High fitness paths can connect proteins with low sequence overlap
Pranav Kantroo1,2, Günter P Wagner3,4,5, Benjamin B Machta6,2
1Computational Biology and Bioinformatics Program, Yale University, New Haven, CT-06520, USA.
Biorxiv : the Preprint Server for Biology
|November 28, 2024
Summary
Researchers developed an AI algorithm to map evolutionary paths between proteins. This tool helps understand protein evolution and homology by generating viable intermediate sequences using the ESM2 protein language model.
Area of Science:
- Computational Biology
- Protein Engineering
- Evolutionary Bioinformatics
Background:
- Protein structure and function are dictated by amino acid sequences, shaped by evolutionary forces.
- Neutral networks connect local protein sequence spaces, but large-scale morphospace connectivity is unclear.
Purpose of the Study:
- To develop an algorithm for generating viable evolutionary paths between distantly related proteins.
- To explore the large-scale connectedness of protein morphospace using AI-driven methods.
Main Methods:
- Utilized artificial intelligence tools for protein structure prediction and functional plausibility assessment.
- Developed a novel algorithm employing the ESM2 protein language model to evaluate sequence fitness.
- Generated intermediate protein sequences via single residue substitutions, insertions, and deletions.
Main Results:
- Successfully generated viable paths between diverse protein pairs, including those with different structural folds.
- Documented qualitative variations in paths connecting progressively divergent protein sequences.
- Demonstrated the potential of sequence interpolation as a proxy for homology detection.
Conclusions:
- The developed algorithm provides insights into protein evolutionary trajectories and morphospace.
- Sequence interpolation ease can serve as a quantitative measure for assessing protein homology.
- This approach advances our understanding of protein evolution and sequence-structure-function relationships.
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