Computational design of macrocyclic binders of S100B(ββ): novel peptide theranostics

• Main Authors: Kannan, Srinivasaraghavan, Aronica, Pietro G. A., Nguyen, Thanh Binh, Li, Jianguo, Verma, Chandra Shekhar
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Biological sciences; Peptide Design; Molecular Dynamics
• Online Access: https://hdl.handle.net/10356/153892
• Institution: Nanyang Technological University

S100B(ββ) proteins are a family of multifunctional proteins that are present in several tissues and regulate a wide variety of cellular processes. Their altered expression levels have been associated with several human diseases, such as cancer, inflammatory disorders and neurodegenerative conditions, and hence are of interest as a therapeutic target and a biomarker. Small molecule inhibitors of S100B(ββ) have achieved limited success. Guided by the wealth of available experimental structures of S100B(ββ) in complex with diverse peptides from various protein interacting partners, we combine comparative structural analysis and molecular dynamics simulations to design a series of peptides and their analogues (stapled) as S100B(ββ) binders. The stapled peptides were subject to in silico mutagenesis experiments, resulting in optimized analogues that are predicted to bind to S100B(ββ) with high affinity, and were also modified with imaging agents to serve as diagnostic tools. These stapled peptides can serve as theranostics, which can be used to not only diagnose the levels of S100B(ββ) but also to disrupt the interactions of S100B(ββ) with partner proteins which drive disease progression, thus serving as novel therapeutics.