Point mutations can have a strong impact on protein stability. A change in stability may subsequently lead to dysfunction and finally cause diseases. Moreover, protein engineering approaches aim to deliberately modify protein properties, where stability is a major constraint. In order to support basic research and protein design tasks, several computational tools for predicting the change in stability upon mutations have been developed. Comparative studies have shown the usefulness but also limitations of such programs.

The major birch pollen allergen, Bet v 1, is a member of the ubiquitous PR-10 family of plant pathogenesis-related proteins. In recent years, a number of diverse plant proteins with low sequence similarity to Bet v 1 was identified. In addition, determination of the Bet v 1 structure revealed the existence of a large superfamily of structurally related proteins. In this study, we aimed to identify and classify all Bet v 1-related structures from the Protein Data Bank and all Bet v 1-related sequences from the Uniprot database.

Summary: The prediction of change in stability upon point mutations in proteins has many applications in protein analysis and engineering. We recently adjoined a new structure-based method called MAESTRO, which is distributed as command line program. We now provide access to the most important features of MAESTRO by an easy to use web service. MAESTROweb allows the prediction of change in stability for user-defined mutations, provides a scan functionality for the most (de)stabilizing n-point mutations for a maximum of n = 5, creates mutation sensitivity profiles and evaluates potential disulfide bonds. MAESTROweb operates on monomers, multimers and biological assemblies as defined by PDB.

Introduction: There is a substantial amount of evidence from animal models that early brain injury (EBI) may play an important role for secondary brain injury after aneurysmal subarachnoid hemorrhage (aSAH). Cerebral microdialysis (CMD) allows online measurement of brain metabolites, including the pro-inflammatory cytokine interleukin-6 (IL-6) and matrix metalloproteinase-9 (MMP-9), which is indicative for disruption of the blood-brain barrier. Methods: Twenty-six consecutive poor-grade aSAH patients with multimodal neuromonitoring were analyzed for brain hemodynamic and metabolic changes, including CMD-IL-6 and CMD-MMP-9 levels. Statistical analysis was performed by using a generalized estimating equation with an autoregressive function. Results: The baseline cerebral metabolic profile revealed brain metabolic distress and an excitatory response which improved over the following 5 days (P <0.001). Brain tissue hypoxia (brain tissue oxygen tension of less than 20 mm Hg) was common (more than 60% of patients) in the first 24 hours of neuromonitoring and improved thereafter (P <0.05). Baseline CMD-IL-6 and CMD-MMP-9 levels were elevated in all patients (median = 4,059 pg/mL, interquartile range (IQR) = 1,316 to 12,456 pg/mL and median = 851 pg/mL, IQR = 98 to 25,860 pg/mL) and significantly decreased over days (P <0.05). A higher pro-inflammatory response was associated with the development of delayed cerebral ischemia (P = 0.04), whereas admission disease severity and early brain tissue hypoxia were associated with higher CMD-MMP-9 levels (P <0.03). Brain metabolic distress and increased IL-6 levels were associated with poor functional outcome (modified Rankin Scale of more than 3, P ≤0.01). All models were adjusted for probe location, aneurysm securing procedure, and disease severity as appropriate. Conclusions: Multimodal neuromonitoring techniques allow insight into pathophysiologic changes in the early phase after aSAH. The results may be used as endpoints for future interventions targeting EBI in poor-grade aSAH patients.

We investigated and optimized a method for structure comparison which is based on rigid body superimposition. The method maximizes the number of structurally equivalent residues while keeping the root mean square deviation constant. The resulting number of equivalent residues then provides an adequate similarity measure, which is easy to interpret. We demonstrate that the approach is able to detect remote structural similarity. We show that the number of equivalent residues is a suitable measure for ranking database searches and that the results are in good agreement with expert knowledge protein structure classification. Structure comparison frequently has multiple solutions. The approach that we use provides a range of alternative alignments rather a single solution. We discuss the nature of alternative solutions on several examples.