Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139.
Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA 02139.
Proc Natl Acad Sci U S A. 2018 Oct 30;115(44):E10342-E10351. doi: 10.1073/pnas.1812939115. Epub 2018 Oct 15.
Many applications in protein engineering require optimizing multiple protein properties simultaneously, such as binding one target but not others or binding a target while maintaining stability. Such multistate design problems require navigating a high-dimensional space to find proteins with desired characteristics. A model that relates protein sequence to functional attributes can guide design to solutions that would be hard to discover via screening. In this work, we measured thousands of protein-peptide binding affinities with the high-throughput interaction assay amped SORTCERY and used the data to parameterize a model of the alpha-helical peptide-binding landscape for three members of the Bcl-2 family of proteins: Bcl-x, Mcl-1, and Bfl-1. We applied optimization protocols to explore extremes in this landscape to discover peptides with desired interaction profiles. Computational design generated 36 peptides, all of which bound with high affinity and specificity to just one of Bcl-x, Mcl-1, or Bfl-1, as intended. We designed additional peptides that bound selectively to two out of three of these proteins. The designed peptides were dissimilar to known Bcl-2-binding peptides, and high-resolution crystal structures confirmed that they engaged their targets as expected. Excellent results on this challenging problem demonstrate the power of a landscape modeling approach, and the designed peptides have potential uses as diagnostic tools or cancer therapeutics.
许多蛋白质工程应用都需要同时优化多个蛋白质性质，例如结合一个靶标但不结合其他靶标，或者在保持稳定性的同时结合靶标。这种多态设计问题需要在高维空间中进行导航，以找到具有所需特性的蛋白质。一种将蛋白质序列与功能属性相关联的模型可以指导设计，找到通过筛选难以发现的解决方案。在这项工作中，我们使用高通量相互作用测定法 amped SORTCERY 测量了数千种蛋白质-肽结合亲和力，并利用这些数据为 Bcl-2 家族的三个成员（Bcl-x、Mcl-1 和 Bfl-1）的α螺旋肽结合景观模型进行参数化。我们应用优化协议来探索这个景观中的极端情况，以发现具有所需相互作用特征的肽。计算设计生成了 36 个肽，所有肽都与 Bcl-x、Mcl-1 或 Bfl-1 中的一种具有高亲和力和特异性结合，正如预期的那样。我们设计了额外的肽，这些肽可以选择性地与这三种蛋白质中的两种结合。这些设计的肽与已知的 Bcl-2 结合肽不同，高分辨率晶体结构证实它们与预期的靶标结合。在这个具有挑战性的问题上取得了出色的结果，证明了景观建模方法的强大功能，并且这些设计的肽具有作为诊断工具或癌症治疗剂的潜在用途。
