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

Premise: With the advent of inexpensive nucleic acid sequencing and automated annotation at the level of basic functionality, the central problem of enzyme discovery is no longer finding active sequences, it is determining which ones are suitable for further study. This requires annotation that goes beyond sequence similarity to known enzymes and provides information at the sequence and structural levels. Methods: Here we introduce a workflow for generating highly informative, richly annotated sequence alignments from protein sequence data. Computer-Assisted Sequence Annotation (CASA) is a freely available Python-based workflow designed to automate portions of novel protein characterization, while producing a human-interpretable final output. Results: We demonstrate CASA using one enzyme from the Discussion: This work demonstrates the utility of detailed annotations and protein structure prediction for choosing protein targets for biochemistry or structural biology from nucleic acid sequence data. The toolchain is freely available along with instructions and representative examples.