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Protein structures at solid/liquid interfaces mediate interfacial protein functions, which are important for many applications. It is difficult to probe interfacial protein structures at buried solid/liquid interfaces in situ at the molecular level. Here, a systematic methodology to determine protein molecular structures (orientation and conformation) at buried solid/liquid interfaces in situ was successfully developed with a combined approach using a nonlinear optical spectroscopic technique – sum frequency generation (SFG) vibrational spectroscopy, isotope labeling, spectra calculation, and computer simulation. With this approach, molecular structures of protein GB1 and its mutant (with two amino acids mutated) were investigated at the polymer/solution interface. Markedly different orientations and similar (but not identical) conformations of the wild-type protein GB1 and its mutant at the interface were detected, due to the varied molecular interfacial interactions. This systematic strategy is general and can be widely used to elucidate protein structures at buried interfaces in situ .
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Rosetta custom score functions accurately predict ΔΔ G of mutations at protein–protein interfaces using machine learning
Protein–protein interfaces play essential roles in a variety of biological processes and many therapeutic molecules are targeted at these interfaces. However, accurate predictions of the effects of interfacial mutations to identify “hotspots” have remained elusive despite the myriad of modeling and machine learning methods tested. Here, for the first time, we demonstrate that nonlinear reweighting of energy terms from Rosetta, through the use of machine learning, exhibits improved predictability of ΔΔ G values associated with interfacial mutations.
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- Award ID(s):
- 1708759
- PAR ID:
- 10172056
- Date Published:
- Journal Name:
- Chemical Communications
- Volume:
- 56
- Issue:
- 50
- ISSN:
- 1359-7345
- Page Range / eLocation ID:
- 6774 to 6777
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d0cc01959c
