iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition

Abstract

Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called “iDNA-Prot|dis”, was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

“…These profile-based methods can significantly improve the protein remote homology detection [7,8], protein fold recognition and so forth. Moreover, added into the amino acid composition category are 3 new modes: they are "DR" [274], "Distance Pair" [271], and "PDT" [270]. DR is the abbreviation for "Distance-based Residue".…”

“…It is sequence-based method, in which the generated feature vector for protein sequence is based on the distance between residue pairs and has shown better performance for protein remote homology detection. "Distance Pair" method incorporates the amino acid distance pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) [108] vector, which is very useful for analysing DNA-binding proteins [15,170,189,275]. PDT is the abbreviation for "physicochemical distance transformation", which can incorporate considerable sequence-order information or important patterns of protein/peptide sequences into Pseudo components [28], which is very useful for conducting various proteome analyses [17, 23, 215-217, 224, 225, 231, 235, 276-289] and genome analysis as well [216,218,220,223,229,255,277,290].…”

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

“…7-10) above were often used in the literature to measure the prediction quality of a prediction method, they are no longer the best ones because they lack intuitiveness and are not easy to understand for most biologists, particularly the MCC (the Matthews correlation coefficient). To make it easy to read, we adopt an additional four metrics proposed by Chou (Chou 2001a, b;Chen et al 2013;Lin et al 2014;Liu et al 2014;Guo et al 2014):…”

Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC

“…More recently the notion of reduce alphabet amino acid composition method (RAAAC) was applied by different researchers and achieved remarkable results. Further it has been used in various area of computational biology, such as prediction of DNA-Binding proteins [21], prediction of defensin family and subfamilies [22] and prediction of bioluminescent proteins [23]. Similarly, Feng et al have used RAAAC and Support Vector Machine (SVM) for prediction of HSPs families and obtained maximum overall accuracy 87.82% [2].…”