Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing.

Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing.
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DOI:
10.1038/nbt.2214
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发表时间:
2012-05-27
影响因子:
46.9
通讯作者:
--
中科院分区:
工程技术1区
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--
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我们证明,通过深度测序获得的全面的序列-功能图可以用于重新编程相互作用的特异性,并通过结合许多在传统方法中无法检测到的单独的小贡献来跨越亲和力成熟的瓶颈。我们使用这种方法来优化两种通过计算设计的针对H1N1流感血凝素的抑制剂,在这两种情况下,都获得了具有亚纳米分子结合亲和力的变体。其中最有效的是一种51个残基的蛋白质,它与包括人类H2在内的所有1型流感血凝素具有广泛的交叉反应,并以与几种人类单抗相媲美的效力中和H1N1病毒,表明计算设计和全面的能量图谱可以产生具有潜在治疗用途的蛋白质。
We show that comprehensive sequence-function maps obtained by deep sequencing can be used to reprogram interaction specificity and to leapfrog over bottlenecks in affinity maturation by combining many individually small contributions not detectable in conventional approaches. We use this approach to optimize two computationally designed inhibitors against H1N1 influenza hemagglutinin and, in both cases, obtain variants with subnanomolar binding affinity. The most potent of these, a 51-residue protein, is broadly cross-reactive against all influenza group 1 hemagglutinins, including human H2, and neutralizes H1N1 viruses with a potency that rivals that of several human monoclonal antibodies, demonstrating that computational design followed by comprehensive energy landscape mapping can generate proteins with potential therapeutic utility.
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