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Updated: Jan 8, 2026

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

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

Jeongha Lee¹, Seong Kyoon Park², Byung Joon Hwang³

¹Department of Brain Sciences, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu 42988, Korea.

|December 17, 2025

Summary

This summary is machine-generated.

We developed a new automated cloning framework and Python pipeline to simplify deep mutational scanning (DMS) library construction. This method improves efficiency and accuracy for generating comprehensive protein variant libraries, accelerating functional genomics research.

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Last Updated: Jan 8, 2026

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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.