Architecture of the Herpes Simplex virus major capsid protein derived from structural bioinformatics

The dispositions of 39 α helices of greater than 2.5 turns and four β sheets in the major capsid protein (VP5, 149 kDa) of herpes simplex virus type 1 were identified by computational and visualization analysis from the 8.5 Å electron cryomicroscopy structure of the whole capsid. The assignment of helices in the VP5 upper domain was validated by comparison with the recently determined crystal structure of this region. Analysis of the spatial arrangement of helices in the middle domain of VP5 revealed that the organization of a tightly associated bundle of ten helices closely resembled that of a domain fold found in the annexin family of proteins. Structure-based sequence searches suggested that sequences in both the N and C-terminal portions of the VP5 sequence contribute to this domain. The long helices seen in the floor domain of VP5 form an interconnected network within and across capsomeres. The combined structural and sequence-based informatics has led to an architectural model of VP5. This model placed in the context of the capsid provides insights into the strategies used to achieve viral capsid stability

Dynamin-SNARE interactions control trans-SNARE formation in intracellular membrane fusion.

The fundamental processes of membrane fission and fusion determine size and copy numbers of intracellular organelles. Although SNARE proteins and tethering complexes mediate intracellular membrane fusion, fission requires the presence of dynamin or dynamin-related proteins. Here we study these reactions in native yeast vacuoles and find that the yeast dynamin homologue Vps1 is not only an essential part of the fission machinery, but also controls membrane fusion by generating an active Qa SNARE-tethering complex pool, which is essential for trans-SNARE formation. Our findings provide new insight into the role of dynamins in membrane fusion by directly acting on SNARE proteins

Sugar transport in Sulfolobus solfataricus is mediated by two families of binding protein-dependent ABC transporters

The extreme thermoacidophilic archaeon Sulfolobus solfataricus grows optimally at 80°C and pH 3 and uses a variety of sugars as sole carbon and energy source. Glucose transport in this organism is mediated by a high-affinity binding protein-dependent ATP-binding cassette (ABC) transporter. Sugar-binding studies revealed the presence of four additional membrane-bound binding proteins for arabinose, cellobiose, maltose and trehalose. These glycosylated binding proteins are subunits of ABC transporters that fall into two distinct groups: (i) monosaccharide transporters that are homologous to the sugar transport family containing a single ATPase and a periplasmic-binding protein that is processed at an unusual site at its amino-terminus; (ii) di- and oligosaccharide transporters, which are homologous to the family of oligo/dipeptide transporters that contain two different ATPases, and a binding protein that is synthesized with a typical bacterial signal sequence. The latter family has not been implicated in sugar transport before. These data indicate that binding protein-dependent transport is the predominant mechanism of transport for sugars in S. solfataricus.

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge

Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency