Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry.

Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA in vitro and 13 PKA phosphorylation sites were identified by mass spectrometry. NetPhosK was 100% sensitive and 41% specific in predicting PKA sites in the four proteins. These results demonstrate the potential of using integrated computational and experimental methods for detailed investigations of the phosphoproteome.

Interaction of avilamycin with ribosomes and resistance caused by mutations in 23S rRNA.

The antibiotic growth promoter avilamycin inhibits protein synthesis by binding to bacterial ribosomes. Here the binding site is further characterized on Escherichia coli ribosomes. The drug interacts with domain V of 23S rRNA, giving a chemical footprint at nucleotides A2482 and A2534. Selection of avilamycin-resistant Halobacterium halobium cells revealed mutations in helix 89 of 23S rRNA. Furthermore, mutations in helices 89 and 91, which have previously been shown to confer resistance to evernimicin, give cross-resistance to avilamycin. These data place the binding site of avilamycin on 23S rRNA close to the elbow of A-site tRNA. It is inferred that avilamycin interacts with the ribosomes at the ribosomal A-site interfering with initiation factor IF2 and tRNA binding in a manner similar to evernimicin.