors as well as unbound form permitted us to perform an extensive comparative analysis of the conformational space accessed upon ligand binding, and its relation to the intrinsic dynamics prior to ligand binding as predicted by elastic network model analysis. Further, we analyzed NMR ensembles of ubiquitin and calmodulin representing their microseconds range solution dynamics. Our results show that the ligand selects the conformer that best matches its structural and dynamic properties amongst the conformers intrinsically accessible to the protein in the unliganded form. The results suggest that simple but robust rules encoded in the protein structure play a dominant role in pre-defining the mechanisms of ligand binding, which may be advantageously exploited in designing inhibitors. We apply these lessons to the study of MAP kinase phosphatases (MKPs), which are therapeutically relevant but challenging signaling enzymes. Our study provides insights into the interactions and selectivity of MKP inhibitors and shows how an allosteric inhibition mechanism holds for a recently discovered inhibitor of MKP-3. We also provide evidence for the functional significance of the structure-encoded dynamics of rhodopsin and nicotinic acetylcholine receptor, members of two membrane proteins classes serving as targets for more than 40% of all current FDA approved drugs

Efficient comprehensive scoring of docked proteincomplexes - a machine learning approach

Biological systems and processes rely on a complex network of molecular interactions. The association of biological macromolecules is a fundamental biochemical phenomenon and an unsolved theoretical problem crucial for the understanding of complex living systems. The term protein-protein docking describes the computational prediction of the assembly of protein complexes from the individual subunits. Docking algorithms generally produce a large number of putative protein complexes. In most cases, some of these conformations resemble the native complex structure within an acceptable degree of structural similarity. A major challenge in the field of docking is to extract the near-native structure(s) out of this considerably large pool of solutions, the so called scoring or ranking problem. It has been the aim of this work to develop methods for the efficient and accurate detection of near-native conformations in the scoring or ranking process of docked protein-protein complexes. A series of structural, chemical, biological and physical properties are used in this work to score docked protein-protein complexes. These properties include specialised energy functions, evolutionary relationship, class specific residue interface propensities, gap volume, buried surface area, empiric pair potentials on residue and atom level as well as measures for the tightness of fit. Efficient comprehensive scoring functions have been developed using probabilistic Support Vector Machines in combination with this array of properties on the largest currently available protein-protein docking benchmark. The established scoring functions are shown to be specific for certain types of protein-protein complexes and are able to detect near-native complex conformations from large sets of decoys with high sensitivity. The specific complex classes are Enzyme-Inhibitor/Substrate complexes, Antibody-Antigen complexes and a third class denoted as "Other" complexes which holds all test cases not belonging to either of the two previous classes. The three complex class specific scoring functions were tested on the docking results of 99 complexes in their unbound form for the above mentioned categories. Defining success as scoring a 'true' result with a p-value of better than 0.1, the scoring schemes were found to be successful in 93%, 78% and 63% of the examined cases, respectively. The ranking of near-native structures can be drastically improved, leading to a significant enrichment of near-native complex conformations in the top ranks. It could be shown that the developed scoring schemes outperform five other previously published scoring functions

Chemoinformatics-Driven Approaches for Kinase Drug Discovery

Given their importance for the majority of cell physiology processes, protein kinases are among the most extensively studied protein targets in drug discovery. Inappropriate regulation of their basal levels results in pathophysiological disorders. In this regard, small-molecule inhibitors of human kinome have been developed to treat these conditions effectively and improve the survival rates and life quality of patients. In recent years, kinase-related data has become increasingly available in the public domain. These large amounts of data provide a rich knowledge source for the computational studies of kinase drug discovery concepts. This thesis aims to systematically explore properties of kinase inhibitors on the basis of publicly available data. Hence, an established "selectivity versus promiscuity" conundrum of kinase inhibitors is evaluated, close structural analogs with diverging promiscuity levels are analyzed, and machine learning is employed to classify different kinase inhibitor binding modes. In the first study, kinase inhibitor selectivity trends are explored on the kinase pair level where kinase structural features and phylogenetic relationships are used to explain the obtained selectivity information. Next, selectivity of clinical kinase inhibitors is inspected on the basis of cell-based profiling campaign results to consolidate the previous findings. Further, clinical candidates are mapped to medicinal chemistry sources and promiscuity levels of different inhibitor subsets are estimated, including designated chemical probes. Additionally, chemical probe analysis is extended to expert-curated representatives to correlate the views established by scientific community and evaluate their potential for chemical biology applications. Then, large-scale promiscuity analysis of kinase inhibitor data combining several public repositories is performed to subsequently explore promiscuity cliffs (PCs) and PC pathways and study structure-promiscuity relationships. Furthermore, an automated extraction protocol prioritizing the most informative pathways is proposed with focus on those containing promiscuity hubs. In addition, the generated promiscuity data structures including cliffs, pathways, and hubs are discussed for their potential in experimental and computational follow-ups and subsequently made publicly available. Finally, machine learning methods are used to develop classification models of kinase inhibitors with distinct experimental binding modes and their potential for the development of novel therapeutics is assessed

Structural Cheminformatics for Kinase-Centric Drug Design

Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Computationally Linking Chemical Exposure to Molecular Effects with Complex Data: Comparing Methods to Disentangle Chemical Drivers in Environmental Mixtures and Knowledge-based Deep Learning for Predictions in Environmental Toxicology

Chemical exposures affect the environment and may lead to adverse outcomes in its organisms. Omics-based approaches, like standardised microarray experiments, have expanded the toolbox to monitor the distribution of chemicals and assess the risk to organisms in the environment. The resulting complex data have extended the scope of toxicological knowledge bases and published literature. A plethora of computational approaches have been applied in environmental toxicology considering systems biology and data integration. Still, the complexity of environmental and biological systems given in data challenges investigations of exposure-related effects. This thesis aimed at computationally linking chemical exposure to biological effects on the molecular level considering sources of complex environmental data. The first study employed data of an omics-based exposure study considering mixture effects in a freshwater environment. We compared three data-driven analyses in their suitability to disentangle mixture effects of chemical exposures to biological effects and their reliability in attributing potentially adverse outcomes to chemical drivers with toxicological databases on gene and pathway levels. Differential gene expression analysis and a network inference approach resulted in toxicologically meaningful outcomes and uncovered individual chemical effects — stand-alone and in combination. We developed an integrative computational strategy to harvest exposure-related gene associations from environmental samples considering mixtures of lowly concentrated compounds. The applied approaches allowed assessing the hazard of chemicals more systematically with correlation-based compound groups. This dissertation presents another achievement toward a data-driven hypothesis generation for molecular exposure effects. The approach combined text-mining and deep learning. The study was entirely data-driven and involved state-of-the-art computational methods of artificial intelligence. We employed literature-based relational data and curated toxicological knowledge to predict chemical-biomolecule interactions. A word embedding neural network with a subsequent feed-forward network was implemented. Data augmentation and recurrent neural networks were beneficial for training with curated toxicological knowledge. The trained models reached accuracies of up to 94% for unseen test data of the employed knowledge base. However, we could not reliably confirm known chemical-gene interactions across selected data sources. Still, the predictive models might derive unknown information from toxicological knowledge sources, like literature, databases or omics-based exposure studies. Thus, the deep learning models might allow predicting hypotheses of exposure-related molecular effects. Both achievements of this dissertation might support the prioritisation of chemicals for testing and an intelligent selection of chemicals for monitoring in future exposure studies.:Table of Contents ... I Abstract ... V Acknowledgements ... VII Prelude ... IX 1 Introduction 1.1 An overview of environmental toxicology ... 2 1.1.1 Environmental toxicology ... 2 1.1.2 Chemicals in the environment ... 4 1.1.3 Systems biological perspectives in environmental toxicology ... 7 Computational toxicology ... 11 1.2.1 Omics-based approaches ... 12 1.2.2 Linking chemical exposure to transcriptional effects ... 14 1.2.3 Up-scaling from the gene level to higher biological organisation levels ... 19 1.2.4 Biomedical literature-based discovery ... 24 1.2.5 Deep learning with knowledge representation ... 27 1.3 Research question and approaches ... 29 2 Methods and Data ... 33 2.1 Linking environmental relevant mixture exposures to transcriptional effects ... 34 2.1.1 Exposure and microarray data ... 34 2.1.2 Preprocessing ... 35 2.1.3 Differential gene expression ... 37 2.1.4 Association rule mining ... 38 2.1.5 Weighted gene correlation network analysis ... 39 2.1.6 Method comparison ... 41 Predicting exposure-related effects on a molecular level ... 44 2.2.1 Input ... 44 2.2.2 Input preparation ... 47 2.2.3 Deep learning models ... 49 2.2.4 Toxicogenomic application ... 54 3 Method comparison to link complex stream water exposures to effects on the transcriptional level ... 57 3.1 Background and motivation ... 58 3.1.1 Workflow ... 61 3.2 Results ... 62 3.2.1 Data preprocessing ... 62 3.2.2 Differential gene expression analysis ... 67 3.2.3 Association rule mining ... 71 3.2.4 Network inference ... 78 3.2.5 Method comparison ... 84 3.2.6 Application case of method integration ... 87 3.3 Discussion ... 91 3.4 Conclusion ... 99 4 Deep learning prediction of chemical-biomolecule interactions ... 101 4.1 Motivation ... 102 4.1.1Workflow ...105 4.2 Results ... 107 4.2.1 Input preparation ... 107 4.2.2 Model selection ... 110 4.2.3 Model comparison ... 118 4.2.4 Toxicogenomic application ... 121 4.2.5 Horizontal augmentation without tail-padding ...123 4.2.6 Four-class problem formulation ... 124 4.2.7 Training with CTD data ... 125 4.3 Discussion ... 129 4.3.1 Transferring biomedical knowledge towards toxicology ... 129 4.3.2 Deep learning with biomedical knowledge representation ...133 4.3.3 Data integration ...136 4.4 Conclusion ... 141 5 Conclusion and Future perspectives ... 143 5.1 Conclusion ... 143 5.1.1 Investigating complex mixtures in the environment ... 144 5.1.2 Complex knowledge from literature and curated databases predict chemical- biomolecule interactions ... 145 5.1.3 Linking chemical exposure to biological effects by integrating CTD ... 146 5.2 Future perspectives ... 147 S1 Supplement Chapter 1 ... 153 S1.1 Example of an estrogen bioassay ... 154 S1.2 Types of mode of action ... 154 S1.3 The dogma of molecular biology ... 157 S1.4 Transcriptomics ... 159 S2 Supplement Chapter 3 ... 161 S3 Supplement Chapter 4 ... 175 S3.1 Hyperparameter tuning results ... 176 S3.2 Functional enrichment with predicted chemical-gene interactions and CTD reference pathway genesets ... 179 S3.3 Reduction of learning rate in a model with large word embedding vectors ... 183 S3.4 Horizontal augmentation without tail-padding ... 183 S3.5 Four-relationship classification ... 185 S3.6 Interpreting loss observations for SemMedDB trained models ... 187 List of Abbreviations ... i List of Figures ... vi List of Tables ... x Bibliography ... xii Curriculum scientiae ... xxxix Selbständigkeitserklärung ... xlii