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Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis

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Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis

Content Provider	Springer Nature Link
Author	Zinati, Zahra Alemzadeh, Abbas KayvanJoo, Amir Hossein
Copyright Year	2016
Abstract	As an extended gamut of integral membrane (extrinsic) proteins, and based on their transporting specificities, P-type ATPases include five subfamilies in Arabidopsis, inter alia, P$_{4}$ATPases (phospholipid-transporting ATPase), P$_{3A}$ATPases (plasma membrane H$^{+}$ pumps), P$_{2A}$ and P$_{2B}$ATPases (Ca$^{2+}$ pumps) and P$_{1B}$ ATPases (heavy metal pumps). Although, many different computational methods have been developed to predict substrate specificity of unknown proteins, further investigation needs to improve the efficiency and performance of the predicators. In this study, various attribute weighting and supervised clustering algorithms were employed to identify the main amino acid composition attributes, which can influence the substrate specificity of ATPase pumps, classify protein pumps and predict the substrate specificity of uncharacterized ATPase pumps. The results of this study indicate that both non-reduced coefficients pertaining to absorption and Cys extinction within 280 nm, the frequencies of hydrogen, Ala, Val, carbon, hydrophilic residues, the counts of Val, Asn, Ser, Arg, Phe, Tyr, hydrophilic residues, Phe-Phe, Ala-Ile, Phe-Leu, Val-Ala and length are specified as the most important amino acid attributes through applying the whole attribute weighting models. Here, learning algorithms engineered in a predictive machine (Naive Bays) is proposed to foresee the Q9LVV1 and O22180 substrate specificities (P-type ATPase like proteins) with 100 % prediction confidence. For the first time, our analysis demonstrated promising application of bioinformatics algorithms in classifying ATPases pumps. Moreover, we suggest the predictive systems that can assist towards the prediction of the substrate specificity of any new ATPase pumps with the maximum possible prediction confidence.
Starting Page	163
Ending Page	174
Page Count	12
File Format	PDF
ISSN	09715894
Journal	Physiology and Molecular Biology of Plants
Volume Number	22
Issue Number	1
e-ISSN	09740430
Language	English
Publisher	Springer India
Publisher Date	2016-04-07
Publisher Place	New Delhi
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	P-type ATPase Arabidopsis Attribute weighting models Supervised clustering algorithms Plant Sciences Plant Physiology Biophysics and Biological Physics Cell Biology
Content Type	Text
Resource Type	Article
Subject	Physiology Plant Science Molecular Biology

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