Amino Acid Features of PIB-ATPase Heavy Metal Transporters Enabling small Numbers of Organisms to Cope with Heavy Metal Pollution

Abstract

Phytoremediation refers to the use of plants for extraction and detoxification of pollutants, providing a new and powerful weapon against a polluted environment. In some plants, such as Thlaspi spp, heavy metal ATPases are involved in overall metal ion homeostasis and hyperaccumulation. P1B-ATPases pump a wide range of cations, especially heavy metals, across membranes against their electrochemical gradients. Determination of the protein characteristics of P1B-ATPases in hyperaccumulator plants provides a new opportuntity for engineering of phytoremediating plants. In this study, using diverse weighting and modeling approaches, 2644 protein characteristics of primary, secondary, and tertiary structures of P1B-ATPases in hyperaccumulator and nonhyperaccumulator plants were extracted and compared to identify differences between proteins in hyperaccumulator and nonhyperaccumulator pumps. Although the protein characteristics were variable in their weighting, tree and rule induction models; glycine count, frequency of glutamine-valine, and valine-phenylalanine count were the most important attributes highlighted by 10, five, and four models, respectively. In addition, a precise model was built to discriminate P1B-ATPases in different organisms based on their structural protein features. Moreover, reliable models for prediction of the hyperaccumulating activity of unknown P1B-ATPase pumps were developed. Uncovering important structural features of hyperaccumulator pumps in this study has provided the knowledge required for future modification and engineering of these pumps by techniques such as site-directed mutagenesis.

DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins

“…In these approaches, simple sequence properties, such as amino acid and di-peptide content and/or frequency, are used to predict potential targets [16][17][18]. Computationally calculated structural amino acid and/or protein features are useful because they can be easily calculated based on sequence and frequently predict protein function accurately [19][20][21][22][23][24][25][26].…”

Plant Responses to Heavy Metal Toxicity

“…P 1B -type ATPases belong to P-type ATPase superfamily and use energy from ATP hydrolysis to translocate diverse metal cations across biological membranes (Axelsen and Palmgren 2001). P 1B -type ATPases share common structural characteristics, such as eight predicted transmembrane domains, a CPx (Cys-Pro-Cys/His/Ser) intramembrane motif that is hypothetically involved in metal translocation (Ashrafi et al 2011), and a putative N-or C-terminal metal binding domain (Colangelo and Guerinot 2006). P 1B -ATPases pump metal ions out of the cytoplasm against their electrochemical gradient, into either the apoplast or into the vacuole.…”

Comparative study of ammonium transporters in different organisms by study of a large number of structural protein features via data mining algorithms

“…To identify the most important protein features contributing to the type of organism (which ammonium transporters isolated from), various screening models (Anomaly detection model and various attribute weighting algorithms), clustering models (K-Means, TwoStep cluster), Tree Induction models (C5.0, C&RT, CHAID, Exhaustive CHAID, QUEST, C5.0, Decision Tree and ID3 models), Item Set Mining (FPGrowth) and Rule Induction model (10 fold cross-validation through stratified sampling) were used, as described previously (Ebrahimi et al, 2009;Bijanzadeh et al, 2010;Ashrafi et al, 2011;Ebrahimie et al, 2011). Whenever requested by model, data were discretized by frequency; i.e.…”