GRASP

GRASP reconstructs ancestral protein sequences and models insertion and deletion (indel) events using maximum likelihood and partial order graphs to support molecular evolution analysis and protein engineering.

Key Features:

• Maximum likelihood ancestral inference: Implements maximum likelihood approaches to infer ancestral sequences.
• Scalability: Applies to large sequence datasets, demonstrated on datasets exceeding 10,000 sequences.
• Partial order graphs (POGs) for indels: Represents and infers insertion and deletion (indel) events across ancestral sequences using POGs.
• Identification of evolutionary building blocks: Detects evolutionary building blocks from modeled indels to inform protein engineering and analysis of sequence variation.
• Experimental validation across enzyme families: Predicted ancestral sequences that exhibited enzymatic activity in glucose-methanol-choline (GMC) oxidoreductases, cytochromes P450, and dihydroxy/sugar acid dehydratases (DHAD).

Scientific Applications:

• Molecular evolution studies: Reconstruction of ancestral sequences and indel histories to study sequence variation and functional evolution of enzymes.
• Protein engineering: Use of reconstructed ancestors and identified evolutionary building blocks to guide engineering of biologically active proteins.
• Reconstruction and functional characterization of ancestral enzymes: Demonstrated application to GMC oxidoreductases, cytochromes P450, and DHAD showing enzymatic activity of predicted ancestors.

Methodology:

Implements maximum likelihood approaches for ancestral sequence inference and uses partial order graphs (POGs) to represent and infer insertion and deletion (indel) events, applied to datasets exceeding 10,000 sequences.