Documentation of an Imperative To Improve Methods for Predicting Membrane Protein Stability
By B. Kroncke, Amanda M. Duran, Jeffrey L. Mendenhall, Jens Meiler, Jeffrey D. Blume, C. Sanders. 2016
52 citations recorded by the source corpus.
There is a compelling and growing need to accurately predict the impact of amino acid mutations on protein stability for problems in personalized medicine and other applications. Here the ability of 10 computational tools to accurately predict mutation-induced perturbation of folding stability (ΔΔG) for membrane proteins of known structure was assessed. All methods for predicting ΔΔG values performed significantly worse when applied to membrane proteins than when applied to soluble proteins, yielding estimated concordance, Pearson, and Spearman correlation coefficients of <0.4 for membrane proteins. Rosetta and PROVEAN showed a modest ability to classify mutations as destabilizing (ΔΔG < −0.5 kcal/mol), with a 7 in 10 chance of correctly discriminating a randomly chosen destabilizing variant from a randomly chosen stabilizing variant. However, even this performance is significantly worse than for soluble proteins. This study highlights the need for further development of reliable and reproducible methods for predicting thermodynamic folding stability in membrane proteins.
Subfields: Genetics, Molecular Biology
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- Alexey Strokach
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Citations in the corpus
- TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions (2017, methodology): “A comparison with other methods is shown in 136 The results of Rosetta methods are obtained from Fig.”
- Protein Structure Prediction and Design in a Biologically Realistic Implicit Membrane. (2020, background): “Previous studies have evaluated membrane energy functions on a single test such as tilt angles (53), native structure discrimination (57,83), predicting hydrophobic lengths (75), DDG prediction (84), and sequence recovery (85).”
- Fast and flexible design of novel proteins using graph neural networks (2019, methodology): “Limitations of those methods include the relatively low accuracy of existing force fields (6, 7) and the inability to sample more than a miniscule portion of the vast search space (sequence space size is 20N, N being the number of residues).”