#DNA ENZYMEX ONLINE SERVER PATCH#

As shown in Figure 1, few residues like Lys, Arg and Glu are abundant in DNA-binding domains where as other residues like Gly are less frequent in DNA-binding domains. In order to understand the high success rate, we compared the amino acid compositions of DNA-binding and non-binding domains in DNAset (Figure 1). First, composition based SVM model has been developed for predicting DNA-binding domains and achieved the accuracy of 79.80% with MCC, 0.58 (Table 1). SVM models have been developed on DNAset or main dataset, which has DNA-binding and non-binding chains obtained from PDB. SVM models Prediction of DNA-binding domains/chains All models developed in this study were evaluated using five-fold cross validation technique. We also examined the performance of similarity search (BLAST and PSI-BLAST) and motif-finding (MEME/MAST) approaches. Besides composition, we also developed SVM models using PSSM profiles obtained from PSI-BLAST. First, we analyzed the amino acid composition of DNA-binding proteins and based on the observation, SVM models have been developed using amino acid, dipeptide and four-part amino acid compositions of proteins. In this work, a systematic attempt has been made to predict DNA-BPs from their amino acid sequences using various features of proteins, like amino acid composition. They used a feature vector of dimension 132, which includes amino acid composition and composition of physico-chemical properties. (2006) has developed a SVM based method for prediction of rRNA, RNA and DNA binding proteins. Cai and Lin 2003, used pseudo-amino acid composition as input for support vector machine (SVM) to discriminate RNA, rRNA and DNA binding proteins from non-binding proteins (NBPs). Unfortunately, these methods can't be used in high throughput annotation, as they require the structure of a protein for prediction.

#DNA ENZYMEX ONLINE SERVER PATCH#

Structure based prediction methods discriminate DNA-binding and non-binding proteins with high accuracy on the basis of positively charged electrostatic patches, DNA-binding structural motifs, protein sequence composition, solvent accessibility and secondary structure, net charge, dipole and quadrapole moments, and size of largest positive surface patch and amino acid composition. Broadly, these methods can be divided into two categories i) prediction from protein structure and ii) prediction from amino acid sequence. In past, several methods have been developed for predicting DNA-BPs. Hence, identification of DNA-BPs can play a vital role in proteome annotation and understanding an important class of proteins. Due to different functions DNA-BPs are diverse group of proteins both in terms of amino acid sequences and three-dimensional structures. In the form of restriction enzymes, DNA-BPs play a crucial role in prokaryotic host defence. Hence proteins that target specific DNA sequences can be a potential therapeutics for genetic diseases and cancers. These proteins play important roles in DNA packaging, replication, transcription regulation and other activities associated with DNA. It has been reported that approximately 2–3% of prokaryotic and 6–7% of eukaryotic proteins can bind to DNA. ĭNA-binding proteins (DNA-BPs) are very important constituent of both eukaryotic and prokaryotic proteomes. A web-server DNAbinder has been developed for identifying DNA-binding proteins and domains from query amino acid sequences. This is the first study in which evolutionary information in form of PSSM profiles has been used successfully for predicting DNA-binding proteins. ConclusionĪ highly accurate method has been developed for predicting DNA-binding proteins using SVM and PSSM profiles. The SVM models developed in this study perform better than existing methods on a blind dataset. In addition, SVM models have been developed on DNAset, which consists of 146 DNA-binding and 250 non-binding chains/domains, and achieved the maximum accuracy of 79.80% and 86.62% using amino acid composition and PSSM profiles. The performance of SVM model improved from 72.42% to 74.22%, when evolutionary information in form of PSSM profiles was used as input instead of amino acid composition. SVM models have been developed on DNAaset, which consists of 1153 DNA-binding and equal number of non DNA-binding proteins, and achieved the maximum accuracy of 72.42% and 71.59% using amino acid and dipeptide compositions, respectively. All models were trained and tested on multiple datasets of non-redundant proteins. In this paper, we developed various SVM modules for predicting DNA-binding domains and proteins. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation, as these proteins play a crucial role in gene-regulation.

Dna enzymex online server