İnsana ait protein fonksiyonlarının protein haritalama teknikleri ve derin öğrenme modeli ile tahmin edilmesi

Öz

Canlıların moleküler mekanizmasının anlaşılabilmesi için protein fonksiyonları önem arz etmektedir. Proteinlere ait fonksiyonlar belirlenirken, proteinlerin yapılarından yararlanılır. Protein fonksiyonları daha çok, karakterize edilmemiş protein dizilimlerinin anotasyonlarının belirleyebilmek, canlıların hücresel mekanizmalarını anlayabilmek, genlerde ya da proteinlerde hastalığa neden olan fonksiyonel değişiklileri belirleyebilmek ve hastalıkların önlenebilmesi, tedavi edilebilmesi ve teşhisi için yeni yaklaşımlar geliştirmek için kullanılmaktadır. Protein fonksiyonları deneysel yöntemlerle etkin bir şekilde belirlenebilmektedir. Ancak, deneysel yöntemlerin zaman alması ve çok sayıda kimyasal süreçten geçmesi, bu aşamaların yavaş ve maliyetli olmasına neden olmaktadır. Bunlara ek olarak, fonksiyonel yapısı ve dizilimi bilinen bazı proteinlerin anotasyonları deneysel süreçlerden dolayı halen belirlenememektedir. Bu gibi nedenler ve dezavantajlardan dolayı hesaplama-tabanlı uygulamalara ihtiyaç duyulmaktadır. Hesaplama-tabanlı uygulamalar için genellikle yapay zeka algoritmaları kullanılmaktadır. Yapay zeka yöntemleri ile protein fonksiyonlarının tahmin edilebilmesi için protein dizilimlerinin belirli haritalama yöntemleri ile sayısal hale getirilmesi gerekmektedir. Bu çalışmada, belirli protein haritalama teknikleri kullanılarak gen ontoloji tabanlı protein fonksiyonlarının tahmini gerçekleştirilmiştir. Çalışma, protein verilerinin elde edilmesi, protein dizilimlerinin sayısallaştırılması, protein fonksiyonlarının sınıflandırılması ve protein haritalama tekniklerinin performanslılarının belirlenmesi olmak üzere dört farklı aşamadan oluşmaktadır. Çalışmanın sonunda, biyolojik süreç kategorisinde en iyi doğruluk ve AUC skoru PAM250 protein haritalama tekniği ile elde edilmiş ve sırasıyla %69 ve %88 olarak hesaplanmıştır. Hücresel bileşen kategorisinde ise en iyi doğruluk ve AUC değer, sırasıyla %64 ve %89 oranı ile FIBHASH protein haritalama tekniği ile elde edilmiştir. Moleküler fonksiyon kategorisinde ise %64 AUC oranı ve %89 doğruluk değeri ile en iyi sonuç FIBHASH ile elde edilmiştir. Önerilen yapay zekâ yöntemi ile protein sayısal haritalama tekniklerinin birlikte kullanımının, protein fonksiyonlarının tahmin edilmesinde etken bir role sahip olduğu gözlemlenmiştir.

Anahtar Kelimeler

• Protein fonksiyonları
• Derin öğrenme
• Protein haritalama teknikleri
• İki yönlü uzun-kısa vadeli bellek

Prediction of human protein functions with protein mapping techniques and deep learning model

Abstract

Protein functions are important for understanding the molecular mechanism of living organisms. Protein structures are used when determining the functions of proteins. Protein functions are mostly used to determine the annotations of uncharacterized protein sequences, to understand the cellular mechanisms of living things, to identify functional changes in genes or proteins that cause disease, and to develop new approaches to prevent, treat and diagnose diseases. Protein functions can be determined effectively by experimental methods. However, experimental methods take time and go through many chemical processes, causing these stages to be slow and costly. In addition to these, the annotations of some proteins whose functional structure and sequence are known cannot be specified due to experimental processes. Due to such reasons and disadvantages, computational-based approaches are needed. Artificial intelligence algorithms are generally used for computational-based applications. In order to predict protein functions with artificial intelligence methods, protein sequences must be mapped with certain mapping methods. In this study, prediction of gene ontology-based protein functions was performed using certain protein mapping techniques. The study consists of four different stages; obtaining protein data, mapping protein sequences, classifying protein functions, and determining the performance of protein mapping techniques. At the end of the study, the best accuracy and AUC score in the biological process category was obtained by the PAM250 protein mapping technique and was calculated as 69% and 88%, respectively. In the cellular component category, the best accuracy and AUC value were obtained by FIBHASH protein mapping technique with 64% and 89%, respectively. In the molecular function category, the best result was obtained with FIBHASH with 64% AUC score and 89% accuracy. It has been observed that the combined use of the proposed artificial intelligence method and protein numerical mapping techniques have an effective role in predicting protein functions.

Keywords

• Protein functions
• Deep learning
• Protein mapping techniques
• Bidirectional long-short term memory

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