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Proje Grubu: EEEAG Sayfa Sayısı: 55 Proje No: 121E326 Proje Bitiş Tarihi: 01.08.2022 Metin Dili: Türkçe İndeks Tarihi: 12-10-2022

Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini

Öz:
Enfeksiyon hastalıklarını önleme ve tedavi etmek için, B-hücresi epitoplarının belirlenmesi önemlidir. B hücresi epitopu tanımlaması, epitop bazlı ası gelistirme, immünodiagnostik testler, antikor üretimi, hastalık teshisi ve tedavisindeki en önemli adımlardan biridir. Bununla birlikte, epitop haritalamada deneysel yöntemlerin kullanılması çok zaman alıcı ve maliyetli olup yogun çalısma gerektirmektedir. Antijenik epitop bölgelerinin in silico analizler ile tespit edilerek deneysel olarak dogrulanması gereken epitop sayısının azaltılması, SARS-CoV-2 gibi pandemiye neden olan bir hastalıga, hızlı tedavi gelistirmek bakımından kritik bir öneme sahiptir. Bununla birlikte, hesaplamalı yöntemler ile epitop bölgesi belirlemek için sınırlı sayıda çalısma bulundugu görülmektedir. Proje kapsamında, genetik benzerliginden dolayı SARS-CoV virüsü ve B hücresi için deneysel yollarla belirlenmis epitop verisi kullanılarak, SARS-CoV-2 için aday epitop bölgelerinin belirlenmesi amaçlanmıstır. Bu amaç dogrultusunda iki yeni yöntem gelistirilmistir. Bunlardan birincisi bulanık mantık tabanlı kollektif bir ögrenme yöntemi, ikincisi ise hibrit bir makine ögrenmesi (SMOTE-RF-SVM) yöntemidir. Gelistirilen bulanık mantık tabanlı kollektif ögrenme yöntemi ile egitilen model, SARS-CoV, B hücresi epitop verilerini %0,083 hata ile sınıflandırılmıstır. SARS-CoV için basarılı olan bu model ile SARS-CoV-2 için epitop tahmini yapılmıstır. Tahmin edilen epitoplar, BepiPred sunucusu tarafından tahmin edilen ve immünoinformatik çalısmaları tarafından belirlenen epitop dizileri ile karsılastırılarak sunulmustur. Gelistirilen hibrit makine ögrenmesi yaklasımı ise Rastgele Orman ve Destek Vektör Makinesi yöntemi tabanlı olup, gelistirilen model SARS-CoV ve B hücresi verileri ile egitilmistir. Veri setlerinde sınıf dagılımının dengesiz olması nedeniyle sentetik azınlık asırı örnekleme teknigi (synthetic minority oversampling technique -SMOTE) kullanılarak veri setleri dengeli hale getirilmistir (yani epitop ve non-epitop örnek sayıları esitlenmistir). Dengeli hale getirilen veri setlerinde, SARS-CoV veri seti için epitop tahmin performansı %94 AUC ve B hücresi veri seti için ise epitop tahmini %95,6 AUC olarak olarak elde edilmistir. Gelistirilen hibrit yöntem ile SARS-CoV ve B hücresi verisinde egitilen model kullanılarak, SARS-CoV-2 spike protein için mevcut 20312 peptidden 252 tanesi aday epitop olarak belirlenmistir. Belirlenen epitoplar AllerTOP 2.0, VaxiJen 2.0 ve ToxinPred araçları ile analiz edilerek alerjik, antijen olmayan ve toksik epitoplar elimine edilmistir. Sonuç olarak protein bazlı COVID-19 ası tasarımında kullanılabilecek alerjik olmayan, antijenitesi yüksek ve toksik olmayan 11 epitop adayı önerilmistir. Gelistirilen epitop tahmin yöntemlerinin, basta SARS-CoV-2 ve olası mutasyonları olmak üzere koronavirüs ailesinin gelecekteki salgınlarına karsı etkili asılar ve ilaçlar tasarlamaya yardımcı olacagı öngörülmektedir.
Anahtar Kelime: SARS-CoV-2 B Hücresi Epitop tahmini Spike protein In-silico analiz.

Konular: Mühendislik, Elektrik ve Elektronik

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Öz:
Identifying B-cell epitopes is important for preventing and treating infectious diseases. B cell epitope identification is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. However, using experimental methods in epitope mapping is very time-consuming and costly and requires intensive work. Reducing the number of epitopes that need to be experimentally verified by detecting antigenic epitope regions by in silico analysis is of critical importance in terms of developing a rapid treatment for a pandemic-causing disease such as SARS-CoV-2. However, there seems to be a limited number of studies to determine the epitope region by computational methods. Within the scope of the study, two new methods have been developed to identify candidate epitope regions for SARS-CoV-2, using experimentally determined epitope data for SARSCoV virus and B cell, considering their genetic similarity: an ensemble based fuzzy learning method and a hybrid machine learning (SMOTE-RF-SVM) method. It has been observed that the model trained with the developed fuzzy-based collective learning method classifies SARSCoV, B cell epitope data with an error of 0.083%. With this successful model for SARS-CoV, epitope prediction was made for SARS-CoV-2. Predicted epitopes were compared with epitope sequences predicted by the BepiPred server and determined by immunoinformatics studies. In the developed hybrid machine learning approach, the model was trained with SARS-CoV and B cell data by using Random Forest (RF) and Support Vector Machine (SVM) methods. Due to the unbalanced class distribution in the data set, artificial data were produced by using the synthetic minority oversampling technique (SMOTE). In balanced datasets, the predictive performance for SARS-CoV was 94% AUC and 95.6% for B-cell. Using the developed hybrid method and the model trained on SARS-CoV and B cell data, 252 of the 20312 peptides available for the SARS-CoV-2 spike protein were identified as candidate epitopes. The identified epitopes were analyzed with the AllerTOP 2.0, VaxiJen 2.0, and ToxinPred tools, eliminating allergic, non-antigen, and toxic epitopes. As a result, 11 nonallergic, highly antigenic, and non-toxic epitope candidates that can be used in protein-based COVID-19 vaccine design have been proposed. We hope that the developed epitope prediction methods will help design effective vaccines and drugs against future outbreaks of the coronavirus family, particularly SARS-CoV-2 and its possible mutations.
Anahtar Kelime:

Konular: Mühendislik, Elektrik ve Elektronik
Erişim Türü: Erişime Açık
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APA ÖZGER Z, CİHAN P (2022). Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. , 0 - 55.
Chicago ÖZGER ZEYNEP BANU,CİHAN Pınar Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. (2022): 0 - 55.
MLA ÖZGER ZEYNEP BANU,CİHAN Pınar Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. , 2022, ss.0 - 55.
AMA ÖZGER Z,CİHAN P Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. . 2022; 0 - 55.
Vancouver ÖZGER Z,CİHAN P Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. . 2022; 0 - 55.
IEEE ÖZGER Z,CİHAN P "Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini." , ss.0 - 55, 2022.
ISNAD ÖZGER, ZEYNEP BANU - CİHAN, Pınar. "Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini". (2022), 0-55.
APA ÖZGER Z, CİHAN P (2022). Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. , 0 - 55.
Chicago ÖZGER ZEYNEP BANU,CİHAN Pınar Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. (2022): 0 - 55.
MLA ÖZGER ZEYNEP BANU,CİHAN Pınar Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. , 2022, ss.0 - 55.
AMA ÖZGER Z,CİHAN P Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. . 2022; 0 - 55.
Vancouver ÖZGER Z,CİHAN P Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini. . 2022; 0 - 55.
IEEE ÖZGER Z,CİHAN P "Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini." , ss.0 - 55, 2022.
ISNAD ÖZGER, ZEYNEP BANU - CİHAN, Pınar. "Yapay Zeka Yöntemleri Ile Sars-Cov-2 Için Epitop Tahmini". (2022), 0-55.