Computational Palaeoproteomics
ESR12 Pelagia Kyriakidou
I have studied Biochemistry & Biotechnology at the University of Thessaly in Greece. I continued my studies at the same university to get my Master's Degree in "Applications of Molecular Biology - Genetics - Diagnostic Biomarkers». During my Master's Degree I shifted my focus to bioinformatics and chose my thesis to be on bioinformatics analysis of phosphoproteomics data. To develop and improve my bioinformatics skills and proteomics knowledge I visited Prof. Dr. Juergen Cox’s research group: "Computational Systems Biochemistry" in Max Planck Institute of Biochemistry as an Erasmus+ intern. After receiving my Master's Degree I worked as a researcher fellow in projects with main focus the analysis of mass cytometry (CyTOF) and mass spectrometry based shotgun proteomics datasets.
My current general research interests include Bayesian approaches to machine learning, graph signal processing and geometric deep learning.
My PhD Project
Identification of amino acid substitutions not present in modern sequence variability, as expected in extinct organisms, can prove to be particularly challenging. In these cases, reliable options for homology searching, identification of sequence variants and amino acid substitutions, or de novo sequencing, integrated within the main search-engine would improve a lot the identification of cryptic substitutions. Finally, native integration of algorithms for blind identification of spontaneous post-translational modifications (PTMs) into the main peptide-spectrum matching (PSM) tools will allow a streamlined identification of amino acid substitutions of phylogenetic relevance. We will implement dedicated computational palaeoproteomics solutions as extensions to the established MaxQuant and Andromeda platforms. The Andromeda search engine will be further optimized to enable confident identification in such a situation. Alternatively, pre-existing de novo sequencing techniques will be further developed to be able to identify peptides and proteins independently of a sequence database. This includes clustering of single spectral de novo identifications in order to be able to confidently identify regions or domains of proteins that have sufficient MS/MS sequence coverage, and to map out their modification content. Algorithm for the search engine-driven and for the unrestricted, i.e. blind, identification of amino acid modifications will be developed for the specific challenges of ancient samples. A deep learning system will be developed and trained to be able to automatically apply domain-specific rules for the interpretation of product ion spectra. This includes special rules for the proper treatment of deamidation and other modifications in order to aid distinguishing clear cases of ancient sample-specific processes from sample preparation by-products. An expert system-informed localization score will apply this knowledge to calculate reliable localization properties for PTMs and amino acid substitutions.
Planned secondments
Secondment period of 4 months, during PhD year 2, at University of Copenhagen under Fernando Racimo’s supervision to optimize the integration of MaxQuant functions with downstream phylogeny and population reconstructions to create a pipeline to streamline data generated by MaxQuant into the following data analysis step with ESR13 support.
CV
2016
Intern as an Erasmus+ student at Prof. Dr. Juergen Cox’s research group: "Computational Systems Biochemistry" in Max Planck Institute of Biochemistry, Martinsried, Bayern, Germany
2013-2016
Master’s Degree in "Applications of Molecular Biology - Genetics - Diagnostic Biomarkers" at the University of Thessaly, Dept. of Biochemistry & Biotechnology, Greece
2008-2013
Bachelor’s Degree in Biochemistry & Biotechnology at the University of Thessaly, Dept. of Biochemistry & Biotechnology, Greece
Publications
Sung-Huan Yu, Pelagia Kyriakidou, Jürgen Cox. Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification. J. Proteome Res., September 2020, PMID: 32892627
Xiao Qin, Jahangir Sufi, Petra Vlckova, Pelagia Kyriakidou, Sophie E Acton, Vivian S W Li, Mark Nitz, Christopher J Tape. Cell-type-specific signaling networks in heterocellular organoids. Nature Methods, February 2020, PMID 32066960
Vlastaridis P, Kyriakidou P, Chaliotis A, Van de Peer Y, Oliver SG, Amoutzias. Estimating the total number of phosphoproteins and phosphorylation sites in eukaryotic proteomes. GD Gigascience, February 2017, PMID 28327990
