Conclusion
In this paper, we introduce a simple Siamese contrastive self-supervised learning framework for protein surface representation learning. The encoder in this framework can be adapted to various point cloud feature extraction backbone networks. Experiments show pre-trained networks consistently demonstrate performance improvements in two downstream tasks. In future work, we aim to explore more efficient protein surface feature extraction networks and delve into additional downstream tasks that better capture protein surface characteristics.
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References
Isert C, Atz K, Schneider G. Structure-based drug design with geometric deep learning. Current Opinion in Structural Biology, 2023, 79: 102548
Gainza P, Sverrisson F, Monti F, Rodolà E, Boscaini D, Bronstein M M, Correia B E. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods, 2020, 17(2): 184–192
Sverrisson F, Feydy J, Correia B E, Bronstein M M. Fast end-to-end learning on protein surfaces. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 15267–15276
Chen X, He K. Exploring simple Siamese representation learning. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 15745–15753
Berman H M, Westbrook J, Feng Z, Gilliland G, Bhat T N, Weissig H, Shindyalov I N, Bourne P E. The protein data bank. Nucleic Acids Research, 2000, 28(1): 235–242
Yuan M, Shen A, Fu K, Guan J, Ma Y, Qiao Q, Wang M. ProteinMAE: masked autoencoder for protein surface self-supervised learning. Bioinformatics, 2023, 39(12): btad724
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 6000–6010
Phan A V, Le Nguyen M, Nguyen Y L H, Bui L T. DGCNN: A convolutional neural network over large-scale labeled graphs. Neural Networks, 2018, 108: 533–543
Qi C R, Yi L, Su H, Guibas L J. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 5105–5114
Acknowledgments
This work was supported by the Science and Technology Innovation Plan of Shanghai Science and Technology Commission (Grant No. 23S41900400) and the National Natural Science Foundation of China (Grant No. 62076070).
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Shen, A., Yuan, M., Ma, Y. et al. SS-Pro: a simplified Siamese contrastive learning approach for protein surface representation. Front. Comput. Sci. 18, 185910 (2024). https://doi.org/10.1007/s11704-024-3806-9
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DOI: https://doi.org/10.1007/s11704-024-3806-9