Transcript Predicting protein degradation rates

Predicting protein degradation
rates
Karen Page
The central dogma
• DNA
Transcription
RNA
protein
Translation
• 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.
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).
Microarrays
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.
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.