Summary of Bridge-if: Learning Inverse Protein Folding with Markov Bridges, by Yiheng Zhu et al.
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
by Yiheng Zhu, Jialu Wu, Qiuyi Li, Jiahuan Yan, Mingze Yin, Wei Wu, Mingyang Li, Jieping Ye, Zheng Wang, Jian Wu
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Bridge-IF model tackles the inverse protein folding challenge, which involves designing protein sequences that fold into desired backbone structures. The existing discriminative approaches have limitations, such as error accumulation and failure to capture sequence diversity. To address these gaps, the authors introduce a generative diffusion bridge model that learns the probabilistic relationship between backbone structures and protein sequences. The model consists of a structure encoder and a Markov bridge, which refines a proposed prior sequence towards native sequences. A reparameterization perspective is also introduced, leading to a simplified loss function for more effective training. Additionally, protein language models are modulated with structural conditions to enhance generation performance while maintaining efficient training. Experimental results on established benchmarks demonstrate that Bridge-IF outperforms existing baselines in sequence recovery and design of plausible proteins with high foldability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bridge-IF is a new way to design protein sequences that can fold into specific shapes. Currently, scientists use machine learning algorithms to do this task, but they often make mistakes or don’t find many possible solutions. The Bridge-IF model is designed to learn how protein sequences relate to their 3D structures and generate more realistic sequences. It uses two main parts: a structure encoder that proposes a sequence based on the desired structure, and a Markov bridge that refines this proposal until it gets closer to the correct native sequence. This approach allows for better results in designing proteins with high foldability. |
Keywords
» Artificial intelligence » Diffusion » Encoder » Loss function » Machine learning
