Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method

Yuming Ma,Yihui Liu,Jinyong Cheng

doi:10.1038/s41598-018-28084-8

Yuming Ma, Yihui Liu + Show 1 more

Open Access

https://doi.org/10.1038/s41598-018-28084-8

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Abstract

Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However there is still room for improvement toward the theoretical limit. In this paper, we present a novel method for protein secondary structure prediction based on a data partition and semi-random subspace method (PSRSM). Data partitioning is an important strategy for our method. First, the protein training dataset was partitioned into several subsets based on the length of the protein sequence. Then we trained base classifiers on the subspace data generated by the semi-random subspace method, and combined base classifiers by majority vote rule into ensemble classifiers on each subset. Multiple classifiers were trained on different subsets. These different classifiers were used to predict the secondary structures of different proteins according to the protein sequence length. Experiments are performed on 25PDB, CB513, CASP10, CASP11, CASP12, and T100 datasets, and the good performance of 86.38%, 84.53%, 85.51%, 85.89%, 85.55%, and 85.09% is achieved respectively. Experimental results showed that our method outperforms other state-of-the-art methods.

Highlights

Protein secondary structure refers to the local conformation proteins’ polypeptide backbone
We presented a data partition and semi-random subspace method (PSRSM) for protein secondary structure prediction
In this paper we proposed a novel method, PSRSM, to predict protein secondary structure

Summary

Introduction

Protein secondary structure refers to the local conformation proteins’ polypeptide backbone. Sander developed a secondary structure assignment method Dictionary of Secondary Structure of Proteins (DSSP)[3], which automatically assigns secondary structure into eight states (H, E, B, T, S, L, G, and I) according to hydrogen-bonding patterns. These eight states are often further simplified into three states of helix, sheet and coil. The secondary structure prediction problem is formulated as follows: given a protein sequence with amino acids, predict whether each amino acid is in the α-helix (H), β-strand (E), or coil region (C). Protein secondary structure prediction is usually evaluated by Q3 accuracy, which measures the percentage of residues for three-state secondary structures to determine whether they have been predicted correctly

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