Genetic Algorithms for the Imitation of Genomic Styles in Protein Backtranslation

Several technological applications require the translation of a protein into a nucleic acid that codes for it (``backtranslation''). The degeneracy of the genetic code makes this translation ambiguous; moreover, not every translation is equally viable. The common answer to this problem is the imitation of the codon usage of the target species. Here we discuss several other features of coding sequences (``coding statistics'') that are relevant for the ``genomic style'' of different species. A genetic algorithm is then used to obtain backtranslations that mimic these styles, by minimizing the difference in the coding statistics. Possible improvements and applications are discussed.Comment: 17 pages, 13 figures. Submitted to Theor. Comp. Scienc

Dissecting the Specificity of Protein-Protein Interaction in Bacterial Two-Component Signaling: Orphans and Crosstalks

Predictive understanding of the myriads of signal transduction pathways in a cell is an outstanding challenge of systems biology. Such pathways are primarily mediated by specific but transient protein-protein interactions, which are difficult to study experimentally. In this study, we dissect the specificity of protein-protein interactions governing two-component signaling (TCS) systems ubiquitously used in bacteria. Exploiting the large number of sequenced bacterial genomes and an operon structure which packages many pairs of interacting TCS proteins together, we developed a computational approach to extract a molecular interaction code capturing the preferences of a small but critical number of directly interacting residue pairs. This code is found to reflect physical interaction mechanisms, with the strongest signal coming from charged amino acids. It is used to predict the specificity of TCS interaction: Our results compare favorably to most available experimental results, including the prediction of 7 (out of 8 known) interaction partners of orphan signaling proteins in Caulobacter crescentus. Surveying among the available bacterial genomes, our results suggest 15~25% of the TCS proteins could participate in out-of-operon "crosstalks". Additionally, we predict clusters of crosstalking candidates, expanding from the anecdotally known examples in model organisms. The tools and results presented here can be used to guide experimental studies towards a system-level understanding of two-component signaling.Comment: Supplementary information available on http://www.plosone.org/article/info:doi/10.1371/journal.pone.001972

Genetic Association Analysis of Phenotypes Jointly Influenced by a Pair of Interacting Organisms

The virulence of infectious diseases is usually affected by a combination of a host and at least one pathogen organism. Previous experiments have revealed that combining genetic information from different organisms has enabled the identification of more relevant genetic variants than just individually performing an association analysis on each organism. Hence, we are interested in performing a joint association analysis to test for the interaction effect of each possible pair of a host and pathogen genetic variant on the phenotypic trait relating to the infectious disease. Three main issues may arise when performing this joint association analysis. First, the presence of a non-trivial interaction effect between one of the genetic variants being tested and some unaccounted factor - either observed or unobserved - can lead to heteroscedasticity in the phenotypic trait. Failure to account for this heteroscedasticity may lead to overinflated type I error rates when testing for an interaction effect between this genetic variant and any genetic variant from the other organism. We compare different methods to test and account for the potential heteroscedasticity in the phenotypic trait in the case where the genotype of the pathogen organism is a binary variable. Secondly, the fact that the phenotypic trait is held fixed while the interacting genotypes vary across different interaction tests in a joint genome-wide association analysis means that the collection of interaction test statistics corresponding to a fixed pathogen genetic variant may often display a tangible departure from the known distribution of the interaction test statistic. Under the global null hypothesis of no interaction, the collection of interaction p-values corresponding to a given pathogen genetic variant might turn out to be consistently smaller than uniform, leading to a phenomenon which has been called the "feast" effect, since we end up with excess false discoveries. Similarly, the collection of interaction p-values corresponding to another fixed pathogen genetic variant might turn out to be consistently larger than uniform, leading to a phenomenon which has been called the "famine" effect, since it limits our ability to make any important discoveries. This "feast or famine" effect has been shown to result from improper conditioning in the construction of the interaction test statistic in a joint association analysis. The ordinary interaction test statistic conditions on the pair of genetic variants being tested for interaction. Instead, we take the approach of conditioning on the phenotypic trait and a fixed pathogen genetic variant in order to construct a corrected host-pathogen interaction test statistic which alleviates the feast or famine effect. We focus our efforts on the case of diploid host organisms where an appropriate discrete correction might be required to account for the binomially distributed host genotype. We present a diagnostic tool to predict the prevalence of the feast or famine effect given only the information about a phenotypic trait and a fixed pathogen genetic variant and demonstrate its relationship with the commonly used genomic control inflation factor. Lastly, accounting for population structure among patients infected with related strains of the same pathogen presents a significant challenge, owing to the presence of genetic variants with differing number of alleles within the pathogen genome. As the number of alleles in a genetic variant increases, some of the alleles may be associated with excessively small observed allele frequencies, which introduce numerical instabilities in the existing methods of constructing a pathogen genetic relatedness matrix (GRM). We build upon previous work to develop a novel pathogen GRM for organisms with multiallelic genetic variants which avoids filtering out genetic variants with exceedingly small observed allele frequencies by introducing an adjusted weighting for rare alleles. We validate the type I error control and rectification of the feast of famine effect by our correction framework through a host of simulation studies. We demonstrate the applicability of our proposed pathogen GRM and our correction framework by testing for interaction effects between human SNPs and hepatitis C viral genetic variants on pre-treatment viral load in a cohort of HCV infected patients from the BOSON clinical trial

Inter-protein sequence co-evolution predicts known physical interactions in bacterial ribosomes and the trp operon

Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data

Proton block and voltage gating are potassium-dependent in the cardiac leak channel Kcnk3.

Potassium leak conductances were recently revealed to exist as independent molecular entities. Here, the genomic structure, cardiac localization, and biophysical properties of a murine example are considered. Kcnk3 subunits have two pore-forming P domains and unique functional attributes. At steady state, Kcnk3 channels behave like open, potassium-selective, transmembrane holes that are inhibited by physiological levels of proton. With voltage steps, Kcnk3 channels open and close in two phases, one appears to be immediate and one is time-dependent (tau = approximately 5 ms). Both proton block and gating are potassium-sensitive; this produces an anomalous increase in outward flux as external potassium levels rise because of decreased proton block. Single Kcnk3 channels open across the physiological voltage range; hence they are "leak" conductances; however, they open only briefly and rarely even after exposure to agents that activate other potassium channels

Genomic Signatures of Human versus Avian Influenza A Viruses

Fifty-two species-associated amino acid residues were found between human and avian influenza viruses