Language models reveal a complex sequence basis for adaptive convergent evolution of protein functions
Zhenqiu Cao1,2, Hongjiu Zhang3, Zhengting Zou1,2
1State Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.
Summary
Protein language models (PLMs) reveal high-order feature convergence in evolution. Our ACEP pipeline detects adaptive convergence beyond site-level similarity, advancing sequence-function understanding.
Area of Science:
- Evolutionary Biology
- Computational Biology
- Biochemistry
Background:
- Convergent evolution describes the independent emergence of similar traits in different species, often driven by environmental adaptation.
- Investigating the genetic basis of functional convergence is key to understanding protein sequence-function relationships.
- Conventional methods focus on site-level amino acid convergence, potentially missing high-order feature convergence.
Purpose of the Study:
- To develop computational methods for detecting high-order protein feature convergence.
- To explore the utility of protein language models (PLMs) in identifying functional convergence.
- To introduce a novel pipeline, ACEP, for detecting adaptive convergence.
Main Methods:
- Derived numerical embeddings from protein sequences using pretrained PLMs.
- Developed the Adaptive Convergence by Embedding of Protein (ACEP) pipeline.
- Applied ACEP to known and candidate genes, including echolocation and crassulacean acid metabolism.
Main Results:
- PLM embeddings captured high-order protein feature convergence, even without site-level similarity.
- ACEP successfully identified known and novel cases of adaptive convergence.
- Genome-wide application demonstrated ACEP's effectiveness in enriching candidate genes.
Conclusions:
- PLM embeddings are powerful indicators of adaptive convergence at the high-order feature level.
- ACEP provides a novel framework for discovering adaptive convergence beyond sequence identity.
- Deep learning tools significantly enhance the investigation of molecular sequence-function mapping.
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