Summary of Dplm-2: a Multimodal Diffusion Protein Language Model, by Xinyou Wang et al.
DPLM-2: A Multimodal Diffusion Protein Language Model
by Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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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 This paper introduces DPLM-2, a multimodal protein foundation model that can generate both amino acid sequences and their corresponding 3D structures simultaneously. The model extends the discrete diffusion protein language model (DPLM) to accommodate both modalities by converting 3D coordinates to discrete tokens using a lookup-free quantization-based tokenizer. Training on experimental and synthetic structures, DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. The paper also proposes an efficient warm-up strategy that leverages large-scale evolutionary data and structural inductive biases from pre-trained sequence-based protein language models. Empirical evaluation shows that DPLM-2 can generate highly compatible sequences and structures, eliminating the need for a two-stage generation approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to model proteins, which are important molecules that do different jobs in our bodies. To create these models, scientists use computers to learn from examples of protein sequences (the order of amino acids) and structures (the shape of the molecule). But current methods can’t handle both at the same time, so they have to do it one step at a time. This new method, called DPLM-2, is different because it can learn about both sequence and structure simultaneously. It’s like having a superpower that lets you generate proteins with the right sequence and shape. The scientists tested this new method and found that it works well for predicting protein structures and generating new ones. |
Keywords
» Artificial intelligence » Diffusion » Language model » Quantization » Tokenizer
