Thesis supervisor: Gábor Tusnády
Location of studies (in Hungarian): Institute of Molecular Life Sciences, Research Centre for Natural Sciences, HUN-REN Abbreviation of location of studies: H-REN
Description of the research topic:
"Transmembrane proteins are a key player in living organisms, performing a wide variety of biological functions, from information transfer to metabolism and energy production. They are one of the most important targets of drugs, as illustrated by the fact that half of the drugs on the market today interact with transmembrane proteins to exert their effects. They act by forming a static, stable or transient, transition complex with other proteins. The elucidation of these protein-protein interactions in a living cell is a major contribution to understanding the function of the cell at the molecular level and thus to unraveling the molecular mechanisms of various diseases and to designing new drug targets.
Investigating the properties and structure of transmembrane proteins is extremely difficult and costly using laboratory tools, due to the inherent dual properties of these proteins. Although the huge progress in the field of mathematical models for artificial intelligence in the last decade has led to a big explosion in the field of protein structure prediction (see e.g. AlphaFold2, AF2), the theoretical determination of protein complexes and transient protein-protein interactions is still a challenge for scientists, especially in the field of transmembrane proteins. Indeed, these techniques can model the monomeric structure of globular proteins with very high accuracy, but this accuracy depends to a large extent on the number of related template proteins in the training set. Therefore, the accuracy of the estimation is greatly reduced for metagenomes and transmembrane proteins. Furthermore, for transmembrane proteins, the spatial constraints imposed by the double lipid layer are not taken into account by the algorithms. Although the AF2-multimer method is able to model the structure of oligomeric proteins, the accuracy is even lower.
Using algorithms (Tmdet, Cctop) and databases (TmAlphaFold, UniTmp) already developed in the research group, the student's task is to determine the protein families for transmembrane proteins that are incorrectly estimated by AF2 with low accuracy; to modify the AF2 procedure in such a way that the spatial constraints imposed by the double lipid layer are incorporated into the model; and create an artificial intelligence application capable of estimating the oligomeric state of transmembrane proteins and, in the case of oligomeric proteins, the homo- and hetero-oligomeric structure of the resulting homo- and hetero-oligomeric structures, both for static complexes and for trans-mediated interactions using sequence motifs in disordered protein regions. The latter interactions would also be validated by in vitro methods for some physiologically or disease relevant cases. The results of the PhD work will provide very important data both for drug discovery and for the treatment of various diseases.
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Required language skills: English Further requirements: programming knowledge (python)
Number of students who can be accepted: 1
Deadline for application: 2024-05-31
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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