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Machine Learning Guided Evaluation of Protein Surface Hydrophobicity.

Source: AIChE
  • Type:
    Conference Presentation
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    AIChE Member Credits 0.5
    AIChE Members $19.00
    AIChE Graduate Student Members Free
    AIChE Undergraduate Student Members Free
    Non-Members $29.00
  • Conference Type:
    AIChE Annual Meeting
  • Presentation Date:
    November 16, 2020
  • Duration:
    15 minutes
  • Skill Level:
    Intermediate
  • PDHs:
    0.30

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Protein surface hydrophobicity and its role protein-surface, protein-ligand, and protein-protein interactions has broad relevance in many fields,1 but remains incompletely understood. Regions of a protein surface with densely packed hydrophobic moieties are believed to preferentially form dewetted interfaces, in part due to the absence of hydrogen bonds between water and hydrophobic atoms/groups.2,3 Nevertheless, the presence of hydrophilic residues within a highly hydrophobic patch (HP) is able to partially disrupt the interface through isolated hydrogen bonding. Previous attempts to identify HPs entailed the use of statistical analysis of previously existing crystal structures, using the spatial proximity of hydrophobic atoms.4 However, these approaches fail to consider the important disrupting effects associated with hydrophilic interactions within the HP regions. Here, we develop a novel algorithm for the identification of clustered hydrophobically interacting protein-surface moieties. Utilizing a three-dimensional Gaussian blur, we are able to identify areas with superimposed hydrophobicity, and through machine-learning based density scanning, we define the shape, surface area, hydrophobic intensity and atoms forming each HP. We further employ this algorithm to correlate information about HPs in proteins with the stability of several immobilized enzymes (spanning a wide range of surface hydrophobicities) that were covalently attached to polymer brushes with different chemical compositions. By correlating enzyme stability with information of HPs on the surface of each enzyme, we were able to test hypotheses about how different brush compositions stabilized the enzymes.

(1) Sammond, D. W.; Yarbrough, J. M.; Mansfield, E.; Bomble, Y. J.; Hobdey, S. E.; Decker, S. R.; Taylor, L. E.; Resch, M. G.; Bozell, J. J.; Himmel, M. E.; et al. Predicting Enzyme Adsorption to Lignin Films by Calculating Enzyme Surface Hydrophobicity. J. Biol. Chem. 2014, 289 (30), 20960–20969. https://doi.org/10.1074/jbc.M114.573642.

(2) Chandler, D. Interfaces and the Driving Force of Hydrophobic Assembly. Nature 2005, 437 (7059), 640–647. https://doi.org/10.1038/nature04162.

(3) Jamadagni, S. N.; Godawat, R.; Garde, S. Hydrophobicity of Proteins and Interfaces: Insights from Density Fluctuations. Annu. Rev. Chem. Biomol. Eng. 2011, 2 (1), 147–171. https://doi.org/10.1146/annurev-chembioeng-061010-114156.

(4) Jacak, R.; Leaver-Fay, A.; Kuhlman, B. Computational Protein Design with Explicit Consideration of Surface Hydrophobic Patches. Proteins Struct. Funct. Bioinforma. 2012, 80 (3), 825–838. https://doi.org/10.1002/prot.23241.

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Checkout

Do you already own this?

Pricing


Individuals

AIChE Member Credits 0.5
AIChE Members $19.00
AIChE Graduate Student Members Free
AIChE Undergraduate Student Members Free
Non-Members $29.00
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