气候变化领域集成服务门户(CCPortal): Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

CCPortal

DOI	10.1073/pnas.1903888116
	Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis
	Nisthal A.; Wang C.Y.; Ary M.L.; Mayo S.L.
发表日期	2019
ISSN	0027-8424
起始页码	16367
结束页码	16377
卷号	116 期号:33
英文摘要	The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability indirectly. Here, we develop an automated method to generate thermodynamic stability data for nearly every single mutant in a small 56-residue protein. Analysis reveals that most single mutants have a neutral effect on stability, mutational sensitivity is largely governed by residue burial, and unexpectedly, hydrophobics are the best tolerated amino acid type. Correlating the output of various stability-prediction algorithms against our data shows that nearly all perform better on boundary and surface positions than for those in the core and are better at predicting large-to-small mutations than small-to-large ones. We show that the most stable variants in the single-mutant landscape are better identified using combinations of 2 prediction algorithms and including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analyzed and the datasets used to train the algorithm. Finally, we find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and that data produced by these methods may be applicable toward training future stability-prediction tools. © 2019 National Academy of Sciences. All rights reserved.
英文关键词	Mutagenesis; Protein engineering; Protein G; Protein stability prediction; Thermodynamic stability
语种	英语
scopus关键词	amino acid; protein; algorithm; amino acid sequence; Article; benchmarking; chemical composition; computer model; controlled study; correlational study; high throughput sequencing; hydrophobicity; mutagenesis; mutational analysis; predictive value; priority journal; protein analysis; protein engineering; protein stability; sensitivity analysis; thermodynamics; thermostability; amino acid substitution; chemistry; computer simulation; genetics; mutagenesis; mutation; protein domain; Amino Acid Substitution; Amino Acids; Computer Simulation; Mutagenesis; Mutation; Protein Domains; Protein Engineering; Protein Stability; Proteins; Thermodynamics
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160356
作者单位	Nisthal, A., Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States, Protein Engineering, Xencor, Inc., Monrovia, CA 91016, United States; Wang, C.Y., Protabit, LLC, Pasadena, CA 91106, United States; Ary, M.L., Protabit, LLC, Pasadena, CA 91106, United States; Mayo, S.L., Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, United States
推荐引用方式 GB/T 7714	Nisthal A.,Wang C.Y.,Ary M.L.,et al. Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis[J],2019,116(33).
APA	Nisthal A.,Wang C.Y.,Ary M.L.,&Mayo S.L..(2019).Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis.Proceedings of the National Academy of Sciences of the United States of America,116(33).
MLA	Nisthal A.,et al."Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis".Proceedings of the National Academy of Sciences of the United States of America 116.33(2019).