Database & Software development • Network analysis • Machine learning • Proteomics • Protein function • Cancer
Making sense of proteins on a system wide level is the next frontier after we have mastered interpretation genetic data reasonably well. Proteins are not static molecules but are constantly changed throughout their lifetime. They become posttranvslationally modified (PTM, e.g. phosphorylated), can be cleaved and shortened, change localization inside or outside a cell and interact with other proteins and biomolecules. Each and any of these events can fundamentally change the properties of the protein including it’s function activity and stability. Thus, the genetic information stored in little over 20,000 genes gives rise to millions of distinct ‘proteoforms’.
We believe that the key to many disease processes – and their treatment – lies in the dynamics of this high dimensional proteoform space. In the Lange Lab we are interested in developing algorithms, software and databases to improve analysis of big mass spectrometric proteomics datasets, improve the prediction of protein function, predict the impact of missense mutations and to identify system-wide network-effects of proteoform perturbation. In our work we use network analysis, machine learning, database concepts (e.g. Hadoop, MapReduce, MongoDB, MySQL, …) and statistical approaches.
Our ultimate goal ist to enable more accurate personalized treatment recommendations using established and next generation targeted drugs based on their individual proteome composition and dynamics.