1EKL

TYPE III ANTIFREEZE PROTEIN ISOFORM HPLC 12 E35K

Summary for 1EKL

• Entry DOI: 10.2210/pdb1ekl/pdb
• Descriptor: PROTEIN (ANTIFREEZE PROTEIN TYPE III) (2 entities in total)
• Functional Keywords: antifreeze protein, mutant, ice binding protein, thermal hysteresis protein
• Biological source: Macrozoarces americanus (ocean pout)
• Cellular location: Secreted: P19614
• Total number of polymer chains: 1
• Total formula weight: 6979.30

Authors

Graether, S.P.,Deluca, C.I.,Baardsnes, J.,Hill, G.A.,Davies, P.L.,Jia, Z. (deposition date: 1999-01-21, release date: 1999-04-29, Last modification date: 2023-08-09)

Primary citation

Graether, S.P.,DeLuca, C.I.,Baardsnes, J.,Hill, G.A.,Davies, P.L.,Jia, Z.
Quantitative and qualitative analysis of type III antifreeze protein structure and function.
J.Biol.Chem., 274:11842-11847, 1999

PubMed Abstract: Some cold water marine fishes avoid cellular damage because of freezing by expressing antifreeze proteins (AFPs) that bind to ice and inhibit its growth; one such protein is the globular type III AFP from eel pout. Despite several studies, the mechanism of ice binding remains unclear because of the difficulty in modeling the AFP-ice interaction. To further explore the mechanism, we have determined the x-ray crystallographic structure of 10 type III AFP mutants and combined that information with 7 previously determined structures to mainly analyze specific AFP-ice interactions such as hydrogen bonds. Quantitative assessment of binding was performed using a neural network with properties of the structure as input and predicted antifreeze activity as output. Using the cross-validation method, a correlation coefficient of 0.60 was obtained between measured and predicted activity, indicating successful learning and good predictive power. A large loss in the predictive power of the neural network occurred after properties related to the hydrophobic surface were left out, suggesting that van der Waal's interactions make a significant contribution to ice binding. By combining the analysis of the neural network with antifreeze activity and x-ray crystallographic structures of the mutants, we extend the existing ice-binding model to a two-step process: 1) probing of the surface for the correct ice-binding plane by hydrogen-bonding side chains and 2) attractive van der Waal's interactions between the other residues of the ice-binding surface and the ice, which increases the strength of the protein-ice interaction.

PubMed: 10207002
DOI: 10.1074/jbc.274.17.11842

Experimental method

X-RAY DIFFRACTION (1.65 Å)