Novel strategy for protein exploration: High-throughput screening. assisted with fuzzy neural network
Novel strategy for protein exploration: High-throughput screening. assisted with fuzzy neural network
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DOI：
10.1016/j.jmb.2005.05.026
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发表时间：
2005-08-19
影响因子：
5.6
通讯作者：
Honda, H
中科院分区：
文献类型：
--
作者：
Kato, R;Nakano, H;Honda, H
高通量筛选技术结合定向进化技术是从天然蛋白质中改造具有所需特性的蛋白质的关键技术。然而，大多数HTS技术都是简单的阳性筛查。从阳性候选者获得的信息仅作为结果而很少作为理解结构规则的线索，这可能解释蛋白质的活性。作为一个模型的情况下，我们探讨了脂肪酶的底物对硝基苯基3-苯基丁酸酯从野生型脂肪酶洋葱伯克霍尔德菌KWI-56，这是原来的选择性（S）-构型的基板反向对映体选择性。从我们以前的工作（R）-对映体选择性脂肪酶筛选的数据被施加到模糊神经网络（FNN），生物信息学算法，提取筛选和工程过程中要遵循的准则。模糊神经网络具有提取变异序列与其酶活性之间的隐藏规则以获得高预测精度的优点，在没有任何先验知识的情况下，模糊神经网络预测了指示"位置L167的大小"的规则，在四个位置中，（L17、F119、L167和L266）是获得具有反向（R）-对映体选择性的脂肪酶的最有影响的因素。基于所获得的指导原则，在实际筛选中未发现的新工程化的新变体通过工程化位置L167处的大小而被实验证明获得高（R）-对映体选择性。我们还设计并测定了两种新的变异体，即FIGV（L17 F，F119 I，L167 G和L266 V）和FFGI（L17 F，L167 G和L266 I），它们与FNN分析得到的指导一致，并证实这些设计的脂肪酶可以获得高的反向对映选择性。结果表明，在生物信息学分析的帮助下，高通量筛选可以扩展其探索蛋白质的巨大组合序列空间的潜力。(c)2005爱思唯尔有限公司保留所有权利。
To engineer proteins with desirable characteristics from a naturally occurring protein, high-throughput screening (HTS) combined with directed evolutional approach is the essential technology. However, most HTS techniques are simple positive screenings. The information obtained from the positive candidates is used only as results but rarely as clues for understanding the structural rules, which may explain the protein activity.In here, we have attempted to establish a novel strategy for exploring functional proteins associated with computational analysis. As a model case, we explored lipases with inverted enantioselectivity for a substrate p-nitrophenyl 3-phenylbutyrate from the wild-type lipase of Burkhorderia cepacia KWI-56, which is originally selective for (S)-configuration of the substrate. Data from our previous work on (R)-enantioselective lipase screening were applied to fuzzy neural network (FNN), bioinformatic algorithm, to extract guidelines for screening and engineering processes to be followed. FNN has an advantageous feature of extracting hidden rules that lie between sequences of variants and their enzyme activity to gain high prediction accuracy.Without any prior knowledge, FNN predicted a rule indicating that "size at position L167,"among four positions (L17, F119, L167, and L266) in the substrate binding core region, is the most influential factor for obtaining lipase with inverted (R)-enantioselectivity. Based on the guidelines obtained, newly engineered novel variants, which were not found in the actual screening, were experimentally proven to gain high (R)-enantioselectivity by engineering the size at position L167. We also designed and assayed two novel variants, namely FIGV (L17F, F119I, L167G, and L266V) and FFGI (L17F, L167G, and L266I), which were compatible with the guideline obtained from FNN analysis, and confirmed that these designed lipases could acquire high inverted enantioselectivity. The results have shown that with the aid of bioinformatic analysis, high-throughput screening can expand its potential for exploring vast combinatorial sequence spaces of proteins. (c) 2005 Elsevier Ltd. All rights reserved.