Center for BioOptical Nanotechnology and Center for Innovations in Medicine, The Biodesign Institute, Arizona State University, Tempe, Arizona, United States of America.
PLoS One. 2010 Nov 11;5(11):e15432. doi: 10.1371/journal.pone.0015432.
There is a significant need for affinity reagents with high target affinity/specificity that can be developed rapidly and inexpensively. Existing affinity reagent development approaches, including protein mutagenesis, directed evolution, and fragment-based design utilize large libraries and/or require structural information thereby adding time and expense. Until now, no systematic approach to affinity reagent development existed that could produce nanomolar affinity from small chemically synthesized peptide libraries without the aid of structural information.
METHODOLOGY/PRINCIPAL FINDINGS: Based on the principle of additivity, we have developed an algorithm for generating high affinity peptide ligands. In this algorithm, point-variations in a lead sequence are screened and combined in a systematic manner to achieve additive binding energies. To demonstrate this approach, low-affinity lead peptides for multiple protein targets were identified from sparse random sequence space and optimized to high affinity in just two chemical steps. In one example, a TNF-α binding peptide with K(d) = 90 nM and high target specificity was generated. The changes in binding energy associated with each variation were generally additive upon combining variations, validating the basis of the algorithm. Interestingly, cooperativity between point-variations was not observed, and in a few specific cases, combinations were less than energetically additive.
CONCLUSIONS/SIGNIFICANCE: By using this additivity algorithm, peptide ligands with high affinity for protein targets were generated. With this algorithm, one of the highest affinity TNF-α binding peptides reported to date was produced. Most importantly, high affinity was achieved from small, chemically-synthesized libraries without the need for structural information at any time during the process. This is significantly different than protein mutagenesis, directed evolution, or fragment-based design approaches, which rely on large libraries and/or structural guidance. With this algorithm, high affinity/specificity peptide ligands can be developed rapidly, inexpensively, and in an entirely chemical manner.
目前需要能够快速、廉价地开发出高靶标亲和力/特异性的亲和试剂。现有的亲和试剂开发方法，包括蛋白质突变、定向进化和基于片段的设计，利用大型文库和/或需要结构信息，从而增加了时间和成本。到目前为止，还没有一种系统的亲和试剂开发方法能够在不需要结构信息的情况下，从小的化学合成肽文库中产生纳摩尔亲和力。
方法/主要发现：基于加和性原理，我们开发了一种生成高亲和力肽配体的算法。在该算法中，对先导序列中的点变异进行筛选，并以系统的方式进行组合，以达到累加的结合能。为了验证这种方法，从稀疏的随机序列空间中鉴定出了多个蛋白质靶标的低亲和力先导肽，并在仅两步化学步骤中优化为高亲和力。在一个例子中，生成了一种 TNF-α 结合肽，其 K(d) = 90 nM，具有高靶标特异性。组合变异时，与每个变异相关的结合能变化通常是加和的，验证了该算法的基础。有趣的是，点变异之间没有观察到协同作用，在少数特定情况下，组合的能量低于累加的能量。
结论/意义：通过使用这种加和算法，生成了对蛋白质靶标具有高亲和力的肽配体。使用该算法，产生了迄今为止报道的最高亲和力 TNF-α 结合肽之一。最重要的是，在整个过程中，无需在任何时候使用结构信息，即可从小的化学合成文库中获得高亲和力。这与蛋白质突变、定向进化或基于片段的设计方法有很大的不同，这些方法依赖于大型文库和/或结构指导。使用该算法，可以快速、廉价、完全以化学方式开发出高亲和力/特异性的肽配体。
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