MOTIVATION: Protein sequence variation analysis is a topic of broad interest in drug discovery and protein engineering to support modulation of protein function for diverse biotechnological and therapeutic applications. To assist in the analysis of multiple sequence alignments (MSAs) and identify residues that account for protein function specificity, computational tools have been developed. Yet, existing programs often omit consideration of amino acid properties, flexibility beyond fixed webserver interfaces, accessible source code, or compatibility with small MSAs. RESULTS: To address these limitations, we present PyTEA-O, a Python implementation of Two-Entropies Analysis that has been developed to be easy to use for the analysis of protein sequence variation. To help users analyze the MSA and screen for residues of interest, we generate modifiable and intuitive visualizations. These visualizations, together with a scoring approach for identifying alignment positions with (dis-)similar physicochemical properties, presents a powerful tool for sequence variability analysis. To demonstrate its capabilities, we present a case study based on the deubiquitinase OTUD7B (Cezanne) where we identify a crucial position that modulates its affinity for its substrate. AVAILABILITY AND IMPLEMENTATION: PyTEA-O is available at https://github.com/CDDLeiden/PyTEA-O/ and archived via Zenodo (https://doi.org/10.5281/zenodo.15914598).

MOTIVATION: The generation and analysis of diverse mutants of a protein is a powerful tool for understanding protein function. However, generating such mutants can be time-consuming, while the commercial option of buying a series of mutant plasmids can be expensive. In contrast, the insertion of a synthesized double-stranded DNA (dsDNA) fragment into a plasmid is a fast and low-cost method to generate a large library of mutants with one or more point mutations, insertions, or deletions. RESULTS: To aid in the design of these DNA fragments, we have developed PyVADesign: a Python package that makes the design and ordering of dsDNA fragments straightforward and cost-effective. In PyVADesign, the mutations of interest are clustered in different cloning groups for efficient exchange into the target plasmid. Additionally, primers that prepare the target plasmid for insertion of the dsDNA fragment, as well as primers for sequencing, are automatically designed within the same program. AVAILABILITY AND IMPLEMENTATION: PyVADesign is open source and available at https://github.com/CDDLeiden/PyVADesign and archived via Zenodo (https://doi.org/10.5281/zenodo.15057525).