The Computer-Assisted Sequence Annotation (CASA) workflow for enzyme discovery.

Area of Science:

• Biochemistry
• Bioinformatics
• Genomics

Background:

• The increasing volume of nucleic acid sequencing data presents a challenge in enzyme discovery.
• Current annotation methods often rely on sequence similarity alone, which is insufficient for selecting promising enzyme candidates.
• Detailed sequence and structural information is crucial for effective enzyme characterization.

Purpose of the Study:

• To introduce a novel computational workflow for enhanced protein annotation.
• To automate key aspects of novel protein characterization for enzyme discovery.
• To generate human-interpretable annotations that go beyond basic sequence similarity.

Main Methods:

• Development of Computer-Assisted Sequence Annotation (CASA), a Python-based workflow.
• Automating the generation of highly informative and richly annotated sequence alignments.
• Demonstration using an enzyme from the *Drosera capensis* genome.

Main Results:

• CASA produces detailed annotations comparing novel sequences to known references.
• Annotations include predicted function, active site residues, disulfide bonds, and substrate-binding sites.
• The workflow successfully characterized an enzyme from *Drosera capensis*.

Conclusions:

• Detailed annotations and protein structure prediction are vital for selecting enzyme targets from sequence data.
• CASA facilitates the identification of suitable protein targets for biochemistry and structural biology.
• The CASA toolchain is freely available, promoting broader application in enzyme discovery.