Sophie Weber, BSc, MSc - no institution - BSc, MSc

Sophie Weber

BSc, MSc

no institution

BSc, MSc

Vienna, Vienna | Austria

Main Specialties: Allergy & Immunology, Biochemical Genetics, Biology, Epidemiology, Infectious Disease, Medical Genetics, Medical Humanities, Medical Toxicology, Molecular Genetic Pathology, Neonatal-Perinatal Medicine, Neuropathology, Oncology, Public Health, Rheumatology

Additional Specialties: Software and Computational Biology


Top Author

Sophie Weber, BSc, MSc - no institution - BSc, MSc

Sophie Weber

BSc, MSc

Introduction

I am a full-stack developer with considerable experience in agile project management, requirements engineering, software architecture, software development, documentation and delivery in the area of Health & Public Services.
Additionally, I have significant competence in Structural and Computational Biology with specification in MD-Simulations, Molecular Omics Technologies, RNA-Seq data analysis and software engineering in the field of Structure-Based Drug Design.

Primary Affiliation: no institution - Vienna, Vienna , Austria

Specialties:

Additional Specialties:

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View Sophie Weber’s Resume / CV

Education

Oct 2016
Molecular Structural Biology
MSc
Molecular Structural- and Computational Biology, Programming, Data Analysis, Omics, MD-Simulations, NMR, Crystallography and Biophysics
Oct 2013
Molecular Biology
BSc
Bioinformatics, Chemistry (Bio, Analytical, Physical, Organic), Biophysics, Proteomics, Cell Biology and Immunology

Publications

5Publications

182Reads

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A 3D grid based tool kit for protein crystal structure ensemble analysis

Authors:
Sophie Weber

Weber, Sophie. A 3D grid based tool kit for protein crystal structure ensemble analysis. Diss. uniwien, 2018.

http://othes.univie.ac.at/52455/

Abstract

The evaluation of protein crystal structures is crucial for understanding protein function and drug development. The analysis of single crystal structures is well established, especially for small chemical compounds. In contrast, visualising and aggregating the relevant information of the variety of whole protein ensembles is still rare and time consuming to perform manually. Since a plethora of protein-ligand complex structures are available in pharmaceutical industry, the analysis of protein crystal structure ensembles has become a new task in the field of drug design. Furthermore, frequent mutations in the binding site of oncogenic targets additionally enhances the requirement for fast analysis tools. In this study, a tool kit to rapidly access and exploit the available 3D structural knowledge was developed. The visualisation and qualitative analysis provides an overview of structural differences and should help to drive decisions for drug design. Grid maps are the main topic of this thesis. They are defined by values of certain structural properties distributed on a 3D grid. Thus, properties can be stored and manipulated, such as potential binding energies of protein-ligand complexes. Among the possible applications of grids in medical chemistry, protein-ligand interaction ”hot spots” are relevant for rational design of new compounds. Hot spots are calculated potential energies, indicating high propensity of a target protein for compound binding, and can be represented as contour surfaces. These interaction hot spots define favourable regions for binding of certain compound features and are crucial to form protein-ligand complexes. The tool kit presented in this thesis provides insight into interaction hot spots of protein-ligand complex ensembles and thus it offers new compound design opportunities. The algorithm is able to extract single hot spots according to a new crystal structure as reference, enabling a spatial resolution of relevant hot spots. These results can be compared pairwise with an ensemble applying the grid analysis script. In addition, it provides combined information of visual and qualitative analysis of 3D grid maps. A heat map representation of the calculated energy differences and similarities assists to evaluate the knowledge gain.

View Article
June 2018

Modeling and CFD simulation of nutrient distribution in picoliter bioreactors for bacterial growth studies on single-cell level.

Lab Chip 2015 Nov 8;15(21):4177-86. Epub 2015 Sep 8.

Forschungszentrum Jülich, IBG-1: Biotechnology, 52425 Jülich, Germany.

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Source
http://dx.doi.org/10.1039/c5lc00646eDOI Listing
November 2015
15 Reads