Proteome-Wide Assessment of Protein Structural Perturbations under High Pressure

• Published in: PRX Life 2024-09, Vol.2 (3), Article 033011
• Main Authors: Moran, Haley M., Manriquez-Sandoval, Edgar, Sharma, Piyoosh, Fried, Stephen D., Gillilan, Richard E.
• Format: Article
• Language: English
• ISSN: 2835-8279; 2835-8279
• DOI: 10.1103/PRXLife.2.033011

One of the planet's more understudied ecosystems is the deep biosphere, where organisms can experience high hydrostatic pressures (30–110 MPa); yet, by current estimates, these subsurface and deep ocean zones host the majority of the Earth's microbial and animal life. The extent to which terrestrially relevant pressures up to 100 MPa deform most globular proteins—and which kinds—has not been established. Here, we report the invention of an experimental apparatus that enables structural proteomic methods to be carried out at high pressures for the first time. The method, called high-pressure limited proteolysis (Hi-P LiP), involves performing pulse proteolysis on whole cell extracts brought to high pressure. The resulting sites of proteolytic susceptibility induced by pressure are subsequently read out by sequencing the peptide fragments with tandem liquid chromatography–mass spectrometry. The method sensitively detects pressure-induced structural changes with residue resolution and on whole proteomes, providing a deep and broad view of the effect of pressure on protein structure. When applied to a piezosensitive thermophilic bacterium, , we find that approximately 40% of its soluble proteome is structurally perturbed at 100 MPa. Proteins with lower charge density are more resistant to pressure-induced deformation, as expected; however, contrary to expectations, proteins with lower packing density (i.e., more voids) are also more resistant to deformation. Furthermore, high pressure has previously been shown to preferentially alter conformations around active sites. Here, we show this is also observed in Hi-P LiP, suggesting that the method could provide a generic and unbiased modality to detect binding sites on a proteome scale. Hence, data sets of this kind could prove useful for training emerging artificial intelligence models to predict cryptic binding sites with greater accuracy.