Zhiyin Xun | PubMed Health

PubMed2007

Protein expression in a Drosophila model of Parkinson's disease.

Liquid chromatographies coupled to mass spectrometry and database analysis techniques are used to carry out a large-scale proteome characterization for a Drosophila model of Parkinson's disease. Semiquantitative analysis is performed on A30P alpha-synuclein expressing transgenic Drosophila and a control lacking the gene at presymptomatic, early, and advanced disease stages. Changes in gene expression at the level of the proteome are compared with changes reported from published transcriptome measurements. A summary of the comparison indicates that approximately 44% of transcripts that show changes can also be observed as proteins. However, the patterns of change in protein expression vary substantially compared with the patterns of change observed for corresponding transcripts. In addition, the expression changes of many genes are observed for only transcripts or proteins. Proteome measurements provide evidence for dysregulation of a group of proteins associated with the actin cytoskeleton and mitochondrion at presymptomatic and early disease stages that may presage the development of later symptoms. Overall, the proteome measurements provide a view of gene expression that is highly complementary to the insights obtained from the transcriptome.

Animals↗

PubMed2006

A computational approach toward label-free protein quantification using predicted peptide detectability.

We propose here a new concept of peptide detectability which could be an important factor in explaining the relationship between a protein's quantity and the peptides identified from it in a high-throughput proteomics experiment. We define peptide detectability as the probability of observing a peptide in a standard sample analyzed by a standard proteomics routine and argue that it is an intrinsic property of the peptide sequence and neighboring regions in the parent protein. To test this hypothesis we first used publicly available data and data from our own synthetic samples in which quantities of model proteins were controlled. We then applied machine learning approaches to demonstrate that peptide detectability can be predicted from its sequence and the neighboring regions in the parent protein with satisfactory accuracy. The utility of this approach for protein quantification is demonstrated by peptides with higher detectability generally being identified at lower concentrations over those with lower detectability in the synthetic protein mixtures. These results establish a direct link between protein concentration and peptide detectability. We show that for each protein there exists a level of peptide detectability above which peptides are detected and below which peptides are not detected in an experiment. We call this level the minimum acceptable detectability for identified peptides (MDIP) which can be calibrated to predict protein concentration. Triplicate analysis of a biological sample showed that these MDIP values are consistent among the three data sets.

Algorithms↗