Journal of Molecular Graphics and Modelling
Journal of Molecular Graphics and Modelling
3 years ago

Predict protein structural class by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition

Predict protein structural class by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition
Structural class is an important attribute of protein, which plays an important role in both the experiment and theoretical research of protein science. Prediction of protein structural classes has been a challenging task, specifically for low-similarity datasets. In this paper, we develop a feature extraction method PSSS-SOMA-PSSM by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition (PseAAC) based on predicted secondary structure sequence (PSSS) and position-specific scoring matrix (PSSM). We construct a 170-dimensional (170D) feature vector for each protein sequence sample, which contains 10D PSSS features that reflect content, alternating word frequency and novel position information, and contains 160D PSSM features that are calculated by second-order moving average (SOMA) algorithm. The SVM classifier with RBF kernel function and the jackknife test are used to predict and evaluate on 1189, 25PDB and 640 datasets with sequence similarity lower than 40%, 25%, and 25%, respectively. Comparison of our results with other methods shows that the proposed method provides the state-of-the-art performance and a cost-effective alternative to structural classes prediction for low-similarity datasets.

Graphical abstract

image

Highlights

Publisher URL: www.sciencedirect.com/science

DOI: S1093326317306770

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.