Summary of Mape-ppi: Towards Effective and Efficient Protein-protein Interaction Prediction Via Microenvironment-aware Protein Embedding, by Lirong Wu et al.
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
by Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V Chawla, Stan Z. Li
First submitted to arxiv on: 22 Feb 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 presents a novel approach to predict Protein-Protein Interactions (PPIs) using Microenvironment-Aware Protein Embedding for PPI prediction (MPAE-PPI). Unlike existing methods that rely heavily on protein sequence, MPAE-PPI considers both sequence and structural contexts to define the microenvironment of an amino acid residue. The authors propose a novel pre-training strategy, Masked Codebook Modeling (MCM), to capture dependencies between microenvironments. They also develop a large-scale PPI prediction model that can scale to millions of interactions with superior trade-offs in effectiveness and computational efficiency compared to state-of-the-art competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers predict how proteins interact with each other, which is important for understanding many biological processes. Current methods rely too much on protein sequence, but this paper shows that structure is also crucial. They developed a new way to combine both sequence and structure information to make predictions. This approach can process millions of interactions quickly and accurately, making it useful for scientists studying proteins. |
Keywords
* Artificial intelligence * Embedding
