Recognition of 27-Class Protein Folds by Adding the Interaction of Segments and Motif Information

Abstract

The recognition of protein folds is an important step for the prediction of protein structure and function. After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered. Therefore, the recognition of 27-class protein folds with the interaction of segments and motif information is very important. Based on the 27-class folds dataset built by Liu et al., amino acid composition, the interactions of secondary structure segments, motif frequency, and predicted secondary structure information were extracted. Using the Random Forest algorithm and the ensemble classification strategy, 27-class protein folds and corresponding structural classification were identified by independent test. The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively. When the training set and testing set were combined, the overall accuracy by 5-fold cross validation was 81.16%. In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained.

Identification of metal ion binding sites based on amino acid sequences

“…In this study, the amino acid composition was reduced and refined by the Increment of Diversity (ID) algorithm, a classifier that has been successfully used in the identification of protein folds and subcellular localization [ 37 , 38 ] in recent years. In the state space of dimension S , for a vector X : [ n 1 , n 2 , …, n s ] the measure of diversity source was For two state spaces of dimension S , for vectors X : [ n 1 , n 2 , …, n s ] and Y : [ m 1 , m 2 , …, m s ], the measure of mixed diversity source X + Y was The increment of diversity between the source of diversity X and Y was The component information was input into the ID algorithm.…”

Identification of Ca2+-binding residues of a protein from its primary sequence

“…The autocross covariance (AC) transformation is employed to calculate the interaction of residues along the sequences (Wold et al, 1993). The autocross covariance (AC) has been successfully adopted by many researchers for the prediction of protein folds, proteins interaction predictions (Deng et al, 2009;Feng and Hu, 2014). As we know, this is the first time to bring AC into the identification of ligand binding residues.…”

ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

“…In the field of protein fold classification, many researchers used ensemble learning methods [ 11 , 18 , 22 , 23 , 34 – 36 , 38 , 46 , 51 , 54 , 79 , 82 – 89 ]. The specific process of those ensemble strategies is as follows.…”

“…DD-dataset was proposed by Ding and Dubchak in 2001 and modified by Shen and Chou in 2006 [ 12 ]. Since then, DD-dataset has been used in many protein fold classification studies [ 11 , 18 , 20 – 24 , 26 , 32 – 36 , 38 , 40 , 57 , 59 ]. There are 311 protein sequences in the training set and 386 protein sequences in the testing set with no two proteins having more than 35% of sequence identity.…”