Predicting protein degradation rates

1. Predicting protein degradation rates Karen Page

2. The central dogma • DNARNAprotein TranscriptionTranslation • The expression of genetic information stored in DNA involves, first transcription into RNA and then translation into the functional protein molecules, in which the amino acid sequence is determined by the nucleotide sequence of the DNA.

3. Gene expression • All cells in your body have the same genomic DNA (up to a very small mutational error), ie. the sequences of nucleotides within the chromosomes are identical. • How then do different cells maintain very different characteristics? • The answer is that not all of the genes in the genome are being transcribed and translated into proteins in every cell. We say that genes which are transcribed & translated are expressed in the cells. • Gene expression controls distinct identities of cells via functional protein molecules (cf. microarrays).

4. Microarrays

5. Proteins are what matters • What we really want to know is the level of proteins in the cell, not mRNAs. • Protein level is dependent on the level of mRNA, the translation rate of the protein and its degradation rate. • We know about the genome and it is relatively easy to measure mRNA level (it is possible to measure protein level too, but harder). • We want to be able to predict protein translation rate and degradation rate.

6. Project • This project looks at predicting protein degradation rate based on the amino acid sequence of the protein. • It is well-known that certain sequence features lead to more rapid degradation (eg. N end rules). • We want to use machine learning techniques, such as neural nets, to predict protein degradation rates from their sequences. • This will have important implications for understanding the way in which the genetic code expresses itself and may help in the reconstruction of cellular signalling pathways.