Directed Chemical Evolution of Self-Assembling Artificial Proteins Utilizing a Supramolecular System
Khyade, A. R.; Sandanaraj, B.
Show abstract
Nature utilizes non-directed evolution to generate a vast array of biological macromolecules with remarkable diversity and functionality, though at a relatively slow pace. With advances in biotechnology, both directed and non-directed evolution approaches have been employed to design a wide range of biomacromolecules, including self-assembling artificial proteins (SAPs). However, these biological methods are limited to the natural set of twenty amino acids, resulting in SAPs that often lack functional diversity. Chemical approaches, such as micelle-assisted protein labeling technology (MAPLabTech), have enabled the creation of functional SAPs. Despite its potential, MAPLabTech remains a complex, low-throughput, and time-consuming methodology. To overcome these challenges, herein, we disclose a new method, termed Supramolecule-Assisted Protein Labeling Technology (SAPLabTech). In this method, {gamma}-cyclodextrin acts as a host to solubilize hydrophobic chemical probes, forming a cyclodextrin-probe supramolecular complex that enables site-specific bioconjugation reaction and the synthesis of well-defined monodisperse SAPs in quantitative yield. The versatility of this technology is demonstrated by employing three distinct classes of chemical probes with varying warhead functionalities, linker lengths, and tail hydrophobicity to construct diverse SAP libraries with rich structural features.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Steric-Free Bioorthogonal Labeling of Acetylation Substrates Based on a Fluorine-Thiol Displacement Reaction (FTDR) 96%
- Depsipeptide nucleic acids: prebiotic formation, oligomerization, and self-assembly of a new candidate proto-nucleic acid 95%
- Bottom-up investigation of spatiotemporal glycocalyx dynamics with interferometric scattering microscopy 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.