Application of knowledge discovery and data mining methods in livestock genomics for hypothesis generation and identification of biomarker candidates influencing meat quality traits in pigs

Recent advancements in genomics and genome profiling technologies have lead to an increase in the amount of data available in livestock genomics. Yet, most of the studies done in livestock genomics have been following a reductionist approach and very few studies have either followed data mining or knowledge discovery concepts or made use of the wealth of information available in the public domain to gain new knowledge. The goals of this thesis were: (i) the adoption of existing analysis strategies or the development of novel approaches in livestock genomics for integrative data analysis following the principles of data mining and knowledge discovery and (ii) demonstrating the application of such approaches in livestockgenomics for hypothesis generation and biomarker discovery. A pig meat quality trait termed androstenone measurement in backfat was selected as the target phenotype for the experiments. Two experiments were performed as a part of this thesis. The first one followed a knowledge driven approach merging high-throughput expression data with metabolic interaction network. Based on the results from this experiment, several novel biomarker candidates and a hypothesis regarding different mechanisms regulating androstenone synthesis in porcine testis samples with divergent androstenone measurements in back fat were proposed. The model proposed that the elevated levels of androstenone synthesis in sample population could be due to the combined effect of cAMP/PKA signaling, elevated levels of fatty acid metabolism and anti lipid peroxidation activity of members of glutathione metabolic pathway. The second experiment followed a data driven approach and integrated gene expression data from multiple porcine populations to identify similarities in gene expression patterns related to hepatic androstenone metabolism. The results indicated that one of the low androstenone phenotype specific co-expression cluster was functionally enriched in pathways related to androgen and androstenone metabolism and that the members of this cluster exhibited weak co-expression in high androstenone phenotype. Based on the results from this experiment, this co-expression cluster was proposed as a signature cluster for hepatic androstenone metabolism in boars with low androstenone content in back fat. The results from these experiments indicate that integrative analysis approaches following data mining and knowledge discovery concepts can be used for the generation of new knowledge from existing data in livestock genomics. But, limited data availability in livestock genomics is a hindrance to the extensive use such analysis methods in livestock genomics field for gaining new knowledge. In conclusion, this study was aimed at demonstrating the capabilities of data mining and knowledge discovery methods and integrative analysis approaches to generate new knowledge in livestock genomics using existing datasets. The results from the experiments hint the possibilities of further exploring such methods for knowledge generation in this field. Although the application of such methods is limited in livestock genomics due to data availability issues at present, the increase in data availability due to evolving high throughput technologies and decrease in data generation costs would aid in the wide spread use of such methods in livestock genomics in the coming future

A new generation of user-friendly and machine learning-accelerated methods for protein pKa calculations

The ability to sense and react to external and internal pH changes is a survival requirement for any cell. pH homeostasis is tightly regulated, and even minor disruptions can severely impact cell metabolism, function, and survival. The pH dependence of proteins can be attributed to only 7 out of the 20 canonical amino acids, the titratable amino acids that can exchange protons with water in the usual 0-14 pH range. These amino acids make up for approximately 31% of all amino acids in the human proteome, meaning that, on average, roughly one-third of each protein is sensitive not only to the medium pH but also to alterations in the electrostatics of its surroundings. Unsurprisingly, protonation switches have been associated with a wide array of protein behaviors, including modulating the binding affinity in protein-protein, protein-ligand, or protein-lipid systems, modifying enzymatic activity and function, and even altering their stability and subcellular location. Despite its importance, our molecular understanding of pHdependent effects in proteins and other biomolecules is still very limited, particularly in big macromolecular complexes such as protein-protein or membrane protein systems. Over the years, several classes of methods have been developed to provide molecular insights into the protonation preference and dependence of biomolecules. Empirical methods offer cheap and competitive predictions for time- or resource-constrained situations. Albeit more computationally expensive, continuum electrostatics-based are a cost-effective solution for estimating microscopic equilibrium constants, pKhalf and macroscopic pKa. To study pH-dependent conformational transitions, constant-pH molecular dynamics (CpHMD) is the appropriate methodology. Unfortunately, given the computational cost and, in many cases, the difficulty associated with using CE-based and CpHMD, most researchers overuse empirical methods or neglect the effect of pH in their studies. Here, we address these issues by proposing multiple pKa predictor methods and tools with different levels of theory designed to be faster and accessible to more users. First, we introduced PypKa, a flexible tool to predict Poisson–Boltzmann/Monte Carlo-based (PB/MC) pKa values of titratable sites in proteins. It was validated with a large set of experimental values exhibiting a competitive performance. PypKa supports CPU parallel computing and can be used directly on proteins obtained from the Protein Data Bank (PDB) repository or molecular dynamics (MD) simulations. A simple, reusable, and extensible Python API is provided, allowing pKa calculations to be easily added to existing protocols with a few extra lines of code. This capability was exploited in the development of PypKa-MD, an easy-to-use implementation of the stochastic titration CpHMD method. PypKa-MD supports GROMOS and CHARMM force fields, as well as modern versions of GROMACS. Using PypKa’s API and consequent abstraction of PB/MC contributed to its greatly simplified modular architecture that will serve as the foundation for future developments. The new implementation was validated on alanine-based tetrapeptides with closely interacting titratable residues and four commonly used benchmark proteins, displaying highly similar and correlated pKa predictions compared to a previously validated implementation. Like most structural-based computational studies, the majority of pKa calculations are performed on experimental structures deposited in the PDB. Furthermore, there is an ever-growing imbalance between scarce experimental pKa values and the increasingly higher number of resolved structures. To save countless hours and resources that would be spent on repeated calculations, we have released pKPDB, a database of over 12M theoretical pKa values obtained by running PypKa over 120k protein structures from the PDB. The precomputed pKa estimations can be retrieved instantaneously via our web application, the PypKa Server. In case the protein of interest is not in the pKPDB, the user may easily run PypKa in the cloud either by uploading a custom structure or submitting an identifier code from the PBD or UniProtKB. It is also possible to use the server to get structures with representative pH-dependent protonation states to be used in other computational methods such as molecular dynamics. The advent of artificial intelligence in biological sciences presented an opportunity to drastically accelerate pKa predictors using our previously generated database of pKa values. With pKAI, we introduced the first deep learning-based predictor of pKa shifts in proteins trained on continuum electrostatics data. By combining a reasonable understanding of the underlying physics, an accuracy comparable to that of physics-based methods, and inference time speedups of more than 1000 ×, pKAI provided a game-changing solution for fast estimations of macroscopic pKa from ensembles of microscopic values. However, several limitations needed to be addressed before its integration within the CpHMD framework as a replacement for PypKa. Hence, we proposed a new graph neural network for protein pKa predictions suitable for CpHMD, pKAI-MD. This model estimates pH-independent energies to be used in a Monte Carlo routine to sample representative microscopic protonation states. While developing the new model, we explored different graph representations of proteins using multiple electrostatics-driven properties. While there are certainly many new features to be introduced and a multitude of development to be expanded, the selection of methods and tools presented in this work poses a significant improvement over the alternatives and effectively constitutes a new generation of user-friendly and machine learning-accelerated methods for pKa calculations

Inhibitor selectivity: profiling and prediction

Less than 1 in 10 drug candidates that enter phase 1 clinical trials actually gets approved for human use. The high failure rate is in part due to unforeseen side effects or toxicity. A better understanding of the role of selectivity and a better insight in the off-target activities of drug candidates could greatly aid in preventing candidates to fail for these reasons. This thesis has tried to address some aspects in this challenging part of drug discovery. The use of activity-based protein profiling as presented in Chapters 2 and 3 in drug discovery and hit-to-lead optimization, and in Chapter 5 and 6 for the interaction profiling of a drug candidate, highlights the versatility and importance of this chemical biology technique. Combined with knowledge derived from biochemical assays, such as that developed in Chapter 4, ABPP can greatly aid the medicinal chemist. The recent surge in popularity of machine learning algorithms, backed by exponential growth of the amount of biological data available, holds great promise for drug discovery. Chapters 7 and 8 showed the applicability of one such algorithm, which was able to quite reliably predict interaction profiles. The challenges in finding, determining and predicting selectivity are far from solved, but, by incrementally expanding our understanding of the binding of small molecules to their (off-)targets, truly selective inhibitors might at some point become a reality or their necessity might be mitigated.Medical Biochemistr

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

Open-source, python-based redevelopment of the ChemShell multiscale QM/MM environment

ChemShell is a scriptable computational chemistry environment with an emphasis on multiscale simulation of complex systems using combined quantum mechanical and molecular mechanical (QM/MM) methods. Motivated by a scientific need to efficiently and accurately model chemical reactions on surfaces and within microporous solids on massively parallel computing systems, we present a major redevelopment of the ChemShell code, which provides a modern platform for advanced QM/MM embedding models. The new version of ChemShell has been re-engineered from the ground up with a new QM/MM driver module, an improved parallelization framework, new interfaces to high performance QM and MM programs, and a user interface written in the Python programming language. The redeveloped package is capable of performing QM/MM calculations on systems of significantly increased size, which we illustrate with benchmarks on zirconium dioxide nanoparticles of over 160,000 atoms

An Open-Source, Python-Based Redevelopment of the ChemShell Multiscale QM/MM Environment

ChemShell is a scriptable computational chemistry environment with an emphasis on multiscale simulation of complex systems using combined quantum mechanical and molecular mechanical (QM/MM) methods. Motivated by a scientific need to efficiently and accurately model chemical reactions on surfaces and within microporous solids on massively parallel computing systems, we present a major redevelopment of the ChemShell code, which provides a modern platform for advanced QM/MM embedding models. The new version of ChemShell has been re-engineered from the ground up with a new QM/MM driver module, an improved parallelization framework, new interfaces to high performance QM and MM programs, and a user interface written in the Python programming language. The redeveloped package is capable of performing QM/MM calculations on systems of significantly increased size, which we illustrate with benchmarks on zirconium dioxide nanoparticles of over 160,000 atoms

Modeling and Simulation of Biological Systems through Electronic Design Automation techniques

Modeling and simulation of biological systems is a key requirement for integrating invitro and in-vivo experimental data. In-silico simulation allows testing different experimental conditions, thus helping in the discovery of the dynamics that regulate the system. These dynamics include errors in the cellular information processing that are responsible for diseases such as cancer, autoimmunity, and diabetes as well as drug effects to the system (Gonalves, 2013). In this context, modeling approaches can be classified into two categories: quantitative and qualitative models. Quantitative modeling allows for a natural representation of molecular and gene networks and provides the most precise prediction. Nevertheless, the lack of kinetic data (and of quantitative data in general) hampers its use for many situations (Le Novere, 2015). In contrast, qualitative models simplify the biological reality and are often able to reproduce the system behavior. They cannot describe actual concentration levels nor realistic time scales. As a consequence, they cannot be used to explain and predict the outcome of biological experiments that yield quantitative data. However, given a biological network consisting of input (e.g., receptors), intermediate, and output (e.g., transcription factors) signals, they allow studying the input-output relationships through discrete simulation (Samaga, 2013). Boolean models are gaining an increasing interest in reproducing dynamic behaviors, understanding processes, and predicting emerging properties of cellular signaling networks through in-silico experiments. They are emerging as a valid alternative to the quantitative approaches (i.e., based on ordinary differential equations) for exploratory modeling when little is known about reaction kinetics or equilibrium constants in the context of gene expression or signaling. Even though several approaches and software have been recently proposed for logic modeling of biological systems, they are limited to specific contexts and they lack of automation in analyzing biological properties such as complex attractors, and molecule vulnerability. This thesis proposes a platform based on Electronic Design Automation (EDA) technologies for qualitative modeling and simulation of Biological Systems. It aims at overtaking limitations that affect the most recent qualitative tools

Expression data dnalysis and regulatory network inference by means of correlation patterns

With the advance of high-throughput techniques, the amount of available data in the bio-molecular field is rapidly growing. It is now possible to measure genome-wide aspects of an entire biological system as a whole. Correlations that emerge due to internal dependency structures of these systems entail the formation of characteristic patterns in the corresponding data. The extraction of these patterns has become an integral part of computational biology. By triggering perturbations and interventions it is possible to induce an alteration of patterns, which may help to derive the dependency structures present in the system. In particular, differential expression experiments may yield alternate patterns that we can use to approximate the actual interplay of regulatory proteins and genetic elements, namely, the regulatory network of a cell. In this work, we examine the detection of correlation patterns from bio-molecular data and we evaluate their applicability in terms of protein contact prediction, experimental artifact removal, the discovery of unexpected expression patterns and genome-scale inference of regulatory networks. Correlation patterns are not limited to expression data. Their analysis in the context of conserved interfaces among proteins is useful to estimate whether these may have co-evolved. Patterns that hint on correlated mutations would then occur in the associated protein sequences as well. We employ a conceptually simple sampling strategy to decide whether or not two pathway elements share a conserved interface and are thus likely to be in physical contact. We successfully apply our method to a system of ABC-transporters and two-component systems from the phylum of Firmicute bacteria. For spatially resolved gene expression data like microarrays, the detection of artifacts, as opposed to noise, corresponds to the extraction of localized patterns that resemble outliers in a given region. We develop a method to detect and remove such artifacts using a sliding-window approach. Our method is very accurate and it is shown to adapt to other platforms like custom arrays as well. Further, we developed Padesco as a way to reveal unexpected expression patterns. We extract frequent and recurring patterns that are conserved across many experiments. For a specific experiment, we predict whether a gene deviates from its expected behaviour. We show that Padesco is an effective approach for selecting promising candidates from differential expression experiments. In Chapter 5, we then focus on the inference of genome-scale regulatory networks from expression data. Here, correlation patterns have proven useful for the data-driven estimation of regulatory interactions. We show that, for reliable eukaryotic network inference, the integration of prior networks is essential. We reveal that this integration leads to an over-estimate of network-wide quality estimates and suggest a corrective procedure, CoRe, to counterbalance this effect. CoRe drastically improves the false discovery rate of the originally predicted networks. We further suggest a consensus approach in combination with an extended set of topological features to obtain a more accurate estimate of the eukaryotic regulatory network for yeast. In the course of this work we show how correlation patterns can be detected and how they can be applied for various problem settings in computational molecular biology. We develop and discuss competitive approaches for the prediction of protein contacts, artifact repair, differential expression analysis, and network inference and show their applicability in practical setups.Mit der Weiterentwicklung von Hochdurchsatztechniken steigt die Anzahl verfügbarer Daten im Bereich der Molekularbiologie rapide an. Es ist heute möglich, genomweite Aspekte eines ganzen biologischen Systems komplett zu erfassen. Korrelationen, die aufgrund der internen Abhängigkeits-Strukturen dieser Systeme enstehen, führen zu charakteristischen Mustern in gemessenen Daten. Die Extraktion dieser Muster ist zum integralen Bestandteil der Bioinformatik geworden. Durch geplante Eingriffe in das System ist es möglich Muster-Änderungen auszulösen, die helfen, die Abhängigkeits-Strukturen des Systems abzuleiten. Speziell differentielle Expressions-Experimente können Muster-Wechsel bedingen, die wir verwenden können, um uns dem tatsächlichen Wechselspiel von regulatorischen Proteinen und genetischen Elementen anzunähern, also dem regulatorischen Netzwerk einer Zelle. In der vorliegenden Arbeit beschäftigen wir uns mit der Erkennung von Korrelations-Mustern in molekularbiologischen Daten und schätzen ihre praktische Nutzbarkeit ab, speziell im Kontext der Kontakt-Vorhersage von Proteinen, der Entfernung von experimentellen Artefakten, der Aufdeckung unerwarteter Expressions-Muster und der genomweiten Vorhersage regulatorischer Netzwerke. Korrelations-Muster sind nicht auf Expressions-Daten beschränkt. Ihre Analyse im Kontext konservierter Schnittstellen zwischen Proteinen liefert nützliche Hinweise auf deren Ko-Evolution. Muster die auf korrelierte Mutationen hinweisen, würden in diesem Fall auch in den entsprechenden Proteinsequenzen auftauchen. Wir nutzen eine einfache Sampling-Strategie, um zu entscheiden, ob zwei Elemente eines Pathways eine gemeinsame Schnittstelle teilen, berechnen also die Wahrscheinlichkeit für deren physikalischen Kontakt. Wir wenden unsere Methode mit Erfolg auf ein System von ABC-Transportern und Zwei-Komponenten-Systemen aus dem Firmicutes Bakterien-Stamm an. Für räumlich aufgelöste Expressions-Daten wie Microarrays enspricht die Detektion von Artefakten der Extraktion lokal begrenzter Muster. Im Gegensatz zur Erkennung von Rauschen stellen diese innerhalb einer definierten Region Ausreißer dar. Wir entwickeln eine Methodik, um mit Hilfe eines Sliding-Window-Verfahrens, solche Artefakte zu erkennen und zu entfernen. Das Verfahren erkennt diese sehr zuverlässig. Zudem kann es auf Daten diverser Plattformen, wie Custom-Arrays, eingesetzt werden. Als weitere Möglichkeit unerwartete Korrelations-Muster aufzudecken, entwickeln wir Padesco. Wir extrahieren häufige und wiederkehrende Muster, die über Experimente hinweg konserviert sind. Für ein bestimmtes Experiment sagen wir vorher, ob ein Gen von seinem erwarteten Verhalten abweicht. Wir zeigen, dass Padesco ein effektives Vorgehen ist, um vielversprechende Kandidaten eines differentiellen Expressions-Experiments auszuwählen. Wir konzentrieren uns in Kapitel 5 auf die Vorhersage genomweiter regulatorischer Netzwerke aus Expressions-Daten. Hierbei haben sich Korrelations-Muster als nützlich für die datenbasierte Abschätzung regulatorischer Interaktionen erwiesen. Wir zeigen, dass für die Inferenz eukaryotischer Systeme eine Integration zuvor bekannter Regulationen essentiell ist. Unsere Ergebnisse ergeben, dass diese Integration zur Überschätzung netzwerkübergreifender Qualitätsmaße führt und wir schlagen eine Prozedur - CoRe - zur Verbesserung vor, um diesen Effekt auszugleichen. CoRe verbessert die False Discovery Rate der ursprünglich vorhergesagten Netzwerke drastisch. Weiterhin schlagen wir einen Konsensus-Ansatz in Kombination mit einem erweiterten Satz topologischer Features vor, um eine präzisere Vorhersage für das eukaryotische Hefe-Netzwerk zu erhalten. Im Rahmen dieser Arbeit zeigen wir, wie Korrelations-Muster erkannt und wie sie auf verschiedene Problemstellungen der Bioinformatik angewandt werden können. Wir entwickeln und diskutieren Ansätze zur Vorhersage von Proteinkontakten, Behebung von Artefakten, differentiellen Analyse von Expressionsdaten und zur Vorhersage von Netzwerken und zeigen ihre Eignung im praktischen Einsatz