Summary of Multi-scale Representation Learning For Protein Fitness Prediction, by Zuobai Zhang et al.
Multi-Scale Representation Learning for Protein Fitness Prediction
by Zuobai Zhang, Pascal Notin, Yining Huang, Aurélie Lozano, Vijil Chenthamarakshan, Debora Marks, Payel Das, Jian Tang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper introduces the Sequence-Structure-Surface Fitness (S3F) model, a novel multimodal representation learning framework that integrates protein features across several scales to predict fitness landscapes. The S3F model combines sequence representations from a protein language model with Geometric Vector Perceptron networks encoding protein backbone and detailed surface topology. This approach achieves state-of-the-art fitness prediction on the ProteinGym benchmark, encompassing 217 substitution deep mutational scanning assays, and provides insights into the determinants of protein function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict how well proteins work. Proteins are like tiny machines inside our bodies, and understanding how they work is important for making medicines and treating diseases. Right now, scientists have limited ways to figure out which proteins do what. This paper introduces a new method that combines different features of proteins, like their sequence (the order of the building blocks) and structure (the shape), to predict how well they work. The new approach works really well and helps us understand more about how proteins function. |
Keywords
» Artificial intelligence » Language model » Representation learning
