Kinetic Rate Constant Prediction Supports the Conformational Selection Mechanism of Protein Binding

The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies

Axenic amastigote cultivation and in vitro development of Leishmania orientalis

Leishmania (Mundinia) orientalis is a recently described new species that causes leishmaniasis in Thailand. To facilitate characterization of this new species, an in vitro culture system to generate L. orientalis axenic amastigotes was developed. In vitro culture conditions of the axenic culture-derived amastigotes were optimized by manipulation of temperature and pH. Four criteria were used to evaluate the resulting L. orientalis axenic amastigotes, i.e., morphology, zymographic analysis of nucleases, cyclic transformation, and infectivity to the human monocytic cell line (THP-1) cells. Results revealed that the best culture condition for L. orientalis axenic amastigotes was Grace's insect medium supplemented with FCS 20%, 2% human urine, 1% BME vitamins, and 25 μg/ml gentamicin sulfate, pH 5.5 at 35 °C. For promastigotes, the condition was M199 medium, 10% FCS supplemented with 2% human urine, 1% BME vitamins, and 25 μg/ml gentamicin sulfate, pH 6.8 at 26 °C. Morphological characterization revealed six main stages of the parasites including amastigotes, procyclic promastigotes, nectomonad promastigotes, leptomonad promastigotes, metacyclic promastigotes, and paramastigotes. Also, changes in morphology during the cycle were accompanied by changes in zymographic profiles of nucleases. The developmental cycle of L. orientalis in vitro was complete in 12 days using both culture systems. The infectivity to THP-1 macrophages and intracellular growth of the axenic amastigotes was similar to that of THP-1 derived intracellular amastigotes. These results confirmed the successful axenic cultivation of L. orientalis amastigotes. The axenic amastigotes and promastigotes can be used for further study on infection in permissive vectors and animals

A predicted three-dimensional structure for the carcinoembryonic antigen (CEA)

AbstractA three-dimensional model for the carcinoembryonic antigen (CEA) has been constructed by knowledge-based computer modelling. Each of the seven extracellular domains of CEA are expected to have immunoglobulin folds. The N-terminal domain of CEA was modelled using the first domain of the recently solved NMR structure or rat CD2, as well as the first domain of the X-ray crystal structure of human CD4 and an immunoglobulin variable domain REI as templates. The remaining domains were modelled from the first and second domains or CD4 and REI. Link conformations between the domains were taken from the elbow region of antibodies. A possible packing model between each of the seven domains is proposed. Each residue of the model is labelled as to its suitability for site-directed mutagenesis

Leishmania Manipulation of Sand Fly Feeding Behavior Results in Enhanced Transmission

In nature the prevalence of Leishmania infection in whole sand fly populations can be very low (<0.1%), even in areas of endemicity and high transmission. It has long since been assumed that the protozoan parasite Leishmania can manipulate the feeding behavior of its sand fly vector, thus enhancing transmission efficiency, but neither the way in which it does so nor the mechanisms behind such manipulation have been described. A key feature of parasite development in the sand fly gut is the secretion of a gel-like plug composed of filamentous proteophosphoglycan. Using both experimental and natural parasite–sand fly combinations we show that secretion of this gel is accompanied by differentiation of mammal-infective transmission stages. Further, Leishmania infection specifically causes an increase in vector biting persistence on mice (re-feeding after interruption) and also promotes feeding on multiple hosts. Both of these aspects of vector behavior were found to be finely tuned to the differentiation of parasite transmission stages in the sand fly gut. By experimentally accelerating the development rate of the parasites, we showed that Leishmania can optimize its transmission by inducing increased biting persistence only when infective stages are present. This crucial adaptive manipulation resulted in enhanced infection of experimental hosts. Thus, we demonstrate that behavioral manipulation of the infected vector provides a selective advantage to the parasite by significantly increasing transmission

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection

<p>Abstract</p> <p>Background</p> <p>Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection.</p> <p>Results</p> <p>In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed.</p> <p>Conclusion</p> <p>This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA.</p

Endogenizing Syndromes

African and African American StudiesGovernmen

Cluster analysis of networks generated through homology: automatic identification of important protein communities involved in cancer metastasis

BACKGROUND: Protein-protein interactions have traditionally been studied on a small scale, using classical biochemical methods to investigate the proteins of interest. More recently large-scale methods, such as two-hybrid screens, have been utilised to survey extensive portions of genomes. Current high-throughput approaches have a relatively high rate of errors, whereas in-depth biochemical studies are too expensive and time-consuming to be practical for extensive studies. As a result, there are gaps in our knowledge of many key biological networks, for which computational approaches are particularly suitable. RESULTS: We constructed networks, or 'interactomes', of putative protein-protein interactions in the rat proteome – the rat being an organism extensively used for cancer studies. This was achieved by integrating experimental protein-protein interaction data from many species and translating this data into the reference frame of the rat. The putative rat protein interactions were given confidence scores based on their homology to proteins that have been experimentally observed to interact. The confidence score was furthermore weighted according to the extent of the experimental evidence, giving a higher weight to more frequently observed interactions. The scoring function was subsequently validated and networks constructed around key proteins, identified as being highly up- or down-regulated in rat cell lines of high metastatic potential. Using clustering methods on the networks, we have identified key protein communities involved in cancer metastasis. CONCLUSION: The protein network generation and subsequent network analysis used here, were shown to be useful for highlighting key proteins involved in metastasis. This approach, in conjunction with microarray expression data, can be extended to other species, thereby suggesting possible pathways around proteins of interest