Abstract
Although the use of affinity tags can greatly improve purification of expressed enzymes, the placement of affinity tags can significantly impact the expression, solubility, and function of recombinant proteins. To facilitate the optimal design of 6xHis-tagged constructs for protein purification, we developed Terminator, a Python-based software package, which takes a UniProt ID or existing protein sequence as input, identifies related sequences, maps sequence conservation retrieved from ConSurf onto protein 3D structures retrieved from the PDB and SWISS-MODEL, and analyzes proximity to cavities and functional sites to recommend the N- or C-terminus for placement of 6xHis fusion tags <15 residues in length. The package also outputs a document with available purification and activity literature for the target and closely related proteins organized by year. Comparative analysis of Terminator predictions against published experimental tag behavior for 6xHis fusion tags <15 residues in length demonstrates an 86-100% accuracy in predicting the relative risk of ill effects between termini and a 92-93% accuracy in predicting the absolute risk of modifying individual termini. This reliability of Terminator's analysis suggests that proximity to surface cavities, not burial of wild-type termini, is the most reliable predictor of ill effects arising from short 6xHis fusion tags. This tool aims to expedite construct design and enhance the successful production of well-behaved proteins for studies in enzymology and biocatalysis with minimal need for computational resources, programming knowledge, or familiarity with protein-tag interference mechanisms.