Dynamic geometry design of cyclic peptide architectures for RNA structure

Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility. Thus, the current approach usually needs to screen thousands of candidate inhibitors for binding. Here, we propose a dynamic geometry design approach to enrich the hits with only a tiny pool of designed geometrically compatible scaffold candidates. First, our method uses graph-based tree decomposition to explore the complementarity rigid binding cyclic peptide and design the amino acid side chain length and charge to fit the RNA pocket. Then, we perform an energy-based dynamical network algorithm to optimize the inhibitor-RNA hydrogen bonds. Dynamic geometry-guided design yields successful inhibitors with low micromolar binding affinity scaffolds and experimentally competes with the natural RNA chaperone. The results indicate that the dynamic geometry method yields higher efficiency and accuracy than traditional methods. The strategy could be further optimized to design the length and chirality by adopting nonstandard amino acids and facilitating RNA engineering for biological or medical applications. Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility.

Automated summary

This study proposes a dynamic geometry design approach for designing inhibitors for RNA, which enriches the results with a small pool of geometrically compatible scaffold candidates. The method explores the complementarity rigid binding cyclic peptide using graph-based tree decomposition and designs the amino acid side chain length and charge to fit the RNA pocket. The authors optimize the inhibitor-RNA hydrogen bonds using an energy-based dynamical network algorithm. The results show that the dynamic geometry method is more efficient and accurate than traditional methods.