İlmek, Özkan

Profile URL
https://hdl.handle.net/20.500.12573/2794
Name Variants
İlmek, Özkan
Job Title
Arş. Gör.
Email Address
ozkan.ilmek@agu.edu.tr
Main Affiliation
04.02. Moleküler Biyoloji ve Genetik
Status
Current Staff
Website
ORCID ID
0000-0002-4778-8689
Scopus Author ID
Turkish CoHE Profile ID
4DDF8D1EB2F8A98F
Google Scholar ID
WoS Researcher ID
No research topics data found.

Sustainable Development Goals

NO POVERTY1
NO POVERTY
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ZERO HUNGER2
ZERO HUNGER
0
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
1
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
0
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
0
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
0
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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LIFE ON LAND15
LIFE ON LAND
0
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
0
Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
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Scholarly Output

1

Articles

0

Views / Downloads

113/43

Supervised MSc Theses

1

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

0

Patents

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Projects

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WoS Citations per Publication

0.00

Scopus Citations per Publication

0.00

Open Access Source

1

Supervised Theses

1

Journals data is not available

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  • Master Thesis
    SARS-CoV-2 Omikron Varyantına Özgü Tek Domainli Antikorların Protein-Protein Kenetlenmesi Yaklaşımlarıyla Tanımlanması
    (Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) İlmek, Özkan; Güner, Şerife Ayaz
    Omicron, became the dominant variant in 2022 in terms of spreading rate, has managed to evade from an immune system of patients due to its unique mutations. Single domain antibodies (sdAb) which are functionally important parts of conventional antibodies are commonly used for diagnosis and treatment. Although there are many sdAbs developed to combat coronavirus in recent years, their effectiveness against Omicron variant has not been sufficiently tested and the effect of mutations regarding neutralization level is not clear. In this study, structure modelling of 850 sdAb sequences obtained from previous studies were generated using AlphaFold 2 and effectiveness of these sdAbs against Omicron variant was tested via protein-protein docking approach. In the docking process, within a realistic approach, missing residues were completed into Spike protein PDB structures, and Spike protein homotrimer structure in closed state conformation was used. Finally, top 1000 and top 100 scores are determined as a threshold value for different protein-protein docking scoring functions such as HDOCK, PRODIGY and Bluues. sdAbs that have successful results for Omicron variant were listed. There were 4 sdAbs which exceed the threshold values after 2 different docking experiments against the Omicron variant. The scripting codes and methodological approach developed within this thesis can be used against new SARS-CoV-2 variants that may emerge in the future or other diseases.