MDC-Analyzer: A novel degenerate primer design tool for the construction of intelligent mutagenesis libraries with contiguous sites. (June 2014)

• Record Type: Journal Article
• Authors: Tang, Lixia; Wang, Xiong; Ru, Beibei; Sun, Hengfei; Huang, Jian; Gao, Hui
• Is Part Of: Biotechniques. Volume 56:Number 6(2014)
• Page Start: 301
• Page End: 310
• Publication Date: 2014-06
• Subjects: partial randomization -- intelligent library design -- contiguous sites -- data-driven protein engineering; Biology, Experimental -- Periodicals; Molecular biology -- Periodicals; Medical technology -- Periodicals; Biology, Experimental; Medical technology; Molecular biology; Clinical Laboratory Techniques -- Periodicals; Research -- Periodicals; Medical Laboratory Science -- Periodicals; Periodicals; Electronic journals; 570
• DOI: 10.2144/000114177
• Languages: English
• ISSNs: 0736-6205

Abstract

Recent computational and bioinformatics advances have enabled the efficient creation of novel biocatalysts by reducing amino acid variability at hot spot regions. To further expand the utility of this strategy, we present here a tool called Multi-site Degenerate Codon Analyzer (MDC-Analyzer) for the automated design of intelligent mutagenesis libraries that can completely cover user-defined randomized sequences, especially when multiple contiguous and/or adjacent sites are targeted. By initially defining an objective function, the possible optimal degenerate PCR primer profiles could be automatically explored using the heuristic approach of Greedy Best-First-Search. Compared to the previously developed DC-Analyzer, MDC-Analyzer allows for the existence of a small amount of undesired sequences as a tradeoff between the number of degenerate primers and the encoded library size while still providing all the benefits of DC-Analyzer with the ability to randomize multiple contiguous sites. MDC-Analyzer was validated using a series of randomly generated mutation schemes and experimental case studies on the evolution of halohydrin dehalogenase, which proved that the MDC methodology is more efficient than other methods and is particularly well-suited to exploring the sequence space of proteins using data-driven protein engineering strategies.