Explicit representation of protein activity states significantly improves causal discovery of protein phosphorylation networks.

ABSTRACT:

Background

Protein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected

SUBMITTER: Liu J 

PROVIDER: S-EPMC7496209 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature