MSc Thesis Defense: Mustafa Malkoç, EVOLUTION OF NUCLEOTIDE EXCISION REPAIR PROTEINS,
EVOLUTION OF NUCLEOTIDE EXCISION REPAIR PROTEINS
Mustafa Malkoç
Molecular Biology, Genetics, and Bioengineering, MSc. Thesis, 2025
Thesis Jury
Assoc. Prof. Ogün Adebali (Thesis Supervisor)
Prof. Levent Öztürk
Asst. Prof. İsmail Kudret Sağlam
Date & Time: December 19th, 2025 – 1:00 PM
Place: FENS L030
Zoom: https://sabanciuniv.zoom.us/j/
Keywords : Nucleotide Excision Repair, Evolution, Phylogenetics, Coevolution, NER
Abstract
Nucleotide Excision Repair (NER) is a DNA repair mechanism found in almost all organisms, fixing primarily bulky adducts. The pathway uses two distinct damage recognition methods (Global Genome and Transcription-Coupled NER) but relies on shared core machinery for lesion removal. In this study, we mapped the evolutionary history of NER by analyzing 86 repair-associated proteins across 174 eukaryotic species. We utilized a computational pipeline to construct phylogenies and manually investigated trees for ortholog assignments. Our results reveal a contrast between the stability of the core machinery and the flexibility of damage recognition modules. Core proteins responsible for DNA unwinding and excising (e.g., XPB, XPD, XPF, XPG, and TFIIH) are present in nearly all eukaryotes. However, damage recognition proteins vary significantly: fungi and certain insects lost key genes like DDB2, CSA, and CSB. We also observed that plants expanded their repair toolkit by retaining multiple gene copies, such as RPA subunits, while vertebrates retained duplicates from whole-genome duplication events. We used PHACE, a phylogeny-aware tool, to detect protein coevolution based on amino acid changes. We recovered known interactions within transcription-coupled repair and detected consistent signals between KAT5 and the XPA-RPA complex subunits. This signal likely reflects functional coupling between KAT5 and the XPA-RPA repair complex, supporting the sensitivity of our method to detect co-evolution beyond direct physical interactions. Finally, our findings clarify the evolutionary landscape of NER and demonstrate our pipeline’s promise for predicting novel functional networks.