Does One Size Fit All? Drug Resistance and Standard Treatments: Results of Six Tuberculosis Programmes in Former Soviet Countries.

SETTING: After the collapse of the Soviet Union, countries in the region faced a dramatic increase in tuberculosis cases and the emergence of drug resistance. OBJECTIVE: To discuss the relevance of the DOTS strategy in settings with a high prevalence of drug resistance. DESIGN: Retrospective analysis of one-year treatment outcomes of short-course chemotherapy (SCC) and results of drug susceptibility testing (DST) surveys of six programmes located in the former Soviet Union: Kemerovo prison, Russia; Abkhasia, Georgia; Nagorno-Karabagh, Azerbaijan; Karakalpakstan, Uzbekistan; Dashoguz Velayat, Turkmenistan; and South Kazakhstan Oblast, Kazakhstan. Results are reported for new and previously treated smear-positive patients. RESULTS: Treatment outcomes of 3090 patients and DST results of 1383 patients were collected. Treatment success rates ranged between 87% and 61%, in Nagorno-Karabagh and Kemerovo, respectively, and failure rates between 7% and 23%. Any drug resistance ranged between 66% and 31% in the same programmes. MDR rates ranged between 28% in Karakalpakstan and Kemerovo prison and 4% in Nagorno-Karabagh. CONCLUSION: These results show the limits of SCC in settings with a high prevalence of drug resistance. They demonstrate that adapting treatment according to resistance patterns, access to reliable culture, DST and good quality second-line drugs are necessary

Direct visualization reveals dynamics of a transient intermediate during protein assembly

Interactions between proteins underlie numerous biological functions. Theoretical work suggests that protein interactions initiate with formation of transient intermediates that subsequently relax to specific, stable complexes. However, the nature and roles of these transient intermediates have remained elusive. Here, we characterized the global structure, dynamics, and stability of a transient, on-pathway intermediate during complex assembly between the Signal Recognition Particle (SRP) and its receptor. We show that this intermediate has overlapping but distinct interaction interfaces from that of the final complex, and it is stabilized by long-range electrostatic interactions. A wide distribution of conformations is explored by the intermediate; this distribution becomes more restricted in the final complex and is further regulated by the cargo of SRP. These results suggest a funnel-shaped energy landscape for protein interactions, and they provide a framework for understanding the role of transient intermediates in protein assembly and biological regulation

Specificity quantification of biomolecular recognition and its implication for drug discovery

Highly efficient and specific biomolecular recognition requires both affinity and specificity. Previous quantitative descriptions of biomolecular recognition were mostly driven by improving the affinity prediction, but lack of quantification of specificity. We developed a novel method SPA (SPecificity and Affinity) based on our funneled energy landscape theory. The strategy is to simultaneously optimize the quantified specificity of the “native” protein-ligand complex discriminating against “non-native” binding modes and the affinity prediction. The benchmark testing of SPA shows the best performance against 16 other popular scoring functions in industry and academia on both prediction of binding affinity and “native” binding pose. For the target COX-2 of nonsteroidal anti-inflammatory drugs, SPA successfully discriminates the drugs from the diversity set, and the selective drugs from non-selective drugs. The remarkable performance demonstrates that SPA has significant potential applications in identifying lead compounds for drug discovery

Evaluation of multiple protein docking structures using correctly predicted pairwise subunits

<p>Abstract</p> <p>Background</p> <p>Many functionally important proteins in a cell form complexes with multiple chains. Therefore, computational prediction of multiple protein complexes is an important task in bioinformatics. In the development of multiple protein docking methods, it is important to establish a metric for evaluating prediction results in a reasonable and practical fashion. However, since there are only few works done in developing methods for multiple protein docking, there is no study that investigates how accurate structural models of multiple protein complexes should be to allow scientists to gain biological insights.</p> <p>Methods</p> <p>We generated a series of predicted models (decoys) of various accuracies by our multiple protein docking pipeline, Multi-LZerD, for three multi-chain complexes with 3, 4, and 6 chains. We analyzed the decoys in terms of the number of correctly predicted pair conformations in the decoys.</p> <p>Results and conclusion</p> <p>We found that pairs of chains with the correct mutual orientation exist even in the decoys with a large overall root mean square deviation (RMSD) to the native. Therefore, in addition to a global structure similarity measure, such as the global RMSD, the quality of models for multiple chain complexes can be better evaluated by using the local measurement, the number of chain pairs with correct mutual orientation. We termed the fraction of correctly predicted pairs (RMSD at the interface of less than 4.0Å) as <it>fpair </it>and propose to use it for evaluation of the accuracy of multiple protein docking.</p

Theory and simulation of short-range models of globular protein solutions

We report theoretical and simulation studies of phase coexistence in model globular protein solutions, based on short-range, central, pair potential representations of the interaction among macro-particles. After reviewing our previous investigations of hard-core Yukawa and generalised Lennard-Jones potentials, we report more recent results obtained within a DLVO-like description of lysozyme solutions in water and added salt. We show that a one-parameter fit of this model based on Static Light Scattering and Self-Interaction Chromatography data in the dilute protein regime, yields demixing and crystallization curves in good agreement with experimental protein-rich/protein-poor and solubility envelopes. The dependence of cloud and solubility points temperature of the model on the ionic strength is also investigated. Our findings highlight the minimal assumptions on the properties of the microscopic interaction sufficient for a satisfactory reproduction of the phase diagram topology of globular protein solutions.Comment: 17 pages, 8 figures, Proc. of Conference "Structural Arrest Transitions in Colloidal Systems with Short-Range Attractions", Messina (ITALY) 17-20 December 200

Role of the Subunits Interactions in the Conformational Transitions in Adult Human Hemoglobin: an Explicit Solvent Molecular Dynamics Study

Hemoglobin exhibits allosteric structural changes upon ligand binding due to the dynamic interactions between the ligand binding sites, the amino acids residues and some other solutes present under physiological conditions. In the present study, the dynamical and quaternary structural changes occurring in two unligated (deoxy-) T structures, and two fully ligated (oxy-) R, R2 structures of adult human hemoglobin were investigated with molecular dynamics. It is shown that, in the sub-microsecond time scale, there is no marked difference in the global dynamics of the amino acids residues in both the oxy- and the deoxy- forms of the individual structures. In addition, the R, R2 are relatively stable and do not present quaternary conformational changes within the time scale of our simulations while the T structure is dynamically more flexible and exhibited the T\rightarrow R quaternary conformational transition, which is propagated by the relative rotation of the residues at the {\alpha}1{\beta}2 and {\alpha}2{\beta}1 interface.Comment: Reprinted (adapted) with permission from J. Phys. Chem. B DOI:10.1021/jp3022908. Copyright (2012) American Chemical Societ

The eNMR platform for structural biology

The e-NMR project is a European cooperation initiative that aims at providing the bio-NMR user community with a software platform integrating and streamlining the computational approaches necessary for the analysis of bio-NMR data. The e-NMR platform is based on a Grid computational infrastructure. A main focus of the current implementation of the e-NMR platform is on streamlining structure determination protocols. Indeed, to facilitate the use of NMR spectroscopy in the life sciences, the eNMR consortium has set out to provide protocolized services through easy-to-use web interfaces, while still retaining sufficient flexibility to handle specific requests by expert users. Various programs relevant for structural biology applications are already available through the e-NMR portal, including HADDOCK, XPLOR-NIH, CYANA and csRosetta. The implementation of these services, and in particular the distribution of calculations to the GRID infrastructure, has required the development of specific tools. However, the GRID infrastructure is maintained completely transparent to the users. With more than 150 registered users, eNMR is currently the second largest European Virtual Organization in the life sciences

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.Comment: 34 pages, 7 figure

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors