Protein Folding Recognition by Artificial Neural Networks

Sena Dikici; Volkan Altuntaş

Makale detayları

Protein Folding Recognition by Artificial Neural Networks

Yazarlar:

Sena Dikici | Volkan Altuntaş

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 2, pp.95-105

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287841

pISSN:

1307-9697

Anahtar kelimeler:

Özet:

 Proteins are made up of amino acid chains and are important for the structure of the cells and their communication with each other, while regulating body chemistry. For a protein to perform its function on a cell basis, it must perform the folding process that converts the molecule into a three-dimensional structure that can interact with its target in the cell. Factors such as temperature, heavy metals or chemical conditions may cause proteins to be folded incorrectly. Incorrectly folded proteins cannot perform their role in the body. Protein folding recognition is considered a problem for biologists. Despite the template-based approaches in the literature, artificial neural networks show high performance in solving the protein folding problem. Artificial neural networks are a computational technique that uses high-specification knowledge in large data sets to help solve the problem that is being addressed and will contribute to the solution of the problem. In this study, protein folding recognition was performed by combining the results from template-based approaches with artificial neural network method using the Scope 2.06, Scope 2.07, Scope 2.08 datasets. The results in the literature were improved by the contribution of the artificial neural network method because of the experiments conducted. This study aims to contribute literature by introducing an innovative approach to the solution of the problem of protein folding recognition in the field of bioinformatics.

 Proteins are made up of amino acid chains and are important for the structure of the cells and their communication with each other, while regulating body chemistry. For a protein to perform its function on a cell basis, it must perform the folding process that converts the molecule into a three-dimensional structure that can interact with its target in the cell. Factors such as temperature, heavy metals or chemical conditions may cause proteins to be folded incorrectly. Incorrectly folded proteins cannot perform their role in the body. Protein folding recognition is considered a problem for biologists. Despite the template-based approaches in the literature, artificial neural networks show high performance in solving the protein folding problem. Artificial neural networks are a computational technique that uses high-specification knowledge in large data sets to help solve the problem that is being addressed and will contribute to the solution of the problem. In this study, protein folding recognition was performed by combining the results from template-based approaches with artificial neural network method using the Scope 2.06, Scope 2.07, Scope 2.08 datasets. The results in the literature were improved by the contribution of the artificial neural network method because of the experiments conducted. This study aims to contribute literature by introducing an innovative approach to the solution of the problem of protein folding recognition in the field of bioinformatics.

 Proteins are made up of amino acid chains and are important for the structure of the cells and their communication with each other, while regulating body chemistry. For a protein to perform its function on a cell basis, it must perform the folding process that converts the molecule into a three-dimensional structure that can interact with its target in the cell. Factors such as temperature, heavy metals or chemical conditions may cause proteins to be folded incorrectly. Incorrectly folded proteins cannot perform their role in the body. Protein folding recognition is considered a problem for biologists. Despite the template-based approaches in the literature, artificial neural networks show high performance in solving the protein folding problem. Artificial neural networks are a computational technique that uses high-specification knowledge in large data sets to help solve the problem that is being addressed and will contribute to the solution of the problem. In this study, protein folding recognition was performed by combining the results from template-based approaches with artificial neural network method using the Scope 2.06, Scope 2.07, Scope 2.08 datasets. The results in the literature were improved by the contribution of the artificial neural network method because of the experiments conducted. This study aims to contribute literature by introducing an innovative approach to the solution of the problem of protein folding recognition in the field of bioinformatics.

 Proteinler uzun aminoasit zincirlerinden oluşur ve vücut kimyasını düzenlemekle birlikte hücrelerin yapısı ve aralarındaki iletişim için öneme sahiptir. Bir proteinin hücre bazındaki görevini gerçekleştirebilmesi için, molekülü hücredeki hedefiyle etkileşime girebilecek üç boyutlu yapıya dönüştüren bir bükülme süreci olan katlanma işlemini gerçekleştirmesi gerekir. Sıcaklık, ağır metaller veya kimyasal durumlar gibi etkenler proteinlerin yanlış katlanmasına sebep olabilir. Yanlış katlanan proteinler, vücuttaki görevini yerine getiremez. Alzaymır, kistik fibrozis, deli dana hastalığı gibi hastalıklara sebep olabilir. Protein katlanması tanıma işlemi, biyologlar açısından bir problem olarak değerlendirilir. Literatürde yer alan şablon tabanlı yaklaşımlara karşın yapay sinir ağları, protein katlanması probleminin çözümüne yönelik yüksek başarım gösterir. Yapay sinir ağları, ele alınan problemin çözümü için geniş veri kümelerinde yer alan ve problemin çözümüne katkı sağlayacak bilgi kazancı yüksek özellikleri kullanan bir hesaplama tekniğidir. Bu çalışmada SCOPe 2.06, SCOPe 2.07, SCOPe 2.08 veri setleri kullanılarak şablon tabanlı yaklaşımlardan elde edilen sonuçların yapay sinir ağı yöntemi ile birleştirilerek protein katlanması tanıma işlemi gerçekleştirilmiştir. Gerçekleştirilen deneyler sonucunda yapay sinir ağı yönteminin katkısı ile literatürde yer alan sonuçların iyileştirildiği görülmüştür. Bu çalışma ile biyoinformatik alanında protein katlanması tanıma probleminin çözümüne yeni bir yaklaşım sunularak literatüre katkı sağlanması amaçlanmıştır.

Makale detayları

Protein Folding Recognition by Artificial Neural Networks

Yazarlar:

Sena Dikici | Volkan Altuntaş

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 2, pp.95-105

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287841

pISSN:

1307-9697

protein folding

artificial neural network

protein folding recognition

bioinformaticsderin öğrenme

protein katlanması

yapay sinir ağları

protein katlanması tanıma

biyoinformatik

Sayı

Makale detayları

Yazarlar: Sena Dikici | Volkan Altuntaş

Yayıncı: Gazi University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Bilişim Teknolojileri Dergisi Vol.16 Issue 2, pp.95-105

Belge Tipi: Makale

Yayın Tarihi: 4/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001287841

pISSN: 1307-9697

Sayı

Yazarlar:

Sena Dikici | Volkan Altuntaş

Yayıncı:

Gazi University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Bilişim Teknolojileri Dergisi Vol.16 Issue 2, pp.95-105

Belge Tipi:

Makale

Yayın Tarihi:

4/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001287841

pISSN:

1307-9697