PyTEA-O: a Python implementation of Two-Entropies Analysis for protein sequence variation analysis

Area of Science:

• Biochemistry and Molecular Biology
• Computational Biology
• Structural Biology

Background:

• Protein sequence variation analysis is crucial for drug discovery and protein engineering.
• Existing computational tools often lack consideration of amino acid properties, flexibility, source code accessibility, or compatibility with small multiple sequence alignments (MSAs).

Purpose of the Study:

• To address limitations of existing tools, this study introduces PyTEA-O, a user-friendly Python implementation of Two-Entropies Analysis.
• The goal is to facilitate the analysis of protein sequence variation and identify residues critical for protein function specificity.

Main Methods:

• PyTEA-O utilizes Python for analyzing multiple sequence alignments (MSAs).
• It generates modifiable and intuitive visualizations to aid users in screening for residues of interest.
• A scoring approach is employed to identify alignment positions with distinct physicochemical properties.

Main Results:

• PyTEA-O provides a powerful tool for sequence variability analysis through visualizations and scoring.
• A case study on deubiquitinase OTUD7B (Cezanne) identified a crucial position modulating substrate affinity.
• The tool successfully demonstrates its capability in pinpointing functionally significant residues.

Conclusions:

• PyTEA-O offers an accessible and effective solution for protein sequence variation analysis.
• The tool supports drug discovery and protein engineering by identifying key residues that modulate protein function and interactions.
• PyTEA-O is available as open-source software with archived data for reproducibility.