한빛사논문
- 조회910
Aerin Yang 1, Kevin M Jude 1,2, Ben Lai 3, Mason Minot 4, Anna M Kocyla 1, Caleb R Glassman 1, Daisuke Nishimiya 1, Yoon Seok Kim 1, Sai T Reddy 4, Aly A Khan 3,5, K Christopher Garcia 1,2,6
1Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA.
2Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
3Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.
4Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
5Departments of Pathology and Family Medicine, University of Chicago, Chicago, IL 60637, USA.
6Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Corresponding author : K Christopher Garcia
Abstract
Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.
논문정보
- Research article
- 2023년 07월 (BRIC 등록일 2023-07-31)
- 국외 연구진
- 생명과학 > 생화학
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