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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Improved protein relative solvent accessibility prediction using deep multi-view feature learning framework.

Xue-Qiang Fan1, Jun Hu1, Ning-Xin Jia1

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.

Analytical Biochemistry
|September 3, 2021
PubMed
Summary
This summary is machine-generated.

A new deep multi-view feature learning framework (DMVFL) accurately predicts protein relative solvent accessibility. The DMVFL-RSA predictor outperforms existing methods across various prediction states and real-valued assessments.

Keywords:
Bidirectional long short-term memory recurrent neural networksBioinformaticsMulti-view feature learningProtein relative solvent accessibility predictionSequence-based feature

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Structural biology

Background:

  • Accurate prediction of protein relative solvent accessibility is crucial for understanding protein structure and function.
  • Existing methods for predicting solvent accessibility have limitations in accuracy and scope.

Purpose of the Study:

  • To develop a novel deep multi-view feature learning (DMVFL) framework for enhanced protein relative solvent accessibility prediction.
  • To introduce DMVFL-RSA, a new predictor based on the DMVFL framework, integrating multiple sequence-based features and neural network architectures.

Main Methods:

  • Developed a deep multi-view feature learning (DMVFL) framework integrating bidirectional long short-term memory recurrent neural network, squeeze-and-excitation, and fully-connected hidden layers.
  • Utilized four sequence-based features: position-specific scoring matrix, position-specific frequency matrix, predicted secondary structure, and relative solvent accessibility probability.
  • Implemented a customized multiple feedback mechanism within the DMVFL-RSA predictor to extract discriminative information.

Main Results:

  • DMVFL-RSA demonstrated superior performance compared to state-of-the-art predictors on TEST524 and CASP14set datasets for two-, three-, and four-state discrete predictions.
  • Achieved high Pearson correlation coefficients for real-valued predictions, indicating strong positive correlation with native relative solvent accessibility.
  • Analysis highlighted the efficiency of the DMVFL framework, the effectiveness of the multiple feedback mechanism, and the sensitivity of sequence-based features as key advantages.

Conclusions:

  • The DMVFL-RSA predictor offers a significant advancement in accurately predicting protein relative solvent accessibility.
  • The developed DMVFL framework and its integrated features provide a robust approach for computational biology tasks.
  • DMVFL-RSA is available as a web server and standalone package for academic use, facilitating further research.