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Journals / Türk Biyokimya Dergisi / 2016 / Cilt: 41 - Sayı: 6

Düzenli-düzensiz proteinlerin doğrusal ve kernel destek vektör makineleri kullanarak sınıflandırılması

Classifying ordered-disordered proteins using linear and kernel support vector machines

Çağın Kandemir ÇAVAŞ
Selen YİLDİRİM
Pages
431–436
Publication Date
2022-11-05
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics.Methods: In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM).Results: Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively.Discussion and conclusion: Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Introduction: Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein.
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Key Findings & Data:Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage.
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Conclusion & Impact:The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics.Methods: In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM).Results: Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively.Discussion and conclusion: Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Introduction: Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein. Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage. The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics.Methods: In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM).Results: Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively.Discussion and conclusion: Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.

Özet

GİRİŞ ve AMAÇ: Yerleşik düzensiz proteinler bir proteinin üçüncül yapısında deformasyonlar meydana geldiğinde oluşur. Düzensiz proteinler, DNA/RNA/proteini tanıma, bağlayıcı proteinin spesifikliği/afinitesi, moleküler parçacığı, bölünme ile aktivasyon modülasyonunda önemli bir rol oynamaktadır. Bu çalışmanın amacı biyoinformatikte çok zorlu bir sorun olan proteinin düzensiz yapıda olup olmamasının tanımlanmasıdır.YÖNTEM ve GEREÇLER: Bu çalışmada, proteinler doğrusal ve kernel (doğrusal olmayan) Destek Vektör Makineleri (DVM) kullanarak düzenli-düzensiz olarak sınıflandırılmaktadır.BULGULAR: Düzenli-düzensiz proteinleri tanımlamada doğrusal DVM ve kernel DVM yöntemlerine göre genel doğru sınıflama oranları sırasıyla %86,54 ve %94,23 bulunmaktadır.TARTIŞMA ve SONUÇ: Kernel DVM en iyi ayırt edici düzeni vermesi nedeniyle, proteinlerin düzenli-düzensiz yapıları belirlemek için çok tatmin edici bir yöntem olduğunu ifade edilebilir

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Cite This Article

ÇAVAŞ, Ç. K., YİLDİRİM, S. (2022). Düzenli-düzensiz proteinlerin doğrusal ve kernel destek vektör makineleri kullanarak sınıflandırılması. Türk Biyokimya Dergisi, Cilt: 41 - Sayı: 6, 431–436. https://ajindex.com/articles/621027

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References (13)

  1. Lesk AM. Introduction to bioinformatics. NewYork: Oxford Univer- sity Press, 2005. Tompa P. Intrinsically unstructured proteins. Trends Biochem Sci 2002;27:527-33.
  2. Khan SH, Kumar R. An overview of the importance of conforma tional flexibility in gene regulation by the transcription factors. J Biophy 2009;2009:1-9. Sandhu KS. Intrinsic disorder explains diverse nuclear roles of chromatin remodeling proteins. J Mol Recognit 2009;22:1-8.
  3. Iakoucheva LM, Brown CJ, Lawson JD, Obradovic Z, Dunker AK. Intrinsic disorder in cell-signaling and cancer-associated pro teins. J Mol Biol 2002;323:573-84.
  4. Li J, Feng Y, Wang X, Li J, Liu W, Rong L, Bao J. An overview of predictors for intrinsically disordered proteins over 2010-2014. Int J Mol Sci 2015;16:23446-62. Uversky VN. Intrinsically disordered proteins and their (disor- dered) proteomes in neurodegenerative disorders. Front Aging Neurosci 2015;7:1-6.
  5. Snyder DA, Chen Y, Denissova NG, Acton T, Aramini JM, Ciano M, et al. Comparisons of NMR spectral quality and success in crys- tallization demonstrate that NMR and X-ray crystallography are complementary methods for small protein structure determina- tion. J Am Chem Soc 2005;127:16505-11.
  6. Chen K, Kurgan LA, Ruan J. Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol 2007;7-25:1-13.
  7. Wang L, Sauer UH. OnD-CRF: predicting order and disorder in pro- teins conditional random fields. Bioinformatics 2008;24:1401-2.
  8. Yang ZR, Thomson R, McNeil P, Esnouf RM. RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics 2005;21:3369-76.
  9. Dosztanyi Z, Csizmok V, Tompa P, Simon I. The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. J Mol Biol 2005;347:827-39.
  10. Wei Z, He J, Harrison R, Tai P, Pan Y. Clustering support vector machines for protein local structure prediction. Expert Syst Appl 2007;32:518-26.
  11. Zhang G, Ge H. Support vector machine with a Pearson VII function kernel for discriminating halophilic and non-halophilic proteins. Comput Biol Chem 2013;46:16-22.
  12. Chen C, Tian Y, Zou X, Cai P, Mo J. Using pseudo-amino acid composition and support vector machine to predict protein structural class. J Theor Biol 2006;243:444-8.
  13. Pugalenthi G, Kumar KK, Suganthan PN, Gangal R. Identifica- tion of catalytic residues from protein structure using support vector machine with se