CodonMutator: a python-based automated oligonucleotide design framework for deep mutational scanning library...

Area of Science:

• Molecular Biology
• Bioinformatics
• Functional Genomics

Background:

• Deep mutational scanning (DMS) is crucial for understanding protein sequence-function relationships.
• Current DMS library construction methods are often complex, labor-intensive, and prone to errors, limiting scalability.
• Existing strategies face challenges in generating comprehensive and unbiased variant libraries.

Purpose of the Study:

• To present a systematic cloning framework and automated Python pipeline for efficient DMS library construction.
• To overcome limitations of existing mutagenesis strategies in scalability and accuracy.
• To provide a flexible and accessible solution for generating diverse protein variant libraries.

Main Methods:

• Developed a restriction enzyme-guided tiling strategy to fragment coding sequences.
• Implemented an automated Python pipeline for oligonucleotide design, supporting strict and relaxed modes.
• Integrated molecular cloning constraints with computational automation for library design.

Main Results:

• The framework ensures uniform sequence coverage and compatibility with standard cloning workflows.
• The pipeline minimizes redundancy and uses silent mutations to avoid restriction site conflicts.
• Optimized library design efficiency and improved accuracy for variant generation.

Conclusions:

• The presented platform offers a scalable and accurate solution for DMS library construction.
• This approach accelerates functional genomics studies by simplifying variant generation.
• The method is adaptable to diverse genes and experimental contexts, enhancing accessibility.