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. Testing popular stability prediction algorithms against our data shows that all perform moderately, and combinations of algorithms are better at identifying the best variants in the single mutant landscape. We find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and may be applicable toward training future stability prediction tools.
Submitter: Connie Wang
Submission Date: Oct. 9, 2018, 3:38 p.m.
|Number of data points||17184|
|Proteins||Immunoglobulin G-binding protein G|
|Assays/Quantities/Protocols||Experimental Assay: Cm ; Experimental Assay: m_value ; Experimental Assay: dG(H20)_mean ; Experimental Assay: ln W ; Derived Quantity: SD of Cm ; Derived Quantity: SD of m_value ; Derived Quantity: SD of dG(H20)_mean ; Derived Quantity: SD of ddG(mAvg)_mean ; Derived Quantity: ddG(mAvg)_mean ; Derived Quantity: dG(mAvg)_mean ; Derived Quantity: SD of dG(mAvg)_mean ; Derived Quantity: Quantitative Set ; Derived Quantity: ddG_wu ; Derived Quantity: ddG_otwinoswki ; Computational Protocol: PoPMuSiC3_ddG ; Computational Protocol: FoldX_ddG ; Computational Protocol: Rosetta NoMin ; Computational Protocol: Rosetta SomeMin ; Computational Protocol: Rosetta FullMin ; Computational Protocol: Rosetta SomeMin_ddG|
|Libraries||∆∆G Data from domain-wide comprehensive mutagenesis ; Benchmark for ∆∆G predictors ; Deep mutational scanning ∆∆G comparisons|