Dataset Information

ConCysFind: a pipeline tool to predict conserved amino acids of protein sequences across the plant kingdom.

ABSTRACT: BACKGROUND:Post-translational modifications (PTM) of amino acid (AA) side chains in peptides control protein structure and functionality. PTMs depend on the specific AA characteristics. The reactivity of cysteine thiol-based PTMs are unique among all proteinaceous AA. This pipeline aims to ease the identification of conserved AA of polypeptides or protein families based on the phylogenetic occurrence in the plant kingdom. The tool is customizable to include any species. The degree of AA conservation is taken as indicator for structural and functional significance, especially for PTM-based regulation. Further, this pipeline tool gives insight into the evolution of these potentially regulatory important peptides. RESULTS:The web-based or stand-alone pipeline tool Conserved Cysteine Finder (ConCysFind) was developed to identify conserved AA such as cysteine, tryptophan, serine, threonine, tyrosin and methionine. ConCysFind evaluates multiple alignments considering the proteome of 21 plant species. This exemplar study focused on Cys as evolutionarily conserved target for multiple redox PTM. Phylogenetic trees and tables with the compressed results of the scoring algorithm are generated for each Cys in the query polypeptide. Analysis of 33 translation elongation and release factors alongside of known redox proteins from Arabidopsis thaliana for conserved Cys residues confirmed the suitability of the tool for identifying conserved and functional PTM sites. Exemplarily, the redox sensitivity of cysteines in the eukaryotic release factor 1-1 (eRF1-1) was experimentally validated. CONCLUSION:ConCysFind is a valuable tool for prediction of new potential protein PTM targets in a broad spectrum of species, based on conserved AA throughout the plant kingdom. The identified targets were successfully verified through protein biochemical assays. The pipeline is universally applicable to other phylogenetic branches by customization of the database.

SUBMITTER: Moore M

PROVIDER: S-EPMC7603750 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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ConCysFind: a pipeline tool to predict conserved amino acids of protein sequences across the plant kingdom.

Moore Marten M Wesemann Corinna C Gossmann Nikolaj N Sahm Arne A Krüger Jan J Sczyrba Alexander A Dietz Karl-Josef KJ

BMC bioinformatics 20201031 1

<h4>Background</h4>Post-translational modifications (PTM) of amino acid (AA) side chains in peptides control protein structure and functionality. PTMs depend on the specific AA characteristics. The reactivity of cysteine thiol-based PTMs are unique among all proteinaceous AA. This pipeline aims to ease the identification of conserved AA of polypeptides or protein families based on the phylogenetic occurrence in the plant kingdom. The tool is customizable to include any species. The degree of AA ...[more]

PMID: 33129266

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Project description:BACKGROUND: Many learning approaches to predicting RNA-binding residues in a protein sequence construct a non-redundant training dataset based on the sequence similarity. The sequence similarity-based method either takes a whole sequence or discards it for a training dataset. However, similar sequences or even identical sequences can have different interaction sites depending on their interaction partners, and this information is lost when the sequences are removed. Furthermore, a training dataset constructed by the sequence similarity-based method may contain redundant data when the remaining sequence contains similar subsequences within the sequence. In addition to the problem with the training dataset, most approaches do not consider the interacting partner (i.e., RNA) of a protein when they predict RNA-binding amino acids. Thus, they always predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNA molecules. RESULTS: We developed a feature vector-based method that removes data redundancy for a non-redundant training dataset. The feature vector-based method constructed a larger training dataset than the standard sequence similarity-based method, yet the dataset contained no redundant data. We identified effective features of protein and RNA (the interaction propensity of amino acid triplets, global features of the protein sequence, and RNA feature) for predicting RNA-binding residues. Using the method and features, we built a support vector machine (SVM) model that predicted RNA-binding residues in a protein sequence. Our SVM model showed an accuracy of 84.2%, an F-measure of 76.1%, and a correlation coefficient of 0.41 with 5-fold cross validation on a non-redundant dataset from 3,149 protein-RNA interacting pairs. In an independent test dataset that does not include the 3,149 pairs and were not used in training the SVM model, it achieved an accuracy of 90.3%, an F-measure of 72.8%, and a correlation coefficient of 0.24. Comparison with other methods on the same datasets demonstrated that our model was better than the others. CONCLUSIONS: The feature vector-based redundancy reduction method is powerful for constructing a non-redundant training dataset for a learning model since it generates a larger dataset with non-redundant data than the standard sequence similarity-based method. Including the features of both RNA and protein sequences in a feature vector results in better performance than using the protein features only when predicting the RNA-binding residues in a protein sequence.

Dataset Information

ConCysFind: a pipeline tool to predict conserved amino acids of protein sequences across the plant kingdom.

Publications

ConCysFind: a pipeline tool to predict conserved amino acids of protein sequences across the plant kingdom.

Similar Datasets

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