article · International Journal of Molecular Sciences · 2025

SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction

Guowei Zhou, Yanpeng Zhao, Song He, Xiaochen Bo

Cited by 2Open access

Abstract

Proteins are the basic building blocks of life and perform fundamental functions in biology. Predicting protein properties based on amino acid sequences and 3D structures has become a key approach to accelerating drug development. In this study, we propose a novel sequence- and structure-based framework, SST-ResNet, which consists of the multimodal language model ProSST and a multi-scale information integration module. This framework is designed to deeply explore the latent relationships between protein sequences and structures, thereby achieving superior synergistic prediction performance. Our method outperforms previous joint prediction models on Enzyme Commission (EC) numbers and Gene Ontology (GO) tasks. Furthermore, we demonstrate the necessity of multi-scale information integration for these two types of data and illustrate its exceptional performance on key tasks. We anticipate that this framework can be extended to a broader range of protein property prediction problems, ultimately facilitating drug development.

Cite this paper

APA 7

Zhou, G., Zhao, Y., He, S., & Bo, X. (2025). SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction. International Journal of Molecular Sciences, 26(6), 2783. https://doi.org/10.3390/ijms26062783

MLA 9

Zhou, Guowei, et al. “SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction.” International Journal of Molecular Sciences, vol. 26, no. 6, 2025, p. 2783, https://doi.org/10.3390/ijms26062783.

Chicago

Zhou, Guowei, Yanpeng Zhao, Song He, and Xiaochen Bo. 2025. “SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction.” International Journal of Molecular Sciences 26 (6): 2783. https://doi.org/10.3390/ijms26062783.

Harvard

Zhou, G. et al. (2025) ‘SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction’, International Journal of Molecular Sciences, 26(6), p. 2783. Available at: https://doi.org/10.3390/ijms26062783.

BibTeX

@article{zhou2025resnet,
  title = {{SST-ResNet: A Sequence and Structure Information Integration Model for Protein Property Prediction}},
  author = {Zhou, Guowei and Zhao, Yanpeng and He, Song and Bo, Xiaochen},
  journal = {International Journal of Molecular Sciences},
  volume = {26},
  number = {6},
  pages = {2783},
  year = {2025},
  doi = {10.3390/ijms26062783}
}

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