Predicted binding site information improves model ranking in protein docking using experimental and computer-generated target structures

BACKGROUND: Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. RESULTS: To address this problem, we developed eRank(PPI), an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank(PPI) employs multiple features including interface probability estimates calculated by eFindSite(PPI) and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank(PPI) consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. CONCLUSIONS: eRank(PPI) was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi

Effects of ceranib-2 on cell survival and TNF-alpha in colon cancer cell line

OBJECTIVE: The aim of this study was to investigate the effects of a novel anti-cancer drug, ceranib-2, which targets the acid ceramidase, in human colon cancer cell line

Altered TGFβ signaling and cardiovascular manifestations in patients with autosomal recessive cutis laxa type I caused by fibulin-4 deficiency

Fibulin-4 is a member of the fibulin family, a group of extracellular matrix proteins prominently expressed in medial layers of large veins and arteries. Involvement of the FBLN4 gene in cardiovascular pathology was shown in a murine model and in three patients affected with cutis laxa in association with systemic involvement. To elucidate the contribution of FBLN4 in human disease, we investigated two cohorts of patients. Direct sequencing of 17 patients with cutis laxa revealed no FBLN4 mutations. In a second group of 22 patients presenting with arterial tortuosity, stenosis and aneurysms, FBLN4 mutations were identified in three patients, two homozygous missense mutations (p.Glu126Lys and p.Ala397Thr) and compound heterozygosity for missense mutation p.Glu126Val and frameshift mutation c.577delC. Immunoblotting analysis showed a decreased amount of fibulin-4 protein in the fibroblast culture media of two patients, a finding sustained by diminished fibulin-4 in the extracellular matrix of the aortic wall on immunohistochemistry. pSmad2 and CTGF immunostaining of aortic and lung tissue revealed an increase in transforming growth factor (TGF)β signaling. This was confirmed by pSmad2 immunoblotting of fibroblast cultures. In conclusion, patients with recessive FBLN4 mutations are predominantly characterized by aortic aneurysms, arterial tortuosity and stenosis. This confirms the important role of fibulin-4 in vascular elastic fiber assembly. Furthermore, we provide the first evidence for the involvement of altered TGFβ signaling in the pathogenesis of FBLN4 mutations in humans