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DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.

Yuexu Jiang1, Duolin Wang, Dong Xu

  • 1Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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Summary

DeepDom, a novel deep learning model, accurately predicts protein domain boundaries using only sequence data. This ab-initio method offers faster predictions and broader applicability than existing approaches.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Protein domain boundary prediction is crucial for understanding protein function and structure.
  • Current methods often lack accuracy, struggle with multi-domain proteins, or cannot handle discontinuous domains.

Purpose of the Study:

  • To develop an accurate and versatile ab-initio protein domain predictor.
  • To overcome limitations of existing domain prediction techniques.

Main Methods:

  • Developed DeepDom, a deep learning model utilizing a stacked bidirectional Long Short-Term Memory (LSTM) network.
  • Trained the model on extensive protein sequence data without feature engineering, such as sequence profiles.

Main Results:

  • DeepDom demonstrated high accuracy in 10-fold cross-validation and on CASP 8 and CASP 9 targets.
  • Outperformed most existing ab-initio methods and matched template-based methods in specific cases.
  • Achieved faster prediction times due to the absence of feature engineering.

Conclusions:

  • DeepDom provides an efficient and broadly applicable solution for protein domain boundary prediction.
  • The ab-initio approach enhances usability across diverse protein types, including those with discontinuous domains.