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FEBS Lett. 2019 May 17. doi: 10.1002/1873-3468.13444. [Epub ahead of print]

Computational design and optimization of novel d-peptide TNFα inhibitors.

Author information

1
School of Life Sciences, Tsinghua University, Beijing, China.
2
School of Life Sciences, Peking University, Beijing, China.
3
Peking-Tsinghua Center for Life Sciences, AAIS, Peking University, Beijing, China.
4
BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.
5
Center for Quantitative Biology, AAIS, Peking University, Beijing, China.

Abstract

Compared to small molecule drugs, peptide therapeutics provides greater efficacy, selectivity, and safety. The intrinsic disadvantages of peptides are their sensitivity to proteases. To overcome this, we have developed a general computational strategy for de novo design of protein binding helical d-peptides. A d-helical fragment library was established and used in generating flexible d-helical conformations, which were then used to generate suitable sequences with the required structural and binding properties. Using this strategy, we successfully de novo designed d-helical peptides that bind to tumor necrosis factor-α (TNFα), inhibit TNFα-TNFR1 binding, reduce TNFα activity in cellular assays, and are stable against protease digestion. Our strategy of helical d-peptide design is generally applicable for discovering d-peptide modulators against protein-protein interactions.

KEYWORDS:

TNFα binding peptide; computational protein design; d-helical peptide design; peptides; protein-protein interactions

PMID:
31102258
DOI:
10.1002/1873-3468.13444

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