Application and Optimization of Contact-Guided Replica Exchange Molecular Dynamics

Proteine sind komplexe Makromoleküle, die in lebenden Organismen eine große Vielfalt an wichtigen Aufgaben erfüllen. Proteine können beispielsweise Gene regulieren, Struktur stabilisieren, Zellsignale übertragen, Substanzen transportieren und vieles mehr. Typischerweise sind umfassende Kenntnisse von Struktur und Dynamik eines Proteins erforderlich um dessen physiologische Funktion und Interaktionsmechanismen vollständig zu verstehen. Gewonnene Erkenntnisse sind für Biowissenschaften unerlässlich und können auf viele Bereiche angewendet werden, wie z.B. für Arzneimitteldesign oder zur Krankheitsbehandlung. Trotz des unfassbaren Fortschritts experimenteller Techniken bleibt die Bestimmung einer Proteinstruktur immer noch eine herausfordernde Aufgabe. Außerdem können Experimente nur Teilinformationen liefern und Messdaten können mehrdeutig und schwer zu interpretieren sein. Aus diesem Grund werden häufig Computersimulationen durchgeführt um weitere Erkenntnisse zu liefern und die Lücke zwischen Theorie und Experiment zu schließen. Heute sind viele in-silico Methoden in der Lage genaue Protein Strukturmodelle zu erzeugen, sei es mit einem de novo Ansatz oder durch Verbesserung eines anfänglichen Modells unter Berücksichtigung experimenteller Daten. In dieser Dissertation erforsche ich die Möglichkeiten von Replica Exchange Molekulardynamik (REX MD) als ein physikbasierter Ansatz zur Erzeugung von physikalisch sinnvollen Proteinstrukturen. Dabei lege ich den Fokus darauf möglichst nativähnliche Strukturen zu erhalten und untersuche die Stärken und Schwächen der angewendeten Methode. Ich erweitere die Standardanwendung, indem ich ein kontaktbasiertes Bias-Potential integriere um die Leistung und das Endergebnis von REX zu verbessern. Die Einbeziehung nativer Kontaktpaare, die sowohl aus theoretischen als auch aus experimentellen Quellen abgeleitet werden können, treibt die Simulation in Richtung gewünschter Konformationen und reduziert dementsprechend den notwendigen Rechenaufwand. Während meiner Arbeit führte ich mehrere Studien durch mit dem Ziel, die Anreicherung von nativ-ähnlichen Strukturen zu maximieren, wodurch der End-to-End Prozess von geleitetem REX MD optimiert wird. Jede Studie zielt darauf ab wichtige Aspekte der verwendeten Methode zu untersuchen und zu verbessern: 1) Ich studiere die Auswirkungen verschiedener Auswahlen von Bias-Kontakten, insbesondere die Reichweitenabhängigkeit und den negativen Einfluss von fehlerhaften Kontakten. Dadurch kann ich ermitteln, welche Art von Bias zu einer signifikanten Anreicherung von nativ-ähnlichen Konformationen führen im Vergleich zu regulärem REX. 2) Ich führe eine Parameteroptimierung am verwendeten Bias-Potential durch. Der Vergleich von Ergebnissen aus REX-Simulationen unter Verwendung unterschiedlicher sigmoidförmiger Potentiale weist mir sinnvolle Parameter Bereiche auf, wodurch ich ein ideales Bias-Potenzial für den allgemeinen Anwendungsfall ableiten kann. 3) Ich stelle eine de novo Faltungsmethode vor, die möglichst schnell viele einzigartige Startstrukturen für REX generieren kann. Dabei untersuche ich ausführlich die Leistung dieser Methode und vergleiche zwei verschiedene Ansätze zur Auswahl der Startstruktur. Das Ergebnis von REX wird stark verbessert, falls Strukturen bereits zu Beginn eine große Bandbreite des Konformationsraumes abdecken und gleichzeitig eine geringe Distanz zum angestrebten Zustand aufweisen. 4) Ich untersuche vier komplexe Algorithmusketten, die in der Lage sind repräsentative Strukturen aus großen biomolekularen Ensembles zu extrahieren, welche durch REX erzeugt wurden. Dabei studiere ich ihre Robustheit und Zuverlässigkeit, vergleiche sie miteinander und bewerte ihre erbrachte Leistung numerisch. 5) Basierend auf meiner Erfahrung mit geleitetem REX MD habe ich ein Python-Paket entwickelt um REX-Projekte zu automatisieren und zu vereinfachen. Es ermöglicht einem Benutzer das Entwerfen, Ausführen, Analysieren und Visualisieren eines REX-Projektes in einer interaktiven und benutzerfreundlichen Umgebung

INVESTIGATING INVASION IN DUCTAL CARCINOMA IN SITU WITH TOPOGRAPHICAL SINGLE CELL GENOME SEQUENCING

Synchronous Ductal Carcinoma in situ (DCIS-IDC) is an early stage breast cancer invasion in which it is possible to delineate genomic evolution during invasion because of the presence of both in situ and invasive regions within the same sample. While laser capture microdissection studies of DCIS-IDC examined the relationship between the paired in situ (DCIS) and invasive (IDC) regions, these studies were either confounded by bulk tissue or limited to a small set of genes or markers. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS), which combines laser-catapulting with single cell DNA sequencing to measure genomic copy number profiles from single tumor cells while preserving their spatial context. We applied TSCS to sequence 1,293 single cells from 10 synchronous DCIS patients. We also applied deep-exome sequencing to the in situ, invasive and normal tissues for the DCIS-IDC patients. Previous bulk tissue studies had produced several conflicting models of tumor evolution. Our data support a multiclonal invasion model, in which genome evolution occurs within the ducts and gives rise to multiple subclones that escape the ducts into the adjacent tissues to establish the invasive carcinomas. In summary, we have developed a novel method for single cell DNA sequencing, which preserves spatial context, and applied this method to understand clonal evolution during the transition between carcinoma in situ to invasive ductal carcinoma

Prospective Molecular Profiling of Canine Cancers Provides a Clinically Relevant Comparative Model for Evaluating Personalized Medicine (PMed) Trials.

Background Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting. Methodology A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type. Conclusions Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (\u3c1 \u3eweek). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

Evaluation of phylogenetic reconstruction methods using bacterial whole genomes: a simulation based study

Background: Phylogenetic reconstruction is a necessary first step in many analyses which use whole genome sequence data from bacterial populations. There are many available methods to infer phylogenies, and these have various advantages and disadvantages, but few unbiased comparisons of the range of approaches have been made. Methods: We simulated data from a defined "true tree" using a realistic evolutionary model. We built phylogenies from this data using a range of methods, and compared reconstructed trees to the true tree using two measures, noting the computational time needed for different phylogenetic reconstructions. We also used real data from Streptococcus pneumoniae alignments to compare individual core gene trees to a core genome tree. Results: We found that, as expected, maximum likelihood trees from good quality alignments were the most accurate, but also the most computationally intensive. Using less accurate phylogenetic reconstruction methods, we were able to obtain results of comparable accuracy; we found that approximate results can rapidly be obtained using genetic distance based methods. In real data we found that highly conserved core genes, such as those involved in translation, gave an inaccurate tree topology, whereas genes involved in recombination events gave inaccurate branch lengths. We also show a tree-of-trees, relating the results of different phylogenetic reconstructions to each other. Conclusions: We recommend three approaches, depending on requirements for accuracy and computational time. Quicker approaches that do not perform full maximum likelihood optimisation may be useful for many analyses requiring a phylogeny, as generating a high quality input alignment is likely to be the major limiting factor of accurate tree topology. We have publicly released our simulated data and code to enable further comparisons

Understand Biology Using Single Cell RNA-Sequencing

This dissertation summarizes the development of experimental and analytical tools for single cell RNA sequencing (scRNA-Seq), including 1) scPLATE-Seq, a FACS- and plate-based scRNASeq platform, which is accurate, robust, fully automated and cost-efficient; 2) metaVIPER, an algorithm for transcriptional regulator activity inference based on scRNA-Seq profiles; and 3) iterClust, a statistical framework for iterative clustering analysis, especially suitable for dissecting hierarchy of heterogeneity among single cells. Further this dissertation summarizes biological questions answered by combining these tools, including 1) understanding inter- and intra-tumor heterogeneity of human glioblastoma; 2) elucidating regulators of β-cell de-differentiation in type-2 diabetes; and 3) developing novel therapeutics targeting cell-state regulators of breast cancer stem cells