Effect of Benchmark Datasets on Protein Structure Prediction As a Concept

Year 2021, , 117 - 121, 01.12.2021

Nuh Azgınoğlu

https://doi.org/10.31590/ejosat.1014716

Abstract

Knowing the protein structures is essential in understanding the job descriptions of proteins involved in vital functions, drug design, and many more. On the other hand, protein structure prediction is an alternative bioinformatics sub-study field to shorten the process that takes a long time in the laboratory environment. Performance analyzes of the methods developed in this field are generally made on benchmark datasets. The size of the datasets directly affects the algorithm runtime. In this study, how to benchmark datasets are reflected in the results is analyzed. Within the scope of the study, two different benchmark datasets, CB513 and EVASet, and two different protein structure prediction methods, JPred and Porter, were used. The study is a source of inspiration for further studies with the idea of developing benchmark datasets that are comprehensive in terms of protein properties but contain as little data as possible in terms of data size.

Keywords

Protein structure prediction, Benchmark dataset, Concept

Supporting Institution

Kayseri University Scientific Research Projects Unit

Project Number

FHD-2021-1045

Thanks

This study was supported as Project Number: FHD-2021-1045 by Kayseri University Scientific Research Projects Unit. We thank Kayseri University Scientific Research Projects unit for their contributions.

Kıyaslama Veri Kümelerinin Protein Yapı Tahminine Etkisi: Bir Kavram Çalışması

Abstract

Protein yapılarının bilinmesi hayati fonksiyonlarda görev alan proteinlerin görev tanımlarının anlaşılabilmesi, ilaç tasarımı ve daha birçok açıdan öneme sahiptir. Protein yapı tahmini ise laboratuvar ortamında oldukça uzun zaman alan süreci kısaltmak için alternatif bir biyoinformatik alt çalışma alanıdır. Bu alanda geliştirilen yöntemlerin performans analizleri genel itibariyle kıyaslama (benchmark) veri kümeleri üzerinden yapılmaktadır. Veri kümelerinin büyüklüğü algoritma çalışma zamanlarına doğrudan etki etmektedir. Bu çalışmada kapsamında kıyaslama veri kümelerinin sonuçlara nasıl yansıdığı analiz edilmiştir. Çalışma kapsamında iki CB513 ve EVASet olmak üzere iki farklı kıyaslama veri kümesi, JPred ve Porter olmak üzere iki farklı protein yapı tahmini yöntemi kullanılmıştır. Çalışma, protein özellikleri açısından geniş kapsamlı ancak, veri büyüklüğü anlamında olabildiğince az veri içerecek olan benchmark veri kümeleri geliştirme fikri itibariyle sonraki çalışmalar için esin kaynağı niteliğindedir.

Keywords

Protein yapı tahmini, Kıyaslama veri kümesi, Kavram

Project Number

FHD-2021-1045

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