A combinatorially complete epistatic fitness landscape in an enzyme active site

• 成果类型: Article
• 署名作者: Johnston, Kadina E.; Almhjell, Patrick J.; Watkins-Dulaney, Ella J.; Liu, Grace; Porter, Nicholas J.; Yang, Jason; Arnold, Frances H.
• 署名单位: Merck & Company; Merck & Company USA; Stanford University; Harvard University; Harvard Medical School; California Institute of Technology; California Institute of Technology
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12088
• DOI: 10.1073/pnas.2400439121
• 发表日期: 2024-08-06
• 关键词: tryptophan synthase; protein; prediction; mutations

摘要

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence- fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model- guided enzyme engineering approaches. We present here a combinatorially complete, 160,000- variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable beta- subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most- fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation- based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more- active variants, these approaches struggle to identify and differentiate among the most- active variants, even for this near- native function. Overall, this work presents a large- scale testing ground for model- guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.