Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition*

By Manoj Bhasin, Gajendra P. Raghava. Journal of Biological Chemistry, 2004

549 citations recorded by the source corpus.

Nuclear receptors are key transcription factors that regulate crucial gene networks responsible for cell growth, differentiation, and homeostasis. Nuclear receptors form a superfamily of phylogenetically related proteins and control functions associated with major diseases (e.g. diabetes, osteoporosis, and cancer). In this study, a novel method has been developed for classifying the subfamilies of nuclear receptors. The classification was achieved on the basis of amino acid and dipeptide composition from a sequence of receptors using support vector machines. The training and testing was done on a non-redundant data set of 282 proteins obtained from the NucleaRDB data base (1). The performance of all classifiers was evaluated using a 5-fold cross validation test. In the 5-fold cross-validation, the data set was randomly partitioned into five equal sets and evaluated five times on each distinct set while keeping the remaining four sets for training. It was found that different subfamilies of nuclear receptors were quite closely correlated in terms of amino acid composition as well as dipeptide composition. The overall accuracy of amino acid composition-based and dipeptide compositionbased classifiers were 82.6 and 97.5%, respectively. Therefore, our results prove that different subfamilies of nuclear receptors are predictable with considerable accuracy using amino acid or dipeptide composition. Furthermore, based on above approach, an online web service, NRpred, was developed, which is available at www.imtech.res.in/raghava/nrpred.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/086554be7421d230b478d739c07b279e3efe999f

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Citations in the corpus

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence (2006, methodology): “Dipeptide composition is defined as: frðr‚s Þ¼ Nrs=ðN � 1Þ, where r,s ¼ 1, 2, 3, ... , 20, and Nrs is the number of dipeptides of amino acid type r and s ( 16 ).”
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, methodology): “The Amino Acid Composition (AAC) encoding (Bhasin and Raghava, 2004) calculates the frequency of each amino acid type in a protein or peptide sequence.”
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2017, background): “DC groups (400 features) The Dipeptide Composition descriptor (Xiao et al., 2015; Bhasin and Raghava, 2004) captures information about the fraction and local order of amino acids.”
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches (2019, background): “Z-Scale ZSCALE (44,45) TPC Tri-Peptide Composition (37) GTPC Grouped Tri-Peptide Composition (37) CKSAAP Composition of k-spaced Amino Acid Pairs (46–49)”