Analysis and prediction of N-terminal protein sorting signals based on proteogenomics data

点击翻译按钮获取中文摘要

Analysis of cellular sorting signals is a cornerstone of protein function prediction, but our understand-ing of these important functional motifs is limited by scarce experimental data, and the accuracy of even the best available prediction methods is essentially unknown for all but a handful of well studied examples. These methods are trained on a very small sample of experimentally confirmed sorting signals and largely rely on generalized signal motifs, ignoring non-standard cases. The overall goal of the project is to expand our knowledge about cleavable N-terminal signals, including various types of signal peptides, mitochondrial targeting signals and chloroplast transit peptides. Based on massive proteogenomics data from a broad range of organisms a quantum leap in signal peptide research will be achieved, with tens of thousands of new signal sequences determined, classified, and analyzed. This work will allow to better understand sequence requirements imposed on signal sequences in different cellular contexts, to gain a more complete knowledge of their structure and function, and to discover novel archetypes of targeting sequences that have been elusive so far. Analysis of mutation spectra, evolutionary conservation of cleavage site positions as well as of gain and loss of entire signals along the evolutionary tree will shed light on what factors determine export pathway specificity, secretion efficiency, and disease propensity. Finally, novel prediction algorithms for cellular sorting signals will be developed.