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Background: High-altitude populations exhibit distinct cellular, respiratory, and cardiovascular phenotypes, some of which provide adaptive advantages to hypoxic conditions compared to populations with sea-level ancestry. Studies performed in populations with a history of high-altitude residence, such as Tibetans, support the idea that many of these phenotypes may be shaped by genomic features that have been positively selected for throughout generations. We hypothesize that such traits observed in Tibetans at high altitude also occur in Tibetans living at intermediate altitude, even in the absence of severe sustained hypoxia. Methodology: We studied individuals of high-altitude ancestry (Tibetans, n = 17 females; n = 12 males) and sea-level ancestry (Han Chinese, n = 6 females; n = 10 males), both who had been living at ~1300m (~4327 ft) for at least 18 months. We measured hemoglobin concentration ([Hb]), hypoxic ventilatory response (HVR), and hypoxic heart rate response (HHRR) with end-tidal CO2 (PETCO2) held constant (isocapnia) or allowed to decrease with hypoxic hyperventilation (poikilocapnia). We also quantified the contribution of CO2 on ventilation and heart rate by calculating the differences of isocapnic versus poikilocapnic hypoxic conditions (Δ &lowbar;VI/ΔPETCO2 and ΔHR/ΔPETCO2, respectively). Results: Male Tibetans had lower [Hb] compared to Han Chinese males (p < 0.05), consistent with reports for individuals from these populations living at high altitude and sea level. Measurements of ventilation (resting ventilation, HVR, and PETCO2) were similar for both groups. Heart rate responses to hypoxia were similar in both groups during isocapnia; however, HHRR in poikilocapnia was reduced in the Tibetan group (p < 0.03), and the heart rate response to CO2 in hypoxia was lower in Tibetans relative to Han Chinese (p < 0.01). Conclusion: These results suggest that Tibetans living at intermediate altitude have blunted cardiac responses in the context of hypoxia. Hence, only some of the phenotypes observed in Tibetans living at high altitude are observed in Tibetans living at intermediate altitude. Whereas blunted cardiac responses to hypoxia is revealed at intermediate altitudes, manifestation of other physiological adaptations to high altitude may require exposure to more severe levels of hypoxia. [ABSTRACT FROM AUTHOR]

Abstract Background The global pattern of distribution of 1033 B. anthracis isolates has previously been defined by a set of 12 conserved canonical single nucleotide polymorphisms (canSNP). These studies reinforced the presence of three major lineages and 12 sub-lineages and sub-groups of this anthrax-causing pathogen. Isolates that form the A lineage (unlike the B and C lineages) have become widely dispersed throughout the world and form the basis for the geographical disposition of "modern" anthrax. An archival collection of 191 different B. anthracis isolates from China provides a glimpse into the possible role of Chinese trade and commerce in the spread of certain sub-lineages of this pathogen. Canonical single nucleotide polymorphism (canSNP) and multiple locus VNTR analysis (MLVA) typing has been used to examine this archival collection of isolates. Results The canSNP study indicates that there are 5 different sub-lineages/sub-groups in China out of 12 previously described world-wide canSNP genotypes. Three of these canSNP genotypes were only found in the western-most province of China, Xinjiang. These genotypes were A.Br.008/009, a sub-group that is spread across most of Europe and Asia; A.Br.Aust 94, a sub-lineage that is present in Europe and India, and A.Br.Vollum, a lineage that is also present in Europe. The remaining two canSNP genotypes are spread across the whole of China and belong to sub-group A.Br.001/002 and the A.Br.Ames sub-lineage, two closely related genotypes. MLVA typing adds resolution to the isolates in each canSNP genotype and diversity indices for the A.Br.008/009 and A.Br.001/002 sub-groups suggest that these represent older and established clades in China. Conclusion B. anthracis isolates were recovered from three canSNP sub-groups (A.Br.008/009, A.Br.Aust94, and A.Br.Vollum) in the western most portion of the large Chinese province of Xinjiang. The city of Kashi in this province appears to have served as a crossroads for not only trade but the movement of diseases such as anthrax along the ancient "silk road". Phylogenetic inference also suggests that the A.Br.Ames sub-lineage, first identified in the original Ames strain isolated from Jim Hogg County, TX, is descended from the A.Br.001/002 sub-group that has a major presence in most of China. These results suggest a genetic discontinuity between the younger Ames sub-lineage in Texas and the large Western North American sub-lineage spread across central Canada and the Dakotas.

Abstract Background Structure-based computational methods are needed to help identify and characterize protein-protein complexes and their function. For individual proteins, the most successful technique is homology modelling. We investigate a simple extension of this technique to protein-protein complexes. We consider a large set of complexes of known structures, involving pairs of single-domain proteins. The complexes are compared with each other to establish their sequence and structural similarities and the relation between the two. Compared to earlier studies, a simpler dataset, a simpler structural alignment procedure, and an additional energy criterion are used. Next, we compare the Xray structures to models obtained by threading the native sequence onto other, homologous complexes. An elementary requirement for a successful energy function is to rank the native structure above any threaded structure. We use the DFIREβ energy function, whose quality and complexity are typical of the models used today. Finally, we compare near-native models to distinctly non-native models. Results If weakly stable complexes are excluded (defined by a binding energy cutoff), as well as a few unusual complexes, a simple homology principle holds: complexes that share more than 35% sequence identity share similar structures and interaction modes; this principle was less clearcut in earlier studies. The energy function was then tested for its ability to identify experimental structures among sets of decoys, produced by a simple threading procedure. On average, the experimental structure is ranked above 92% of the alternate structures. Thus, discrimination of the native structure is good but not perfect. The discrimination of near-native structures is fair. Typically, a single, alternate, non-native binding mode exists that has a native-like energy. Some of the associated failures may correspond to genuine, alternate binding modes and/or native complexes that are artefacts of the crystal environment. In other cases, additional model filtering with more sophisticated tools is needed. Conclusion The results suggest that the simple modelling procedure applied here could help identify and characterize protein-protein complexes. The next step is to apply it on a genomic scale.

Abstract Background Protein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent. We continue to evaluate the performance of the Coulomb/Accessible Surface Area (CASA) implicit solvent model, in combination with the Charmm19 molecular mechanics force field. We test a set of model parameters optimized earlier, and we also carry out a new optimization in this work, using as a target a set of experimental stability changes for single point mutations of various proteins and peptides. The optimization procedure is general, and could be used with other force fields. The computation of stability changes requires a model for the unfolded state of the protein. In our approach, this state is represented by tripeptide structures of the sequence Ala-X-Ala for each amino acid type X. We followed an iterative optimization scheme which, at each cycle, optimizes the solvation parameters and a set of tripeptide structures for the unfolded state. This protocol uses a set of 140 experimental stability mutations and a large set of tripeptide conformations to find the best tripeptide structures and solvation parameters. Results Using the optimized parameters, we obtain a mean unsigned error of 2.28 kcal/mol for the stability mutations. The performance of the CASA model is assessed by two further applications: (i) calculation of protein-ligand binding affinities and (ii) computational protein design. For these two applications, the previous parameters and the ones optimized here give a similar performance. For ligand binding, we obtain reasonable agreement with a set of 55 experimental mutation data, with a mean unsigned error of 1.76 kcal/mol with the new parameters and 1.47 kcal/mol with the earlier ones. We show that the optimized CASA model is not inferior to the Generalized Born/Surface Area (GB/SA) model for the prediction of these binding affinities. Likewise, the new parameters perform well for the design of 8 SH3 domain proteins where an average of 32.8% sequence identity relative to the native sequences was achieved. Further, it was shown that the computed sequences have the character of naturally-occuring homologues of the native sequences. Conclusion Overall, the two CASA variants explored here perform very well for a wide variety of applications. Both variants provide an efficient solvent treatment for the computational engineering of ligands and proteins.

Abstract Background In structural genomics, an important goal is the detection and classification of protein–protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein–protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue–residue interactions and a stepwise distance-dependence. We used increased computational ressources, however, constructing 290,000 decoys for 219 protein–protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment. Results Performance is similar to several other statistical potentials of the same complexity. For example, the CAPRI target structure is correctly ranked ahead of 90% of its decoys in 6 cases out of 13. The hierarchy of amino acid alphabets leads to a coherent hierarchy of energy functions, with qualitatively similar parameters for similar amino acid types at all levels. Most remarkably, the performance with six amino acid classes is equivalent to that of the most detailed, 20-class energy function. Conclusion This suggests that six carefully chosen amino acid classes are sufficient to encode specificity in protein–protein interactions, and provide a starting point to develop more complicated energy functions.