PhD Scientific Days 2020

Molecular Sciences III. Lectures

Translocatome: A Novel Resource for the Analysis of Protein Translocation Between Cellular Organelles

Előadó neve

Dr. Mendik, Péter

Előadó munkahelye

Department of Medical Chemistry, Molecular Biology and Pathobiochemistry

Előadó telefonszáma

+36 30 474 0319

Előadó e-mail címe

mendik.peter@med.semmelweis-univ.hu

Az előadás címe

Translocatome: A Novel Resource for the Analysis of Protein Translocation Between Cellular Organelles

Szerző(k) neve és munkahelye

Péter Mendik1, Levente Dobronyi1, Ferenc Hári1, Csaba Kerepesi2,3, Leonardo Maia-Moco4,1, Donat Buszlai1, Peter Csermely1,5, Daniel V Veres5,1

1 Department of Medical Chemistry, Semmelweis University, Budapest, HU;
2 Institute for Computer Science and Control (MTA SZTAKI), Hungarian Academy of Sciences, Budapest, HU;
3 Institute of Mathematics, Eötvös Loránd University, Budapest, HU;
4 Cancer Biology and Epigenetics Group, Research Center of Portuguese Oncology Institute of Porto, PT;
5 Turbine Ltd., Budapest, HU

Szekció

Molecular Sciences III. Lectures

Language of the presentation

Hungarian

Section, first choice

Molecular Sciences

Section, second choice

Theoretical and Translational Medicine

Összefoglaló szövege

Subcellular localization of proteins is essential in the spatial and temporal organisation of biological processes. Translocating proteins play a key role in the reconfiguration of cellular functions after environmental changes, as well as in embryonic or disease development. Protein translocation as a systems biology phenomenon, refers to the regulated movement of a protein between subcellular compartments. Translocation changes the interaction partners and leads to altered function(s) of translocating proteins. Though several protein translocations are well characterized, the systematic analysis of this phenomenon was still missing.

The Translocatome database (translocatome.linkgroup.hu) contains 213 manually curated human translocating proteins. The database contains information about the details of the translocating proteins’ structure, localization, regulation and interacting partners. Based on the manually curated proteins we implemented a machine learning algorithm using the XGBoost learning algorithm. To predict the translocation probabilities of 13 066 human proteins we used 139 human non-translocating proteins as a negative learning set and annotated each protein in our database with functional (Gene Ontology) and network parameters. With this method, we identified 1133 high-confidence and 3268 low-confidence translocating proteins.

The Translocatome database enables a systematic analysis of protein translocation. Thus, we’ll be able to better understand the role translocating proteins play in cellular behaviour and certain cellular disfunctions. As translocating proteins play a key role in cancer progression the better understanding of this phenomenon may lead to the recognition of new therapeutic targets.

Additional Information

Supervisors: Péter Csermely and Dániel Veres

Bemutatás módja

Szóbeli

Kind

Szabad

Status

elfogadva

Accepted presentation method

szóbeli

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

4782

Start

10:25

End

10:40

Authors (legacy)

Péter Mendik1, Levente Dobronyi1, Ferenc Hári1, Csaba Kerepesi2,3, Leonardo Maia-Moco4,1, Donat Buszlai1, Peter Csermely1,5, Daniel V Veres5,1

1 Department of Medical Chemistry, Semmelweis University, Budapest, HU;
2 Institute for Computer Science and Control (MTA SZTAKI), Hungarian Academy of Sciences, Budapest, HU;
3 Institute of Mathematics, Eötvös Loránd University, Budapest, HU;
4 Cancer Biology and Epigenetics Group, Research Center of Portuguese Oncology Institute of Porto, PT;
5 Turbine Ltd., Budapest, HU