Related Experiment Video
Updated: May 9, 2025
09:51
Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
Published on: July 16, 2017
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MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation.
Beibei Wang1, Boyue Cui1, Shiqu Chen1
1School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guang Dong 518055, China.
Bioinformatics (Oxford, England)
|May 6, 2025
Summary
Integrating protein structures and network propagation, the MSNGO model enhances multi-species protein function prediction. This approach improves cross-species label propagation for species with limited protein annotations.
Area of Science:
- Bioinformatics
- Computational Biology
- Structural Biology
Background:
- Protein function prediction accuracy has advanced using AlphaFold2-predicted structures, outperforming sequence-only methods.
- Multi-species protein function prediction faces challenges in data integration and knowledge transfer between diverse species.
- Effective cross-species label propagation for sparsely annotated species remains a critical challenge.
Purpose of the Study:
- To propose a novel model, MSNGO (Multi-species protein Structures and Network to predict GO terms), for improved multi-species protein function prediction.
- To integrate structural features and network propagation to address limitations in current multi-species approaches.
- To enhance cross-species label propagation for species with sparse protein annotations.
Main Methods:
- Utilizing graph representation learning to extract amino acid features from protein structure contact maps.
- Employing a graph convolution pooling module to derive protein-level structural features.
- Incorporating sequence features from ESM-2 and applying network propagation within a heterogeneous network.
Main Results:
- The MSNGO model integrates structural and sequence features for enhanced protein function prediction.
- Network propagation effectively aggregates information and updates node representations in a heterogeneous network.
- MSNGO demonstrates superior performance compared to existing multi-species methods relying solely on sequence features and protein-protein networks.
Conclusions:
- Integrating structural features significantly improves the accuracy of multi-species protein function prediction.
- MSNGO offers an effective solution for cross-species label propagation, particularly for species with limited annotations.
- The developed model advances the field of multi-species protein function prediction by leveraging structural and network information.
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