Essential role of PI3-kinase pathway in p53-mediated transcription: Implications in cancer chemotherapy

The PI3-kinase pathway is the target of inactivation in achieving better cancer chemotherapy. Here, we report that p53-mediated transcription is inhibited by pharmacological inhibitors and a dominant-negative mutant of PI3-kinase, and this inhibition was relieved by a constitutively active mutant of PI3-kinase. Akt/PKB and mTOR, the downstream effectors of PI3-kinase, were also found to be essential. LY294002 (PI3-kinase inhibitor) pre-treatment altered the post-translational modifications and the sub-cellular localization of p53. Although LY294002 increased the chemosensitivity of cells to low concentrations of adriamycin (adriamycin-low), it protected the cells from cytotoxicity induced by high concentrations of adriamycin (adriamycin-high) in a p53-dependent manner. Further, we found that LY294002 completely abolished the activation of p53 target genes (particularly pro-apoptotic) under adriamycin-high conditions, whereas it only marginally repressed the p53 target genes under adriamycin-low conditions; in fact, it further activated the transcription of NOXA, HRK, APAF1 and CASP5 genes. Thus, the differential effect of PI3-kinase on p53 functions seems to be responsible for the differential regulation of DNA damage-induced cytotoxicity and cell death by PI3-kinase. Our finding becomes relevant in the light of ongoing combination chemotherapy trials with the PI3-kinase pathway inhibitors and underscores the importance of p53 status in the careful formulation of combination chemotherapies. Oncogene (2010) 29, 3605-3618; doi: 10.1038/onc.2010.123; published online 26 April 2010

Assembling novel protein folds from super-secondary structural fragments

The results of applying a fragment-based protein tertiary structure prediction method to the prediction of 14 CASP5 target domains are described. The method is based on the assembly of supersecondary structural fragments taken from highly resolved protein structures using a simulated annealing algorithm. A number of good predictions for proteins with novel folds were produced, although not always as the first model. For two fold recognition targets, FRAGFOLD produced the most accurate model in both cases, despite the fact that the predictions were not based on a template structure. Although clear progress has been made in improving FRAGFOLD since CASP4, the ranking of final models still seems to be the main problem that needs to be addressed before the next CASP experiment

Protein contact prediction using patterns of correlation

We describe a new method for using neural networks to predict residue contact pairs in a protein. The main inputs to the neural network are a set of 25 measures of correlated mutation between all pairs of residues in two windows of size 5 centered on the residues of interest. While the individual pair-wise correlations are a relatively weak predictor of contact, by training the network on windows of correlation the accuracy of prediction is significantly improved. The neural network is trained on a set of 100 proteins and then tested on a disjoint set of 1033 proteins of known structure. An average predictive accuracy of 21.7% is obtained taking the best L/2 predictions for each protein, where L is the sequence length. Taking the best L/10 predictions gives an average accuracy of 30.7%. The predictor is also tested on a set of 59 proteins from the CASP5 experiment. The accuracy is found to be relatively consistent across different sequence lengths, but to vary widely according to the secondary structure. Predictive accuracy is also found to improve by using multiple sequence alignments containing many sequences to calculate the correlations. (C) 2004 Wiley-Liss, Inc.