Composition, Transition and Distribution (CTD) &amp;#x2014; A dynamic feature for predictions based on hierarchical structure of cellular sorting

Govindan, Geetha; Nair, Achuthsankar S.

doi:10.1109/indcon.2011.6139332

Cited by 54 publications

(36 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present study, we used six different sequence-based features to map the amino acid sequences into vectors of numeric observations. The features are compositions of amino acids (AAC; Bhasin and Raghava, 2004 ; Cai and Chou, 2006 ), compositions of di-peptides (DPC; Bhasin and Raghava, 2004 ; Meher et al, 2017 ), pseudo amino acid compositions (PseAAC; Chou, 2001 ), composition-transition-distribution (CTD; Dubchak et al, 1995 ; Cai et al, 2003 ; Govindan and Nair, 2011 ), gap-pair compositions (GPC; Yu et al, 2006 ), and auto-correlation function (ACF; Liu and Chou, 1998 ; Zhang et al, 1998 ). Succinct descriptions about computation of the above mentioned features are given in the following sub-sections.…”

Section: Methodsmentioning

confidence: 99%

“…Dubchak et al ( 1995 ) introduced the concept of CTD feature while making the prediction for different classes of protein folding. Since its introduction, the CTD feature has been successfully employed in many functional and structural related studies of proteins (Govindan and Nair, 2011 ). In CTD, C (composition) stands for the compositions of amino acids, T (transition) represents the percentage with which frequency of amino acids with specific properties is followed by amino acids with other properties and D (distribution) determines the length of the sequence within which the 1 st as well as 25, 50, and 75 percents of amino acids of certain characteristics are located.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine

Meher

Sahu

Mohanty³

et al. 2018

Front. Microbiol.

View full text Add to dashboard Cite

As inorganic nitrogen compounds are essential for basic building blocks of life (e.g., nucleotides and amino acids), the role of biological nitrogen-fixation (BNF) is indispensible. All nitrogen fixing microbes rely on the same nitrogenase enzyme for nitrogen reduction, which is in fact an enzyme complex consists of as many as 20 genes. However, the occurrence of six genes viz., nifB, nifD, nifE, nifH, nifK, and nifN has been proposed to be essential for a functional nitrogenase enzyme. Therefore, identification of these genes is important to understand the mechanism of BNF as well as to explore the possibilities for improving BNF from agricultural sustainability point of view. Further, though the computational tools are available for the annotation and phylogenetic analysis of nifH gene sequences alone, to the best of our knowledge no tool is available for the computational prediction of the above mentioned six categories of nitrogen-fixation (nif) genes or proteins. Thus, we proposed an approach, which is first of its kind for the computational identification of nif proteins encoded by the six categories of nif genes. Sequence-derived features were employed to map the input sequences into vectors of numeric observations that were subsequently fed to the support vector machine as input. Two types of classifier were constructed: (i) a binary classifier for classification of nif and non-nitrogen-fixation (non-nif) proteins, and (ii) a multi-class classifier for classification of six categories of nif proteins. Higher accuracies were observed for the combination of composition-transition-distribution (CTD) feature set and radial kernel, as compared to the other feature-kernel combinations. The overall accuracies were observed >90% in both binary and multi-class classifications. The developed approach further achieved >92% accuracy, while evaluated with blind (independent) test datasets. The developed approach also produced higher accuracy in identifying nif proteins, while evaluated using proteome-wide datasets of several species. Furthermore, we established a prediction server nifPred (http://webapp.cabgrid.res.in/nifPred) to assist the scientific community for proteome-wide identification of six categories of nif proteins. Besides, the source code of nifPred is also available at https://github.com/PrabinaMeher/nifPred. The developed web server is expected to supplement the transcriptional profiling and comparative genomics studies for the identification and functional annotation of genes related to BNF.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine

Meher

Sahu

Mohanty³

et al. 2018

Front. Microbiol.

View full text Add to dashboard Cite

show abstract

“…To extract the composition, transition, and distribution (CTD) features [ 43 , 44 ], first the 20 amino acids are categorized into 3 groups based on one out of seven physicochemical properties each time. The seven amino acid properties are hydrophobicity; normalized Van der Waals volume; polarity; polarizibility; charge; secondary structures; and solvent accessibility [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest

Ismail

Jones

Kim

et al. 2016

BioMed Research International

View full text Add to dashboard Cite

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.

show abstract

“…Recently, CTD has been adopted by more and more leading investigators for protein function and structure studies [54,55] . Composition (C) is the number of amino acids of a particular property divided by the total number of amino acids.…”

Section: Methodsmentioning

confidence: 99%

An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

Zou

Gong

2013

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundDNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence.ResultsIn this work, the focus is how to transform these informative features into uniform numeric representation appropriately and improve the prediction accuracy of our SVM-based classifier for DNA-BPs. A systematic representation of some selected features known to perform well is investigated here. Firstly, four kinds of protein properties are obtained and used to describe the protein sequence. Secondly, three different feature transformation methods (OCTD, AC and SAA) are adopted to obtain numeric feature vectors from three main levels: Global, Nonlocal and Local of protein sequence and their performances are exhaustively investigated. At last, the mRMR-IFS feature selection method and ensemble learning approach are utilized to determine the best prediction model. Besides, the optimal features selected by mRMR-IFS are illustrated based on the observed results which may provide useful insights for revealing the mechanisms of protein-DNA interactions. For five-fold cross-validation over the DNAdset and DNAaset, we obtained an overall accuracy of 0.940 and 0.811, MCC of 0.881 and 0.614 respectively.ConclusionsThe good results suggest that it can efficiently develop an entirely sequence-based protocol that transforms and integrates informative features from different scales used by SVM to predict DNA-BPs accurately. Moreover, a novel systematic framework for sequence descriptor-based protein function prediction is proposed here.

show abstract

Composition, Transition and Distribution (CTD) — A dynamic feature for predictions based on hierarchical structure of cellular sorting

Cited by 54 publications

References 32 publications

nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine

nifPred: Proteome-Wide Identification and Categorization of Nitrogen-Fixation Proteins of Diaztrophs Based on Composition-Transition-Distribution Features Using Support Vector Machine

RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest

An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

Contact Info

Product

Resources

About