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Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance

Sajid Mahmud1, Zhiye Guo1, Farhan Quadir1

  • 1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.

BMC Bioinformatics
|July 19, 2022
PubMed
Summary

Predicting protein domain boundaries is challenging. DistDom, a deep learning method using 1D sequence and 2D distance map features, accurately identifies domain boundaries and classifies protein types.

Keywords:
AttentionDeep learningProtein distance mapProtein domain boundaryProtein sequence

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning in proteomics

Background:

  • Protein domain architecture is crucial for understanding protein structure and function.
  • Accurately predicting protein domain boundaries from sequence alone remains a significant computational challenge.
  • Existing methods often struggle with integrating diverse feature types for precise boundary identification.

Purpose of the Study:

  • To develop a novel deep learning method, DistDom, for accurate prediction of protein domain boundaries.
  • To leverage both 1D sequence-derived features and 2D structural information for improved domain boundary prediction.
  • To enhance the classification of single-domain versus multi-domain proteins.

Main Methods:

  • Developed DistDom, a deep learning model employing multi-head U-Nets.
  • Utilized 1D sequence features (evolutionary, physicochemical) and predicted 2D inter-residue distance maps as input.
  • Integrated 1D and 2D features through U-Nets and multi-head attention for residue-level domain boundary probability prediction.

Main Results:

  • DistDom achieved 75.9% accuracy in classifying CASP14 single-domain and multi-domain targets, outperforming state-of-the-art methods by 13.28%.
  • For multi-domain protein targets, DistDom's average F1 score for domain boundary prediction was 0.263, a 29.56% improvement over existing methods.
  • The method effectively utilizes both local and global information from sequence and structural features.

Conclusions:

  • DistDom offers a significant advancement in predicting protein domain boundaries by integrating 1D and 2D features.
  • The method demonstrates superior performance in both domain boundary prediction and protein domain classification.
  • This approach holds promise for advancing the study of protein architecture and function.