Quantitative and qualitative analysis of type III antifreeze protein structure and function.
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.
Graether
S P
SP
Department of Biochemistry, Queen's University, Kingston, Ontario, K7L 3N6 Canada.
DeLuca
C I
CI
Baardsnes
J
J
Hill
G A
GA
Davies
P L
PL
Jia
Z
Z
D021301:Antifreeze Proteins; D006023:Glycoproteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D008958:Models, Molecular; D016297:Mutagenesis, Site-Directed; D016571:Neural Networks, Computer; D011487:Protein Conformation; D013329:Structure-Activity Relationship
D021301:Antifreeze Proteins; D006023:Glycoproteins