PFP-RFSM: Protein fold prediction by using random forests and sequence motifs

Abstract

Protein tertiary structure is indispensible in revealing the biological functions of proteins. De novo perdition of protein tertiary structure is dependent on protein fold recognition. This study proposes a novel method for prediction of protein fold types which takes primary sequence as input. The proposed method, PFP-RFSM, employs a random forest classifier and a comprehensive feature representation, including both sequence and predicted structure descriptors. Particularly, we propose a method for generation of features based on sequence motifs and those features are firstly employed in protein fold prediction. PFP-RFSM and ten representative protein fold predictors are validated in a benchmark dataset consisting of 27 fold types. Experiments demonstrate that PFP-RFSM outperforms all existing protein fold predictors and improves the success rates by 2%-14%. The results suggest sequence motifs are effective in classification and analysis of protein sequences.

Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

“…Moreover, the PFP-RFSM method is the first to use the RF classifier as its prediction engine. As reported in [25], RF classifier is superior over the other commonly used classifiers, such as SVM, NB, and LR. In terms of overall performance, RF outperforms most of the existing methods, especially some of the ensemble-classifier methods (e.g., the well-known PFP-FunDSeqE method).…”

“…For this reason, Damoulas et al [21] propose a single multi-class kernel machine that informatively combines available feature groups. Apart from the SVM classifier, other single classifiers, such as RF (Random Forest) [25] and Hidden Markov Model [26], are used to construct a prediction engine in machine learning-based methods.…”

“…Chen et al [25] recently propose an RF-based protein fold recognition method called PFP-RFSM. The framework of PFP-RFSM involves a comprehensive feature representation algorithm that can capture distinctive sequential and structural information from primary sequences and predicted structures, respectively.…”

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

“…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. The protein sequences in DD-dataset were selected from 27 SCOP [ 35 ] folds comprehensively, which belong to different structural classes containing α , β , α / β , and α + β .…”

“…As effective features to describe a protein, the amino acid composition and physiochemical properties have reached good predict result, respectively [ 13 , 34 , 35 ]. Ding and Dubchak [ 13 ] tried to integrate the features for the first time and achieved a good result.…”