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

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, P4ATPases (phospholipid-transporting ATPase), P3AATPases (plasma membrane H(+) pumps), P2A and P2BATPases (Ca(2+) pumps) and P1B 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.

Meta-analysis of transcriptomic responses to biotic and abiotic stress in tomato

“…The bioinformatic approach is an emerging and rapidly developing field which allows researchers to analyze biological data ( Khatri & Drăghici, 2005 ; Chang et al, 2013 ; Zinati, Alemzadeh & KayvanJoo, 2016 ). Recent advances in genome-wide transcriptome analysis methods, such as RNA sequencing and microarrays, enable researchers to simultaneously study the expression of thousands of genes and their co-expression partners under various stresses ( Khatri & Drăghici, 2005 ; Sharma et al, 2013 ; Rest et al, 2016 ).…”

Peer Review #1 of "Meta-analysis of transcriptomic responses to biotic and abiotic stress in tomato (v0.1)"

“…The bioinformatic approach is an emerging and rapidly developing field which allows researchers to analyze biological data (Khatri & Drăghici, 2005;Chang et al, 2013;Zinati, Alemzadeh & KayvanJoo 2016). Recent advances in genome-wide transcriptome analysis methods, such as RNA sequencing and microarrays, enable researchers simultaneously to study the expression of thousands of genes and their coexpression partners under various stresses (Khatri & Drăghici, 2005;Sharma et al, 2013;Rest et al, 2016).…”

New selection strategies for determining the traits contributing to increased grain yield in wheat (Triticum aestivum L.) under aluminum stress

“…A key advantage of machinelearning for plant breed and biology studies is the chance to find patterns in datasets and control the discovery through looking at a mixture of factors at the same time rather than analyzing the factors one by one (Sing et al 2016). This tool has drawn a growing attention and it has been effectively used to discover the key significant structural attributes and predict ATPase function and the content of alpha-linolenic acid (Zinati et al 2014(Zinati et al , 2016.…”