AI Article Synopsis
- Directed evolution has revolutionized protein engineering by enabling quick enhancements of protein properties, but it faces challenges due to the vast number of potential protein sequences to explore.
- Identifying mutagenic hot spots—areas on proteins where changes are likely to lead to beneficial outcomes—is crucial for making directed evolution more efficient.
- Recent advancements in protein dynamics and machine learning are offering promising strategies for pinpointing these hot spots, which could significantly propel the field forward.
Article Abstract
Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523367 | PMC |
| http://dx.doi.org/10.1021/jacsau.3c00315 | DOI Listing |
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