Conditional activation of Pik3ca(H1047R) in a knock-in mouse model promotes mammary tumorigenesis and emergence of mutations.

Oncogenic mutations in PIK3CA, which encodes the phosphoinositide-3-kinase (PI3K) catalytic subunit p110α, occur in ∼25% of human breast cancers. In this study, we report the development of a knock-in mouse model for breast cancer where the endogenous Pik3ca allele was modified to allow tissue-specific conditional expression of a frequently found Pik3ca(H1047R) (Pik3ca(e20H1047R)) mutant allele. We found that activation of the latent Pik3ca(H1047R) allele resulted in breast tumors with multiple histological types. Whole-exome analysis of the Pik3ca(H1047R)-driven mammary tumors identified multiple mutations, including Trp53 mutations that appeared spontaneously during the development of adenocarinoma and spindle cell tumors. Further, we used this model to test the efficacy of GDC-0941, a PI3K inhibitor, in clinical development, and showed that the tumors respond to PI3K inhibition.

Progress in predicting protein function from structure: unique features of O-glycosidases.

The Structural Genomics Initiative promises to deliver between 10,000 and 20,000 new protein structures within the next ten years. One challenge will be to predict the functions of these proteins from their structures. Since the newly solved structures will be enriched in proteins with little sequence identity to those whose structures are known, new methods for predicting function will be required. Here we describe the unique structural characteristics of O-glycosidases, enzymes that hydrolyze O-glycosidic bonds between carbohydrates. O-glycosidase function has evolved independently many times and enzymes that carry out this function are represented by a large number of different folds. We show that O-glycosidases none-the-less have characteristic structural features that cross sequence and fold families. The electrostatic surfaces of this class of enzymes are particularly distinctive. We also demonstrate that accurate prediction of O-glycosidase function from structure is possible.

Predicting protein function from structure: unique structural features of proteases.

We have noted consistent structural similarities among unrelated proteases. In comparison with other proteins of similar size, proteases have smaller than average surface areas, smaller radii of gyration, and higher C(alpha) densities. These findings imply that proteases are, as a group, more tightly packed than other proteins. There are also notable differences in secondary structure content between these two groups of proteins: proteases have fewer helices and more loops. We speculate that both high packing density and low alpha-helical content coevolved in proteases to avoid autolysis. By using the structural parameters that seem to show some separation between proteases and nonproteases, a neural network has been trained to predict protease function with over 86% accuracy. Moreover, it is possible to identify proteases whose folds were not represented during training. Similar structural analyses may be useful for identifying other classes of proteins and may be of great utility for categorizing the flood of structures soon to flow from structural genomics initiatives.