Metabolites and peptides have a central role in biology that is often overlooked. Despite the importance of metabolites in key protein-metabolite interactions (PMIs), the extent and identity of these interactions is not known. Likewise, the extent to which short open reading frames (sORFs) in the genome are translated into peptides has also been an elusive question. This dissertation describes the development and application of methods to elucidate unknown molecules and interactions critical to understanding biology, and the subsequent characterization of the biological roles of these discoveries in cells and mice. / Chemistry and Chemical Biology

<p>Data science uses the latest techniques in statistics and machine learning to extract insights from data. With the increasing amount of protein data, a number of novel research approaches have become feasible.</p><p>Micropeptides are an emerging field in the protein universe. They are small proteins with <= 100 amino acid residues (aa) and are translated from small open reading frames (sORFs) of <= 303 base pairs (bp). Traditionally, their existence was ignored because of the technical difficulties in isolating them. With technological advances, a growing number of micropeptides have been characterized and shown to play vital roles in many biological processes. Yet, we lack bioinformatics methods for predicting them directly from DNA sequences, which could substantially facilitate research in this field with minimal cost. With the increasing amount of data, developing new methods to address this need becomes possible. We therefore developed MiPepid, a machine-learning-based method specifically designed for predicting micropeptides from DNA sequences by curating a high-quality dataset and by training MiPepid using logistic regression with 4-mer features. MiPepid performed exceptionally well on holdout test sets and performed much better than existing methods. MiPepid is available for downloading, easy to use, and runs sufficiently fast.</p><p>Long noncoding RNAs (LncRNAs) are transcripts of > 200 bp and does not encode a protein. Contrary to their “noncoding” definition, an increasing number of lncRNAs have been found to be translated into functional micropeptides. Therefore, whether most lncRNAs are translated is an open question of great significance. To address this question, by harnessing the availability of large-scale human variation data, we have explored the relationships between lncRNAs, micropeptides, and canonical regular proteins (> 100 aa) from the perspective of genetic variation, which has long been used to study natural selection to infer functional relevance. Through rigorous statistical analyses, we find that lncRNAs share a similar genetic variation profile with proteins regarding single nucleotide polymorphism (SNP) density, SNP spectrum, enrichment of rare SNPs, etc., suggesting lncRNAs are under similar negative selection strength with proteins. Our study revealed similarities between micropeptides, lncRNAs, and canonical proteins and is the first attempt to explore the relationships between the three groups from a genetic variation perspective.</p><p>Deep learning has been tremendously successful in 2D image recognition. Protein binding ligand prediction is fundamental topic in protein research as most proteins bind ligands to function. Proteins are 3D structures and can be considered as 3D images. Prediction of binding ligands of proteins can then be converted to a 3D image classification problem. In addition, a large number of protein structure data are available now. We therefore utilized deep learning to predict protein binding ligands by designing a 3D convolutional neural network from scratch and by building a large 3D image dataset of protein structures. The trained model achieved an average F1 score of over 0.8 across 151 classes on the holdout test set. Compared to existing methods, our model performed better. In summary, we showed the feasibility of deploying deep learning in protein structure research.</p><p>In conclusion, by exploring various edges of the protein universe from the perspective of data science, we showed that the increasing amount of data and the advancement of data science methods made it possible to address a wide variety of pressing biological questions. We showed that for a successful data science study, the three components – goal, data, method – all of them are indispensable. We provided three successful data science studies: the careful data cleaning and selection of machine learning algorithm lead to the development of MiPepid that fits the urgent need of a micropeptide prediction method; identifying the question and exploring it from a different angle lead to the key insight that lncRNAs resemble micropeptides; applying deep learning to protein structure data lead to a new approach to the long-standing question of protein-ligand binding. The three studies serve as excellent examples in solving a wide range of data science problems with a variety of issues.</p>

Chez les cellules eucaryotes, la mitochondrie est une organelle impliquée dans plusieurs fonctions cellulaires (production d’énergie, apoptose, production de ROS, prolifération, signalisation cellulaire, vieillissement, immunité et plus encore) et possédant son propre génome, soit l’ADN mitochondrial (ADNmt). Chez l’humain, on croyait que l’ADNmt ne codait que pour 37 gènes impliqués dans la production d’énergie et la traduction mitochondriale. Cependant, le potentiel codant du génome mitochondrial aurait été sous-estimé. Il a récemment été démontré qu’à l’intérieur de ces principaux gènes connus pouvaient se cacher plusieurs petits gènes alternatifs. Ceux-ci se retrouvent au sein de régions non codantes ou possèdent des séquences d’initiation ou de terminaison de la traduction distinctes de celles du gène de référence dans lequel ils se retrouvent. Ils codent pour des micropeptides dérivés des mitochondries qui possèdent un large éventail de fonctions, s’ajoutant à la longue liste de fonctions dans lesquelles la mitochondrie est déjà impliquée. Parmi ces peptides, on retrouve l’Humanine, MOTS-c, SHLP1-6, Gau et SHMOOSE. Nous avons précédemment découvert un nouveau gène alternatif situé dans le gène nd4, nommé mtaltnd4. Dans cette étude, nous visions à clarifier les fonctions du peptide alternatif correspondant MTALTND4 en étudiant son patron d’expression dans les tissus humains, l’impact de plusieurs stress sur son expression, l’impact du peptide sur la transcription des gènes, et ses partenaires d’interaction. Nous avons découvert que MTALTND4 pourrait être une molécule de signalisation sécrétée par les cellules en réponse au stress et affectant la physiologie pour induire un état de dépression bioénergétique en réduisant les processus de production et de demande en ATP. Plusieurs autres indices révélés par nos expériences suggèrent que MTALTND4 pourrait être une protéine multifonctionnelle impliquée dans de nombreuses voies de régulation. / In eukaryotic cells, mitochondria are organelles involved in many cellular functions (energy production, apoptosis, ROS production, proliferation, cell signaling aging, immunity and more) and that possess their own genome, namely mitochondrial DNA (mtDNA). In humans, mtDNA was believed to encode only 37 genes involved in energy production and mitochondrial translation. However, the coding potential of the mitochondrial genome has been underestimated. It has recently been shown that within these main genes could hide several small alternative genes (i.e., genes withing non-coding regions or with translation initiation/termination sequences that are distinct from the reference gene sequences in which they are found). They code for mitochondrial-derived micropeptides (MDPs) that have a broad spectrum of functions, adding to the extensive list of functions in which mitochondria are already involved. These peptides include Humanine, MOTS-c, SHLP1-6, Gau and SHMOOSE. We have previously discovered a new alternative gene located in the nd4 gene, termed mtaltnd4. In this study, we aim to clarify the functions of the corresponding alternative peptide MTALTND4 by studying its expression pattern in human tissues, the impact of several stresses on its expression, the impact of the peptide on gene transcription, and its interaction partners. We have found that MTALTND4 could be a signaling molecule secreted by cells in response to stress and would affect physiology to induce a state of bioenergetic depression by reducing ATP-producing and ATP-demanding processes. Several other clues revealed by our experiments suggest that MTALTND4 could be a multifunctional protein involved in numerous regulatory pathways.

Kürzlich wurde mithilfe von Ribo-seq Experimenten die Translation hunderter Mikroproteine in menschlichen Herzen entdeckt. Diese blieben zuvor aufgrund ihrer geringen Größe (< 100 Aminosäuren) unentdeckt, und ihre physiologische Rolle ist noch weitgehend unbekannt. Ziel dieser Promotionsarbeit ist es, potentielle Funktionen dieser neuartigen Mikroproteine zu entschlüsseln. Dabei sollen insbesondere die Aufklärung ihrer evolutionären Konservierungssignatur, subzellulären Lokalisierung und ihres Proteininteraktoms helfen. Die Konservierungsanalyse ergab, dass fast 90% der Mikroproteine nur in Primaten konserviert ist. Weiterhin konnte ich die Produktion von Mikroproteine in vitro und in vivo nachweisen, die subzelluläre Lokalisierung von 92 Mikroproteinen definieren, und Interaktionspartner für 60 Mikroproteine identifizieren. Dutzende dieser Mikroproteine lokalisieren in Mitochondrien. Dazu gehörte ein im Herzen angereichertes Mikroprotein, das aufgrund der Interaktions- und Lokalisationsdaten einen neuartigen Modulator der mitochondrialen Proteintranslation darstellen könnte. Der Interaktom-Screen zeigte außerdem, dass evolutionär junge Mikroproteine ähnliche Interaktionsfähigkeiten wie konservierte Kandidaten haben. Schließlich wurden kurze Sequenzmotive identifiziert, die Mikroprotein-Protein-Wechselwirkungen vermitteln, wodurch junge Mikroproteine mit zellulären Prozessen – wie z.B. Endozytose und Spleißen – in Verbindung gebracht werden konnten. Zusammenfassend wurde die Produktion vieler kleiner Proteine im menschlichen Herzen bestätigt, von denen die meisten lediglich in Primaten konserviert sind. Zusätzlich verknüpften umfangreiche Lokalisierungs- und Interaktionsdaten mehrere Mikroproteine mit Prozessen wie Spleißen, Endozytose und mitochondrialer Translation. Weitere Untersuchungen dieses zuvor verborgenen Teils des Herzproteoms werden zu einem besseren Verständnis von evolutionär jungen Proteinen und kardiologischen Prozessen beitragen. / Recently, the active translation of hundreds of previously unknown microproteins was detected using ribosome profiling on tissues of human hearts. They had remained undetected due to their small size (< 100 amino acids), and their physiological roles are still largely unknown. This dissertation aims to investigate these novel microproteins and validate their translation by independent methods. Particularly, elucidating their conservation signature, subcellular localization, and protein interactome shall aid in deciphering their potential biological role. Conservation analysis revealed that sequence conservation of almost 90% of microproteins was restricted to primates. I next confirmed microprotein production in vitro and in vivo by in vitro translation assays and mass spectrometry-based approaches, defined the subcellular localization of 92 microproteins, and identified significant interaction partners for 60 candidates. Dozens of these microproteins localized to the mitochondrion. These included a novel cardiac-enriched microprotein that may present a novel modulator of mitochondrial protein translation based on its interaction profile and subcellular localization. The interactome screen further revealed that evolutionarily young microproteins have similar interaction capacities to conserved candidates. Finally, it allowed identifying short linear motifs that may mediate microprotein-protein interactions and implicated several young microproteins in distinct cellular processes such as endocytosis and splicing. I conclude that many novel small proteins are produced in the human heart, most of which exhibit poor sequence conservation. I provide a substantial resource of microprotein localization and interaction data that links several to cellular processes such as splicing, endocytosis, and mitochondrial translation. Further investigation into this hidden part of the cardiac proteome will contribute to our understanding of recently evolved proteins and heart biology.