Predicting GPCR-G-protein coupling using hidden Markov models.

Motivation: Determining the coupling specificity of G-protein coupled receptors (GPCRs) is important for understanding the biology of this class of pharmacologically important proteins. Currently available in silico methods for predicting GPCR-G-protein coupling specificity have high error rate.

Method: We introduce a new approach for creating hidden Markov models (HMMs) based on a first guess about the importance of various residues.

Interactions required for binding of simian virus 40 T antigen to the viral origin and molecular modeling of initial assembly events.

The purified T-antigen origin binding domain binds site specifically to site II, the central region of the simian virus 40 core origin. However, in the context of full-length T antigen, the origin binding domain interacts poorly with DNA molecules containing just site II. Here we investigate the contributions of additional core origin regions, termed the flanking sequences, to origin recognition and the assembly of T-antigen hexamers and double hexamers.

A novel p53-inducible apoptogenic gene, PRG3, encodes a homologue of the apoptosis-inducing factor (AIF).

The p53 tumor suppressor protein induces cell cycle arrest or apoptosis in response to cellular stresses. We have identified PRG3 (p53-responsive gene 3), which is induced specifically under p53-dependent apoptotic conditions in human colon cancer cells, and encodes a novel polypeptide of 373 amino acids with a predicted molecular mass of 40.5 kDa.