Development of computational approaches for high precision and accuracy protein quantification
Development of a machine learning tool to extract peptide stability patterns and predict peptide stability for new sequences, and of methods to explore the relationship between quantitative peptides and their sequence, post-translational modifications, polymorphisms, and point mutations.
To predict peptide stability we will use the stability data of over 150,000 unique tryptic peptides from the human proteome through an active collaboration with M. Wilhelm (P6, TUM). Peptides will be analyzed by mass spectrometry for 3-4 weeks and several semi-supervised classification algorithms will be evaluated to build a classification model of peptide stability. Moreover, the models will be used to predict peptide stability for new sequences, and the knowledge generated will be integrated back to ProteomeTools and ProteomicsDB. Finally, we will explore the relationship between quantitative peptides and their sequence, post-translational modifications, polymorphisms and point mutations to identify properties and sequence variants that exhibit a significant relation with the quantitative behavior of peptides.
Knowledge of machine learning algorithms, bioinformatics, and molecular biology.
A machine learning model to classify stable from non-stable peptides and predict the stability of new peptide sequences, and a map of the quantitative peptides concerning the known sites of peptide modifications, polymorphisms and point mutations.
Host: TUM (M. Wilhelm), Duration: 3 Months; When: Year 2, Goal: Integration of results within ProteomicsDB.
Host: EMBO (B. Pulverer), Duration: 2 Weeks; When: Year 3, Goal: Scientific writing and editing.
Host: ANAXOMICS (J. Farrés), Duration: 1 Month; When: Year 3, Goal: Network analysis.
Enrolment in doctoral programs
PhD in Bioinformatics from Universitat Pompeu Fabra.
1. Chiva, C., Pastor, O., Trilla-Fuertes, L., Gámez-Pozo, A., Fresno Vara, J. Á., & Sabidó, E. (2019). Isotopologue Multipoint Calibration for Proteomics Biomarker Quantification in Clinical Practice. Analytical Chemistry, 91(8), 4934–4938. https://doi.org/10.1021/acs.analchem.8b05802
2. Borràs, E., & Sabidó, E. (2018). DIA+: A Data-Independent Acquisition Method Combining Multiple Precursor Charges to Improve Peptide Signal. Analytical Chemistry, 90(21), 12339–12341. https://doi.org/10.1021/acs.analchem.8b03418
3. Chiva, C., & Sabidó, E. (2017). Peptide Selection for Targeted Protein Quantitation. Journal of Proteome Research, 16(3), 1376–1380. https://doi.org/10.1021/acs.jproteome.6b00115