Sequence Design of Random Heteropolymers as Protein Mimics
| Autor: | Ivan Jayapurna, Zhiyuan Ruan, Marco Eres, Prajna Jalagam, Spencer Jenkins, Ting Xu |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Biomacromolecules, vol 24, iss 2 |
| ISSN: | 1526-4602 1525-7797 |
| DOI: | 10.1021/acs.biomac.2c01036 |
| Popis: | Random heteropolymers (RHPs) have been computationally designed and experimentally shown to recapitulate protein-like phase behavior and function. However, unlike proteins, RHP sequences are only statistically defined and cannot be sequenced. Recent developments in reversible-deactivation radical polymerization allowed simulated polymer sequences based on the well-established Mayo-Lewis equation to more accurately reflect ground-truth sequences that are experimentally synthesized. This led to opportunities to perform bioinformatics-inspired analysis on simulated sequences to guide the design, synthesis, and interpretation of RHPs. We compared batches on the order of 10000 simulated RHP sequences that vary by synthetically controllable and measurable RHP characteristics such as chemical heterogeneity and average degree of polymerization. Our analysis spans across 3 levels: segments along a single chain, sequences within a batch, and batch-averaged statistics. We discuss simulator fidelity and highlight the importance of robust segment definition. Examples are presented that demonstrate the use of simulated sequence analysis for in-silico iterative design to mimic protein hydrophobic/hydrophilic segment distributions in RHPs and compare RHP and protein sequence segments to explain experimental results of RHPs that mimic protein function. To facilitate the community use of this workflow, the simulator and analysis modules have been made available through an open source toolkit, the RHPapp. |
| Databáze: | OpenAIRE |
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