Local protein structure prediction using discriminative models

BACKGROUND: In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results. We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates. The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information. RESULTS: The step of clustering local structures yields an average RMSD quantization error of 1.19 Å for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88. CONCLUSION: The described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods

Interactive and computational methods for molecular modelling applied to the bacterial ribosome

Rechnerische Methoden zur effektiven Modellierung von großen Biomolekülen wurden entwickelt und dann auf die Struktur des bakteriellen Ribosoms angewendet. Basierend auf der im Englischen molekulardynamisch (molecular dynamics) genannten Technik wurden Algorithmen entworfen, um definierte Gruppen von Atomen zu starren Clustern zusammenzufassen; dadurch läßt sich die Anzahl der Parameter stark vermindern und damit der Rechenprozeß beschleunigen (Clustered Molecular Dynamics (CMD)). Die im molekulardynamischen Ansatz zur Beschreibung der strukturellen Details von Partikeln verwendete Energiepotentialfunktion wurde um zwei Terme erweitert. Diese berücksichtigen gröbere biochemische Strukturzusammenhänge, die sich aus Cross-linking Techniken und aus der Cryo-Elektronen-Mikroskopie ergeben. Grob- und detailstrukturelle Eigenschaften der Potentialfunktion wurden spezifiziert und die CMD Technik wurde in den interaktiven Modellbildungsprozeß einbezogen um ein Strukturmodell des bakteriellen Ribosoms zu generieren. Dieses weist einen hohen Plausibilitätsgrad auf, da es den zugrundegelegtenA computational multi-scale modelling approach for the refinement of large biomolecules was designed and then applied to the structure of the bacterial ribosome. Algorithms were developed, allowing defined groups of atoms to be clustered into rigid objects, which greatly reduces the number of parameters in the molecular dynamics approach and thus speeds up the computational process considerably (clustered molecular dynamics). The energy potential function, which is used in molecular dynamics to describe structural details of a particle, was extended to include terms that describe high-level biochemical constraints resulting from cross-linking techniques and cryo-electron microscopy. High- and low-level features of the potential function were specified, and the clustered molecular dynamics technique was integrated into the interactive model building process, t

Structural Descriptors of gp120 V3 Loop for the Prediction of HIV-1 Coreceptor Usage

HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists. Predictive methods for inferring coreceptor usage based on the third hypervariable (V3) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as the 11/25 rule) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively. Here, we show, based on a recently resolved structure of gp120 with an untruncated V3 loop, that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage. In particular, we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance. For a fixed specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favorably with the sensitivities of 0.62 for the traditional 11/25 rule and 0.73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement. A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4. Furthermore, an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor. The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning

Dung‐visiting beetle diversity is mainly affected by land use, while community specialization is driven by climate

Dung beetles are important actors in the self‐regulation of ecosystems by driving nutrient cycling, bioturbation, and pest suppression. Urbanization and the sprawl of agricultural areas, however, destroy natural habitats and may threaten dung beetle diversity. In addition, climate change may cause shifts in geographical distribution and community composition. We used a space‐for‐time approach to test the effects of land use and climate on α‐diversity, local community specialization (H (2)′) on dung resources, and γ‐diversity of dung‐visiting beetles. For this, we used pitfall traps baited with four different dung types at 115 study sites, distributed over a spatial extent of 300 km × 300 km and 1000 m in elevation. Study sites were established in four local land‐use types: forests, grasslands, arable sites, and settlements, embedded in near‐natural, agricultural, or urban landscapes. Our results show that abundance and species density of dung‐visiting beetles were negatively affected by agricultural land use at both spatial scales, whereas γ‐diversity at the local scale was negatively affected by settlements and on a landscape scale equally by agricultural and urban land use. Increasing precipitation diminished dung‐visiting beetle abundance, and higher temperatures reduced community specialization on dung types and γ‐diversity. These results indicate that intensive land use and high temperatures may cause a loss in dung‐visiting beetle diversity and alter community networks. A decrease in dung‐visiting beetle diversity may disturb decomposition processes at both local and landscape scales and alter ecosystem functioning, which may lead to drastic ecological and economic damage

Local Function Conservation in Sequence and Structure Space

We assess the variability of protein function in protein sequence and structure space. Various regions in this space exhibit considerable difference in the local conservation of molecular function. We analyze and capture local function conservation by means of logistic curves. Based on this analysis, we propose a method for predicting molecular function of a query protein with known structure but unknown function. The prediction method is rigorously assessed and compared with a previously published function predictor. Furthermore, we apply the method to 500 functionally unannotated PDB structures and discuss selected examples. The proposed approach provides a simple yet consistent statistical model for the complex relations between protein sequence, structure, and function. The GOdot method is available online (http://godot.bioinf.mpi-inf.mpg.de)

Physiopathologie de l'intoxication au monoxyde de carbone (données épidémiologiques du CAP de Bordeaux)

BORDEAUX2-BU Santé (330632101) / SudocSudocFranceF

Alignment of Non-Covalent Interactions at Protein- Protein Interfaces

Background: The study and comparison of protein-protein interfaces is essential for the understanding of the mechanisms of interaction between proteins. While there are many methods for comparing protein structures and protein binding sites, so far no methods have been reported for comparing the geometry of non-covalent interactions occurring at proteinprotein interfaces. Methodology/Principal Findings: Here we present a method for aligning non-covalent interactions between different protein-protein interfaces. The method aligns the vector representations of van der Waals interactions and hydrogen bonds based on their geometry. The method has been applied to a dataset which comprises a variety of protein-protein interfaces. The alignments are consistent to a large extent with the results obtained using two other complementary approaches. In addition, we apply the method to three examples of protein mimicry. The method successfully aligns respective interfaces and allows for recognizing conserved interface regions. Conclusions/Significance: The Galinter method has been validated in the comparison of interfaces in which homologous subunits are involved, including cases of mimicry. The method is also applicable to comparing interfaces involving nonpeptidic compounds. Galinter assists users in identifying local interface regions with similar patterns of non-covalen