Holden Jeffrey K, Pavlovicz Ryan, Gobbi Alberto, Song Yifan, Cunningham Christian N
Department of Early Discovery Biochemistry, Genentech, South San Francisco, CA, United States.
Cyrus Biotechnology, Seattle, WA, United States.
Front Mol Biosci. 2022 Apr 14;9:848689. doi: 10.3389/fmolb.2022.848689. eCollection 2022.
Technologies for discovering peptides as potential therapeutics have rapidly advanced in recent years with significant interest from both academic and pharmaceutical labs. These advancements in turn drive the need for new computational tools to design peptides for purposes of advancing lead molecules into the clinic. Here we report the development and application of a new automated tool, AutoRotLib, for parameterizing a diverse set of non-canonical amino acids (NCAAs), N-methyl, or peptoid residues for use with the computational design program Rosetta. In addition, we developed a protocol for designing thioether-cyclized macrocycles within Rosetta, due to their common application in mRNA display using the RaPID platform. To evaluate the utility of these new computational tools, we screened a library of canonical and NCAAs on both a linear peptide and a thioether macrocycle, allowing us to quickly identify mutations that affect peptide binding and subsequently measure our results against previously published data. We anticipate screening of peptides against a diverse chemical space will be a fundamental component for peptide design and optimization, as more amino acids can be explored in a single screen than an screen. As such, these tools will enable maturation of peptide affinity for protein targets of interest and optimization of peptide pharmacokinetics for therapeutic applications.
近年来,发现具有潜在治疗作用的肽的技术迅速发展,受到了学术实验室和制药实验室的广泛关注。这些进展反过来推动了对新计算工具的需求,以便设计肽,将先导分子推进到临床试验阶段。在此,我们报告了一种新的自动化工具AutoRotLib的开发和应用,该工具用于为一系列不同的非标准氨基酸(NCAA)、N-甲基或类肽残基设置参数,以便与计算设计程序Rosetta一起使用。此外,由于硫醚环化大环化合物在使用RaPID平台的mRNA展示中具有广泛应用,我们开发了一种在Rosetta中设计硫醚环化大环化合物的方案。为了评估这些新计算工具的实用性,我们在一个线性肽和一个硫醚大环化合物上筛选了一个标准氨基酸和非标准氨基酸库,这使我们能够快速识别影响肽结合的突变,并随后将我们的结果与先前发表的数据进行比较。我们预计,针对不同化学空间筛选肽将成为肽设计和优化的一个基本组成部分,因为在单次筛选中可以探索的氨基酸比传统筛选更多。因此,这些工具将能够提高肽对感兴趣蛋白质靶点的亲和力,并优化肽在治疗应用中的药代动力学。