Using Deep Learning (CNN, RNN, LSTM, GRU) methods for the prediction of Protein Secondary Structure

Year 2022, , 43 - 52, 28.06.2022

Ezgi Çakmak , İhsan Hakan Selvi

https://doi.org/10.26650/acin.1008075

Abstract

Proteins play a crucial function in the biological processes of living organisms. Knowing the function of the protein offers significant insight into future biological and medical research. Since a protein’s shape determines its function, it is important to understand the protein’s 3D structure. Although experimental methods such as X-ray crystallography and nuclear magnetic resonance (NMR) have been used to examine the shape of proteins, so far the results have been insufficient. As a result, predicting the 3D structure of proteins is crucial. Determining the 3D structure of a protein from its primary structure is challenging. Therefore, predicting the protein secondary structure becomes important for studying its structure and function. Many emerging methods, including machine learning, as well as deep learning, have been used to predict the secondary structure of proteins and comprise a crucial part of Structural Bioinformatics. The goal of this study is to compare the results generated by predictive models that were created using the four most frequently utilized deep learning methods: convolutional neural networks (CNN), recurrent neural networks (RNN), long short term memory networks (LSTM), and gated recurrent units (GRU). The CB513 dataset was used to train and test these models, and performance evaluation metrics viz. accuracy, f1 score, recall, and precision were applied. The CNN, RNN, LSTM, and GRU models had an accuracy of 82.54%, 82.06%, 81.1%, and 81.48%, respectively.

Keywords

Protein Secondary Structure Prediction, CNN, RNN, GRU

Derin Öğrenme (CNN, RNN, LSTM, GRU) Kullanarak Protein İkincil Yapı Tahmini

Abstract

Protein, canlı organizmaların biyolojik süreçlerinde çok önemli bir role sahiptir. Proteinin işlevini bilmek, biyoloji ve tıp alanında gelecekteki çalışmalara büyük katkı sağlar. Proteinin fonksiyonunu anlamak için üç boyutlu yapısını anlamak önemlidir. Protein yapısını çözümlemek için X-ışını kristalografisi ve NMR gibi deneysel yöntemler kullanılmasına rağmen, sonuçların yetersiz olduğu kanıtlanmıştır. Bu nedenle, proteinlerin üç boyutlu yapısının tahmini, süreçlerdeki en önemli konulardan biri haline gelmektedir. Birincil yapı olarak bilinen amino asit dizisinden proteinin üç boyutlu şeklinin belirlenmesi zorlu olarak tanımlandığından, ikincil yapının tahmin edilmesi bu konuda önemli bir rol oynamaktadır. Literatürde protein ikincil yapısını tahmin etmek için makine öğrenmesi ve son zamanlarda derin öğrenme gibi birçok yöntem kullanılmıştır. Bu makale, yaygın olarak uygulanan dört derin öğrenme yöntemi olan CNN, RNN, LSTM ve GRU kullanılarak geliştirilen modellerin performanslarının bir karşılaştırmasını sağlamayı amaçlamaktadır. Bu modellerin eğitimi ve test edilmesi amacıyla CB513 veri seti kullanılmış, buna ek olarak doğruluk, f1 skoru, doğruluk ve kesinlik gibi performans değerlendirme ölçütleri uygulanmıştır. CNN, RNN, LSTM ve GRU modellerinin doğruluk oranları sırasıyla %82,54, %82,06, %81,1 ve %81,48’dir.

Keywords

Protein İkincil Yapı Tahmini, CNN, RNN, GRU

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