A 3D model of the voltage-dependent anion channel (VDAC)

Eukaryotic porins are a group of membrane proteins whose best known role is to form an aqueous pore channel in the mitochondrial outer membrane. As opposed to the bacterial porins (a large family of protein whose 3D structure has been determined by X-ray diffraction), the structure of eukaryotic porins (also termed VDACs, voltage-dependent anion-selective channels) is still a matter of debate. We analysed the secondary structure of VDAC from the yeast Saccharomyces cerevisiae, the fungus Neurospora crassa and the mouse with different types of neural network-based predictors. The predictors were able to discriminate membrane β-strands, globular -helices and membrane -helices and localised, in all three VDAC sequences, 16 β-strands along the chain. For all three sequences the N-terminal region showed a high propensity to form a globular -helix. The 16 β-strand VDAC motif was thus aligned to a bacterial porin-derived template containing a similar 16 β-strand motif. The alignment of the VDAC sequence with the bacterial porin sequence was used to compute a set of 3D coordinates, which constitutes the first 3D prediction of a eukaryotic porin. All the predicted structures assume a β-barrel structure composed of 16 β-strands with the N-terminus outside the membrane. Loops are shorter in this side of the membrane than in the other, where two long loops are protruding. The shape of the pore varies between almost circular for Neurospora and mouse and slightly oval for yeast. Average values between 3 and 2.5 nm at the C-carbon backbone are found for the diameter of the channels. In this model VDAC shows large portions of the structure exposed on both sides of the membrane. The architecture we determine allows speculation about the mechanism of possible interactions between VDAC and other proteins on both sides of the mitochondrial outer membrane. The computed 3D model is consistent with most of the experimental results so far reported.     

Retrospective optimization of time-dependent fermentation control strategies using time-independent historical data

We have previously shown the usefulness of historical data for fermentation process optimization. The methodology developed includes identification of important process inputs, training of an artificial neural network (ANN) process model, and ultimately use of the ANN model with a genetic algorithm to find the optimal values of each critical process input. However, this approach ignores the time-dependent nature of the system, and therefore, does not fully utilize the available information within a database. In this work, we propose a method for incorporating time-dependent optimization into our previously developed three-step optimization routine. This is achieved by an additional step that uses a fermentation model (consisting of coupled ordinary differential equations (ODE)) to interpret important time-course features of the collected data through adjustments in model parameters. Important process variables not explicitly included in the model were then identified for each model parameter using automatic relevance determination (ARD) with Gaussian process (GP) models. The developed GP models were then combined with the fermentation model to form a hybrid neural network model that predicted the time-course activity of the cell and protein concentrations of novel fermentation conditions. A hybrid-genetic algorithm was then used in conjunction with the hybrid model to suggest optimal time-dependent control strategies. The presented method was implemented upon an E. coli fermentation database generated in our laboratory. Optimization of two different criteria (final protein yield and a simplified economic criteria) was attempted. While the overall protein yield was not increased using this methodology, we were successful in increasing a simplified economic criterion by 15% compared to what had been previously observed. These process conditions included using 35% less arabinose (the inducer) and 33% less typtone in the media and reducing the time required to reach the maximum protein concentration by 10% while producing approximately the same level of protein as the previous optimum.     

RET oncoproteins induce tyrosine phosphorylation changes of proteins involved in RNA metabolism

We report the identification of proteins induced in response to RET/PTC2, an oncogene implicated in thyroid cancers. Anti-phosphotyrosine antibody affinity resin was used to purify Tyr(P)-containing and interacting proteins from 293T and NIH3T3 cells which were transfected with kinase active or inactive RET/PTC and RETMEN2 oncogenes. Proteins were separated by one-dimensional SDS-PAGE, extracted by in-gel digestion, and identified by MALDI-TOF peptide mass fingerprinting. The expression and tyrosine phosphorylation of Sam68, a protein implicated in mRNA nucleocytoplasmic translocation and splicing, were further examined in RET-transfected cells and thyroid tumors. Of relevance, cells transfected with RETMEN2B examined for anti-phosphotyrosine bound proteins, showed other proteins implicated in splicing: DEAD-box p68 RNA helicase, SYNCRIP, and hnRNP K. Western blotting analysis suggested that these proteins are singularly tyrosine phosphorylated in RETMEN2B-transfected cells, and that they constitutively bind with Sam68. The study concludes that regulation of splicing factors is likely to be important in RET-mediated thyroid carcinogenesis.     

Evolution on distributive lattices

We consider the directed evolution of a population after an intervention that has significantly altered the underlying fitness landscape. We model the space of genotypes as a distributive lattice; the fitness landscape is a real-valued function on that lattice. The risk of escape from intervention, i.e., the probability that the population develops an escape mutant before extinction, is encoded in the risk polynomial. Tools from algebraic combinatorics are applied to compute the risk polynomial in terms of the fitness landscape. In an application to the development of drug resistance in HIV, we study the risk of viral escape from treatment with the protease inhibitors ritonavir and indinavir.     

Computational exploration of the activated pathways associated with DNA damage response in breast cancer

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.     

Spots and stripes: the evolution of repetition in visual signal form

It is common to find spatially repetitive patterns in animal visual signals. The evolution of such patterns is not well explained by existing theories of signal evolution. In this paper, we suggest that the evolution of signals with spatial repetition may be due to specific recognition problems and receiver biases. The logics of our hypotheses are studied in co-evolutionary simulations using artificial neural networks as models of receivers. These simulations yield repetitive visual signals under the following conditions: translations and reflections of the signal, partial obstruction of the signal, a fixed feature in the signal, and lateral inhibition in the receiver. In addition to regular repetitions our simulations sometimes result in other organisations of the signal such as blocky patterns and gradients.     

Phylogenetic Reconstruction of a Known HIV-1 CRF04_cpx Transmission Network Using Maximum Likelihood and Bayesian Methods

The CRF04_cpx strains of HIV-1 accounts for approximately 2–10% of the infected population in Greece, across different transmission risk groups. CRF04_cpx was the lineage documented in an HIV-1 transmission network in Thessalonica, northern Greece. Most of the transmissions occurred through unprotected heterosexual contacts between 1989 and 1993. Blood samples were available for six patients, obtained 6–10 years later, except for one patient sampled in 1991. Our objective was to examine whether the transmission history is compatible with the evolutionary tree of the virus, in partial gag, partial env, and partial gag+env. The inferred phylogenetic tree obtained using maximum likelihood and Bayesian methods in partial gag+env was much closer to the transmission tree than that using either env or gag separately. Our findings suggest that the epidemiological relationships among patients who have been infected by a common source correspond almost exactly to the evolutionary trees of the virus, given that enough phylogenetic signal is present in the alignment. Moreover, we found evidence that recombination is not the most parsimonious explanation for the phylogenetic incongruence between gag and env. For patients with known infection dates, the estimated dates of the coalescent events obtained using molecular clock calculations based on a newly developed Bayesian method in gag + env were in agreement with the actual infection dates.This article contains online supplementary material.Reviewing Editor: Dr. Lauren Ancel-MeyersIsolated sequences from patients belonging to the CRF04_cpx transmission network always correspond to partially characterized gag, env, and gag+env genomic regions.     

Structure—chemical approach to organization of information on metabolic charts

A nontraditional approach to construction of metabolic charts is proposed. It is based on the discovery of symmetry in the structure of the network of metabolic reactions. So if compounds and reactions are located on the metabolic chart according to their chemical features, the chart structure will acquire a periodic pattern. The charts thus created have a natural two-dimensional coordinate system of the metabolic network. Points on the X-axis correspond to number of carbon atoms in the carbon skeleton of compounds in columns and points on the Y-axis correspond to number of -COOH groups in compounds filing in series of rows on the charts. As a result this coordinate system sections the field of the charts into rectangular blocks each of which containing compounds with the same numbers of carbon atoms and the same numbers of -COOH groups. The latter substantially improves the charts and makes them a more valid source of metabolic data possessing heuristic properties. The periodicity of the metabolic network structure enables us easily to remember information about biochemical reactions and their products. The charts can also be used as a universal key for any biological database information that is systematically connected with the metabolic information. The charts can be important for medicine and pharmacology because they can help to understand the metabolic processes involved in decomposition of a particular drug or to find the chain of reactions blocked or initiated by administering this drug into a living organism.Translated from Biokhimiya, Vol. 69, No. 12, 2004, pp. 1691–1699.Original Russian Text Copyright © 2004 by Malygin.