Predicting Important Residues and Interaction Pathways in Proteins Using Gaussian Network Model: Binding and Stability of HLA Proteins

A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed

CD4 receptor diversity in chimpanzees protects against SIV infection

Human and simian immunodeficiency viruses (HIV/SIV) use CD4 as the primary receptor to enter target cells. Here, we show that the chimpanzee CD4 is highly polymorphic, with nine coding variants present in wild populations, and that this diversity interferes with SIV envelope (Env)-CD4 interactions. Testing the replication fitness of SIVcpz strains in CD4+ T cells from captive chimpanzees, we found that certain viruses were unable to infect cells from certain hosts. These differences were recapitulated in CD4 transfection assays, which revealed a strong association between CD4 genotypes and SIVcpz infection phenotypes. The most striking differences were observed for three substitutions (Q25R, Q40R, P68T), with P68T generating a second N-linked glycosylation site (N66) in addition to an invariant N32 encoded by all chimpanzee CD4 alleles. In-silico modeling and site-directed mutagenesis identified charged residues at the CD4-Env interface and clashes between CD4- and Env-encoded glycans as mechanisms of inhibition. CD4 polymorphisms also reduced Env-mediated cell entry of monkey SIVs, which was dependent on at least one D1 domain glycan. CD4 allele frequencies varied among wild chimpanzees, with high diversity in all but the western subspecies, which appeared to have undergone a selective sweep. One allele was associated with lower SIVcpz prevalence rates in the wild. These results indicate that substitutions in the D1 domain of the chimpanzee CD4 can prevent SIV cell entry. Although some SIVcpz strains have adapted to utilize these variants, CD4 diversity is maintained to protect chimpanzees against infection with SIVcpz and other SIVs to which they are exposed

Diagnosis of cirrus cloud occurrence using large-scale analysis data and a cloud-scale model

International audienceThe development of cirrus clouds is governed by large-scale synoptic movements such as updraft regions in convergence zones, but also by smaller scale features, for instance microphysical phenomena, entrainment, small-scale turbulence and radiative field, fall-out of the ice phase or wind shear. For this reason, the proper handling of cirrus life cycles is not an easy task using a large-scale model alone. We present some results from a small-scale cirrus cloud model initialized by ECMWF first-guess data, which prove more convenient for this task than the analyzed ones. This model is Starr’s 2-D cirrus cloud model, where the rate of ice production/destruction is parametrized from environmental data. Comparison with satellite and local observations during the ICE89 experiment (North Sea) shows that such an efficient model using large-scale data as input provides a reasonable diagnosis of cirrus occurrence in a given meteorological field. The main driving features are the updraft provided by the large-scale model, which enhances or inhibits the cloud development according to its sign, and the water vapour availability. The cloud fields retrieved are compared to satellite imagery. Finally, the use of a small-scale model in large-scale numerical studies is examined

Diagnosis of cirrus cloud occurrence using large-scale analysis data and a cloud-scale model

The development of cirrus clouds is governed by large-scale synoptic movements such as updraft regions in convergence zones, but also by smaller scale features, for instance microphysical phenomena, entrainment, small-scale turbulence and radiative field, fall-out of the ice phase or wind shear. For this reason, the proper handling of cirrus life cycles is not an easy task using a large-scale model alone. We present some results from a small-scale cirrus cloud model initialized by ECMWF first-guess data, which prove more convenient for this task than the analyzed ones. This model is Starr's 2-D cirrus cloud model, where the rate of ice production/destruction is parametrized from environmental data. Comparison with satellite and local observations during the ICE89 experiment (North Sea) shows that such an efficient model using large-scale data as input provides a reasonable diagnosis of cirrus occurrence in a given meteorological field. The main driving features are the updraft provided by the large-scale model, which enhances or inhibits the cloud development according to its sign, and the water vapour availability. The cloud fields retrieved are compared to satellite imagery. Finally, the use of a small-scale model in large-scale numerical studies is examined