The loss and gain of functional amino acid residues is a common mechanism causing human inherited disease

Elucidating the precise molecular events altered by disease-causing genetic variants represents a major challenge in translational bioinformatics. To this end, many studies have investigated the structural and functional impact of amino acid substitutions. Most of these studies were however limited in scope to either individual molecular functions or were concerned with functional effects (e.g. deleterious vs. neutral) without specifically considering possible molecular alterations. The recent growth of structural, molecular and genetic data presents an opportunity for more comprehensive studies to consider the structural environment of a residue of interest, to hypothesize specific molecular effects of sequence variants and to statistically associate these effects with genetic disease. In this study, we analyzed data sets of disease-causing and putatively neutral human variants mapped to protein 3D structures as part of a systematic study of the loss and gain of various types of functional attribute potentially underlying pathogenic molecular alterations. We first propose a formal model to assess probabilistically function-impacting variants. We then develop an array of structure-based functional residue predictors, evaluate their performance, and use them to quantify the impact of disease-causing amino acid substitutions on catalytic activity, metal binding, macromolecular binding, ligand binding, allosteric regulation and post-translational modifications. We show that our methodology generates actionable biological hypotheses for up to 41% of disease-causing genetic variants mapped to protein structures suggesting that it can be reliably used to guide experimental validation. Our results suggest that a significant fraction of disease-causing human variants mapping to protein structures are function-altering both in the presence and absence of stability disruption

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research

Analysis of the understudied parts of the phospho-signalome using machine learning methods

Abstract Analysis of the understudied parts of the phospho-signalome using machine learning methods Borgthor Petursson In order to make decisions and respond appropriately to external stimuli, cells rely on an intricate signalling system. One of the most important and best studied components of this signalling system is the phospho-signalling network. Phosphorylation relays information through adding phosphoryl groups onto substrates such as lipids or proteins, which in turn leads to changes in substrate function. Crucial components of this system include kinases, which phosphorylate on the substrate molecule and phosphatases that remove the phosphoryl group from the substrate. To date, even though >100K phosphoproteins have been identified through high throughput experiments, the vast majority of phosphosites are of unknown function, while over a third of kinases have no known substrate (Needham et al., 2019). Furthermore, there is a large study bias in our current knowledge, demonstrated by a disproportionate number of interactions between highly cited kinases and substrates Invergo and Beltrao, 2018. The vast understudied signalling space combined with this study bias make it difficult to understand the general principles underpinning cell signalling regulation and stresses the need to research the phosphoproteomic signalling system in an unbiased manner. In this thesis the central aim is to use data-driven and unbiased approaches to study the human phosphoproteomic signalling network. The first chapter describes a project where I co-developed a machine learning model to predict signed kinase-kinase regulatory circuits based on kinase specificities and high throughput phosphoproteomics and transcriptomic data. The network was validated using independent high throughput data and used to identify novel kinase-kinase regulatory interactions. This project was done in collaboration with Brandon Invergo, a postdoc in Pedro Beltrao’s research group. In the second chapter I expand upon work done in the first chapter. I used various predictors such as: Co-expression, kinase specificities and different variables characterising kinase-substrate potential target phosphosites to predict kinase-substrate relationships and their signs. I then used independent experimental kinase-substrate predictions to validate the predictions and identify high confidence kinase-substrate relationships. I then combined the kinase-substrate predictions with the kinase-kinase regulatory circuits to identify condition-specific signalling networks. To enable easy use of my method and networks and analyses of phosphoproteomics data by non-expert users I also developed the SELPHI2 server, where the user can extract biological insight from their datasets. SELPHI2 presents a substantial improvement upon the SELPHI server, which was developed in 2015 by my supervisor, Evangelia Petsalaki. Thirdly, to study the architecture of human cell signalling networks at a whole-cell level and address the limited predictive power of the current models of cell signalling such as pathways found in KEGG (Kanehisa, 2019), Reactome (Jassal et al., 2020) and WikiPathways (Slenter et al., 2018), the third chapter aims to identify signalling modules from phosphoproteomic data. These data-extracted modules were found to have a greater predictive power for independent data sets in terms of number of significant enrichments. Furthermore, we sought to predict the probability of module co-membership from predictors such as membership within data-driven modules, co-phosphorylation and co-expression. In summary, the work presented here seeks to explore the understudied phospho-signalling systems through system-wide prediction of kinase-substrate regulation and the identification of phospho-signalling modules through data-driven means

Classifying transcription factor targets and discovering relevant biological features

<p>Abstract</p> <p>Background</p> <p>An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a supervised-learning approach to TF target identification, and used it to predict targets of 104 transcription factors in yeast. We now include a new sequence conservation measure, expand our predictions to include 59 new TFs, introduce a web-server, and implement an improved ranking method to reveal the biological features contributing to regulation. The classifiers combine 8 genomic datasets covering a broad range of measurements including sequence conservation, sequence overrepresentation, gene expression, and DNA structural properties.</p> <p>Principal Findings</p> <p>(1) Application of the method yields an amplification of information about yeast regulators. The ratio of total targets to previously known targets is greater than 2 for 11 TFs, with several having larger gains: Ash1(4), Ino2(2.6), Yaf1(2.4), and Yap6(2.4).</p> <p>(2) Many predicted targets for TFs match well with the known biology of their regulators. As a case study we discuss the regulator Swi6, presenting evidence that it may be important in the DNA damage response, and that the previously uncharacterized gene YMR279C plays a role in DNA damage response and perhaps in cell-cycle progression.</p> <p>(3) A procedure based on recursive-feature-elimination is able to uncover from the large initial data sets those features that best distinguish targets for any TF, providing clues relevant to its biology. An analysis of Swi6 suggests a possible role in lipid metabolism, and more specifically in metabolism of ceramide, a bioactive lipid currently being investigated for anti-cancer properties.</p> <p>(4) An analysis of global network properties highlights the transcriptional network hubs; the factors which control the most genes and the genes which are bound by the largest set of regulators. Cell-cycle and growth related regulators dominate the former; genes involved in carbon metabolism and energy generation dominate the latter.</p> <p>Conclusion</p> <p>Postprocessing of regulatory-classifier results can provide high quality predictions, and feature ranking strategies can deliver insight into the regulatory functions of TFs. Predictions are available at an online web-server, including the full transcriptional network, which can be analyzed using VisAnt network analysis suite.</p> <p>Reviewers</p> <p>This article was reviewed by Igor Jouline, Todd Mockler(nominated by Valerian Dolja), and Sandor Pongor.</p

The identification of short linear motif-mediated interfaces within the human interactome

Motivation: Eukaryotic proteins are highly modular, containing multiple interaction interfaces that mediate binding to a network of regulators and effectors. Recent advances in high-throughput proteomics have rapidly expanded the number of known protein–protein interactions (PPIs); however, the molecular basis for the majority of these interactions remains to be elucidated. There has been a growing appreciation of the importance of a subset of these PPIs, namely those mediated by short linear motifs (SLiMs), particularly the canonical and ubiquitous SH2, SH3 and PDZ domain-binding motifs. However, these motif classes represent only a small fraction of known SLiMs and outside these examples little effort has been made, either bioinformatically or experimentally, to discover the full complement of motif instances

Neue bioinformatische und statistische Methoden für die Analyse von Massenspektrometrie-basierten phosphoproteomischen Daten

In living cells, reversible protein phosphorylation events propagate signals caused by external stimuli from the plasma membrane to their intracellular destinations. Aberrations in these signaling cascades can lead to diseases such as cancer. To identify and quantify phosphorylation events on a large scale, mass spectrometry (MS) has become the predominant technology. The large amount of data generated by MS requires efficient, tailor-made computational tools in order to draw meaningful biological conclusions. In this work, four new methods for analyzing MS-based phosphoproteomic data are presented. The first method, called SubExtractor, combines phosphoproteomic data with protein network information to identify differentially regulated subnetworks. The method is based on a Bayesian probabilistic model that accounts for information about both differential regulation and network topology, combined with a genetic algorithm and rigorous significance testing. The second method, called MeanRank test, is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. The test successfully deals with small numbers of replicates, missing values without the need of imputation, non-normally distributed expression levels, and non-identical distribution of up- and down-regulated features, while its statistical power scales well with the number of replicates. The third method is a biomarker discovery workflow that aims at identifying a multivariate response prediction biomarker for treatment of non-small cell lung cancer cell lines with the kinase inhibitor dasatinib from phosphoproteomic data (referred to as NSCLC biomarker). An elaborate biomarker workflow based on robust feature selection in combination with a support vector machine (SVM) was designed in order to find a phosphorylation signature that accurately predicts the response to dasatanib. The fourth method, called Pareto biomarker, extends the previous NSCLC biomarker workflow by optimizing not only one single objective (i.e. best possible separation of responders and non-responders), but also the objectives signature size and relevance (i.e. association of signature proteins with dasatinib’s main target). This is achieved by employing a multiobjective optimization algorithm based on the principle of Pareto optimality, which allows for a simultaneous optimization of all three objectives. These novel data analysis methods were thoroughly validated using experimental data and compared to existing methods. They can be used on their own, or they can be combined into a joint workflow in order to efficiently answer complex biological questions in the field of large-scale omics in general and phosphoproteomics in particular.In lebenden Zellen sind reversible Proteinphosphorylierungen für die Weiterleitung von Signalen externer Stimuli zu deren intrazellulären Bestimmungsorten verantwortlich. Anomalien in solchen Signaltransduktionswegen können zu Krankheiten wie beispielsweise Krebs führen. Um Phosphorylierungsstellen in großem Maßstab zu identifizieren und zu quantifizieren, hat sich die Massenspektrometrie (MS) zur vorherrschenden Technologie entwickelt. Die große Menge an Daten, die von Massenspektrometern generiert wird, erfordert effiziente maßgeschneiderte Computerprogramme, um aussagekräftige biologische Schlüsse ziehen zu können. In dieser Arbeit werden vier neue Methoden zur Analyse von MS-basierten phosphoproteomischen Daten präsentiert. Die erste Methode, genannt SubExtractor, kombiniert phosphoproteomische Daten mit Proteinnetzwerkinformationen um differentiell regulierte Subnetzwerke zu identifizieren. Die Methode basiert auf einem Bayesschen Wahrscheinlichkeitsmodell, das sowohl Information über die differentielle Regulation der Einzelknoten als auch die Netzwerktopologie berücksichtigt. Das Modell ist kombiniert mit einem genetischen Algorithmus und stringenter Signifikanzanalyse. Die zweite Methode, genannt MeanRank-Test, ist ein globaler Einstichproben-Lagetest, der auf den mittleren Rängen der Replikate beruht, und die False Discovery Rate implizit abschätzt und kontrolliert. Der Test eignet sich für die Anwendung auf Daten mit wenigen Replikate, fehlenden und nicht normalverteilten Werten, sowie nicht gleichverteilter Hoch- und Runterregulation. Gleichzeitig skaliert die Teststärke gut mit der Anzahl an Replikaten. Die dritte Methode ist ein Arbeitsablauf zur Biomarkeridentifizierung und hat zum Ziel, einen multivariaten Stratifikationsbiomarker aus phosphoproteomischen Daten zu extrahieren, der das Ansprechen von nichtkleinzelligen Bronchialkarzinomzelllinien auf den Kinaseinhibitor Dasatinib vorhersagt (bezeichnet als NSCLC-Biomarker). Dazu wurde ein ausführlicher Biomarkerarbeitsablauf basierend auf einer robusten Feature Selection in Kombination mit Support Vector Machine-Klassifizierung erstellt, um eine Phosphorylierungssignatur zu finden, die das Ansprechen auf Dasatinib richtig vorhersagt. Die vierte Methode, genannt Pareto-Biomarker, erweitert den vorherigen Biomarkerarbeitsablauf, indem nicht nur eine Zielfunktion (d.h. die bestmögliche Trennung von Respondern und Nichtrespondern) optimiert wird, sondern zusätzlich noch die Signaturgröße und Relevanz (d.h. die Verbindung der Signaturproteine mit dem Targetprotein von Dasatinib). Dies wird durch die Verwendung eines multiobjektiven Optimierungsalgorithmus erreicht, der auf dem Prinzip der Pareto-Optimalität beruht und die gleichzeitige Optimierung aller drei Zielfunktionen ermöglicht. Die hier präsentierten neuen Datenanalysemethoden wurden gründlich mittels experimenteller Daten validiert und mit bereits bestehenden Methoden verglichen. Sie können einzeln verwendet werden, oder man kann sie zu einem gemeinsamen Arbeitsablauf zusammenfügen, um komplexe biologische Fragestellungen in Omik-Gebieten im Allgemeinen und Phosphoproteomik im Speziellen zu beantworten