Predicting Protein Phenotypes Based on Protein-Protein Interaction Network

BACKGROUND: Identifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. METHODOLOGY/PRINCIPAL FINDINGS: Here, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. CONCLUSIONS/SIGNIFICANCE: The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms

The Lack of Dopamine Transporter Is Associated With Conditional Associative Learning Impairments and Striatal Proteomic Changes.

Dopamine (DA) is critically involved in different functions of the central nervous system (CNS) including control of voluntary movement, affect, reward, sleep, and cognition. One of the key components of DA neurotransmission is DA reuptake by the DA transporter (DAT), ensuring rapid clearance of DA from the synaptic cleft. Thus, lack of DAT leads to persistent high extracellular DA levels. While there is strong evidence for a role of striatal dopaminergic activity in learning and memory processes, little is known about the contribution of DAT deficiency to conditional learning impairments and underlying molecular processes. DAT-knockout (DAT-KO) rats were tested in a set of behavioral experiments evaluating conditional associative learning, which requires unaltered striatal function. In parallel, a large-scale proteomic analysis of the striatum was performed to identify molecular factors probably underlying behavioral patterns. DAT-KO rats were incapable to acquire a new operant skill in Pavlovian/instrumental autoshaping, although the conditional stimulus-unconditional stimulus (CS-US) association seems to be unaffected. These findings suggest that DAT directly or indirectly contributes to the reduction of transference of incentive salience from the reward to the CS. We propose that specific impairment of conditional learning might be caused by molecular adaptations to the hyperdopaminergic state, presumably by dopamine receptor 1 (DRD1) hypofunction, as proposed by proteomic analysis. Whether DRD1 downregulation can cause cognitive deficits in the hyperdopaminergic state is the subject of discussion, and further studies are needed to answer this question. This study may be useful for the interpretation of previous and the design of future studies in the dopamine field