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使用极度浓缩文库对南极假丝酵母脂肪酶 A 的底物口袋进行组合重塑以实现对映选择性。

Combinatorial reshaping of the Candida antarctica lipase A substrate pocket for enantioselectivity using an extremely condensed library.

机构信息

Department of Organic Chemistry, Arrhenius Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden.

出版信息

Proc Natl Acad Sci U S A. 2012 Jan 3;109(1):78-83. doi: 10.1073/pnas.1111537108. Epub 2011 Dec 16.

Abstract

A highly combinatorial structure-based protein engineering method for obtaining enantioselectivity is reported that results in a thorough modification of the substrate binding pocket of Candida antarctica lipase A (CALA). Nine amino acid residues surrounding the entire pocket were simultaneously mutated, contributing to a reshaping of the substrate pocket to give increased enantioselectivity and activity for a sterically demanding substrate. This approach seems to be powerful for developing enantioselectivity when a complete reshaping of the active site is required. Screening toward ibuprofen ester 1, a substrate for which previously used methods had failed, gave variants with a significantly increased enantioselectivity and activity. Wild-type CALA has a moderate activity with an E value of only 3.4 toward this substrate. The best variant had an E value of 100 and it also displayed a high activity. The variation at each mutated position was highly reduced, comprising only the wild type and an alternative residue, preferably a smaller one with similar properties. These minimal binary variations allow for an extremely condensed protein library. With this highly combinatorial method synergistic effects are accounted for and the protein fitness landscape is explored efficiently.

摘要

本文报道了一种高度组合的基于结构的蛋白质工程方法,用于获得对映选择性,该方法彻底改变了南极假丝酵母脂肪酶 A(CALA)的底物结合口袋。同时突变了围绕整个口袋的九个氨基酸残基,有助于重塑底物口袋,从而提高了对空间要求高的底物的对映选择性和活性。当需要完全重塑活性位点时,这种方法似乎对于开发对映选择性非常有效。对布洛芬酯 1 进行筛选,这是以前使用的方法未能成功的底物,得到了对映选择性和活性显著提高的变体。野生型 CALA 对此底物的活性仅为 3.4,具有中等活性。最佳变体的 E 值为 100,其活性也很高。每个突变位置的变化都非常少,仅包含野生型和另一种替代残基,优选具有相似性质的更小残基。这些最小的二元变化允许形成极其浓缩的蛋白质文库。通过这种高度组合的方法,可以考虑协同效应,并有效地探索蛋白质适应度景观。

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