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Optimization of Combinatorial Mutagenesis

机译:组合诱变的优化

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Protein engineering by combinatorial site-directed mutagenesis evaluates a portion of the sequence space near a target protein, seeking variants with improved properties (stability, activity, immuno-genicity, etc.). In order to improve the hit-rate of beneficial variants in such mutagenesis libraries, we develop methods to select optimal positions and corresponding sets of the mutations that will be used, in all combinations, in constructing a library for experimental evaluation. Our approach, OCoM (Optimization of Combinatorial Mutagenesis), encompasses both degenerate oligonucleotides and specified point mutations, and can be directed accordingly by requirements of experimental cost and library size. It evaluates the quality of the resulting library by one-and two-body sequence potentials, averaged over the variants. To ensure that it is not simply recapitulating extant sequences, it balances the quality of a library with an explicit evaluation of the novelty of its members. We show that, despite dealing with a combinatorial set of variants, in our approach the resulting library optimization problem is actually iso-morphic to single-variant optimization. By the same token, this means that the two-body sequence potential results in an NP-hard optimization problem. We present an efficient dynamic programming algorithm for the one-body case and a practically-efficient integer programming approach for the general two-body case. We demonstrate the effectiveness of our approach in designing libraries for three different case study proteins targeted by previous combinatorial libraries—a green fluorescent protein, a cytochrome P450, and a beta lactamase. We found that OCoM worked quite efficiently in practice, requiring only 1 hour even for the massive design problem of selecting 18 mutations to generate 107 variants of a 443-residue P450. We demonstrate the general ability of OCoM in enabling the protein engineer to explore and evaluate trade-offs between quality and novelty as well as library construction technique, and identify optimal libraries for experimental evaluation.
机译:通过组合定点诱变的蛋白质工程技术可评估目标蛋白质附近序列空间的一部分,以寻找具有改善的特性(稳定性,活性,免疫原性等)的变体。为了提高此类诱变文库中有益变体的命中率,我们开发了选择最佳位置和相应突变组的方法,这些方法将在所有组合中用于构建用于实验评估的库。我们的方法OCoM(组合诱变的优化)既包括简并的寡核苷酸又包括指定的点突变,并且可以根据实验成本和文库大小的要求进行相应的指导。它通过一体和两体序列电势(在变体中平均)来评估所得文库的质量。为了确保它不只是简单地概括现存的序列,它在平衡库的质量和对其成员的新颖性进行明确评估之间取得了平衡。我们表明,尽管处理了组合的变量集,但在我们的方法中,所产生的库优化问题实际上与单变量优化是同构的。同样,这意味着两体序列势会导致NP困难的优化问题。我们提出了一种有效的动态编程算法,适用于单身体情况,而实用的整数编程方法适用于一般的两身体情况。我们证明了我们的方法在设计针对先前组合文库靶向的三种不同案例研究蛋白的文库中的有效性:绿色荧光蛋白,细胞色素P450和β内酰胺酶。我们发现OCoM在实践中非常有效地工作,即使对于选择18个突变以产生443个残基的P450的107个变异的大规模设计问题,也只需要1个小时。我们展示了OCoM在使蛋白质工程师探索和评估质量与新颖性以及库构建技术之间的取舍以及确定用于实验评估的最佳库方面的一般能力。

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