The design of a peptide sequence to inhibit HIV replication: a search algorithm combining Monte Carlo and self-consistent mean field techniques

We developed a search algorithm combining Monte Carlo (MC) and self-consistent mean field techniques to evolve a peptide sequence that has good binding capability to the anticodon stem and loop (ASL) of human lysine tRNA species, tRNA^Lys3, with the ultimate purpose of breaking the replication cycle of human immunodeficiency virus-1. The starting point is the 15-amino-acid sequence, RVTHHAFLGAHRTVG, found experimentally by Agris and co-workers to bind selectively to hypermodified tRNA^Lys3. The peptide backbone conformation is determined via atomistic simulation of the peptide-ASL^Lys3 complex and then held fixed throughout the search. The proportion of amino acids of various types (hydrophobic, polar, charged, etc.) is varied to mimic different peptide hydration properties. Three different sets of hydration properties were examined in the search algorithm to see how this affects evolution to the best-binding peptide sequences. Certain amino acids are commonly found at fixed sites for all three hydration states, some necessary for binding affinity and some necessary for binding specificity. Analysis of the binding structure and the various contributions to the binding energy shows that: 1) two hydrophilic residues (asparagine at site 11 and the cysteine at site 12) “recognize” the ASL^Lys3 due to the VDW energy, and thereby contribute to its binding specificity and 2) the positively charged arginines at sites 4 and 13 preferentially attract the negatively charged sugar rings and the phosphate linkages, and thereby contribute to the binding affinity.     

Development of a pharmacophore model for the catecholamine release-inhibitory peptide catestatin: Virtual screening and functional testing identify novel small molecule therapeutics of hypertension

The endogenous catecholamine release-inhibitory peptide catestatin (CST) regulates events leading to hypertension and cardiovascular disease. Earlier we studied the structure of CST by NMR, molecular modeling, and amino acid scanning mutagenesis. That structure has now been exploited for elucidation of interface pharmacophores that mediate binding of CST to its target, with consequent secretory inhibition. Designed pharmacophore models allowed screening of 3D structural domains. Selected compounds were tested on both cultured catecholaminergic cells and an in vivo model of hypertension; in each case, the candidates showed substantial mimicry of native CST actions, with preserved or enhanced potency and specificity. The approach and compounds have thus enabled rational design of novel drug candidates for treatment of hypertension or autonomic dysfunction.     

Ester Formation by Alcohol Acetyltransferase from Brewers’ Yeast

Alcohol acetyltransferase responsible for the formation of acetate esters during beer fermentation was found to be localized at the cell membrane of brewers’ yeast. This cell membrane-bound enzyme was purified 120-fold by solubilization with Triton X-100, gel filtration on a Sepharose 6B column and chromatography on a DEAE-Sephadex A-50 column. The enzyme was most active at 30°C at pH 7 ? 8. It was least active against C₃ alcohol among C₁ ? C₆ alcohols, and slightly more active against straight-chain alcohols than against branched-chain alcohols with the same carbon number. The enzyme was strongly inhibited by unsaturated fatty acids, heavy metal ions and sulfhydryl reagents.     

Phylogenetic plant community structure along elevation is lineage specific

The trend of closely related taxa to retain similar environmental preferences mediated by inherited traits suggests that several patterns observed at the community scale originate from longer evolutionary processes. While the effects of phylogenetic relatedness have been previously studied within a single genus or family, lineage‐specific effects on the ecological processes governing community assembly have rarely been studied for entire communities or flora. Here, we measured how community phylogenetic structure varies across a wide elevation gradient for plant lineages represented by 35 families, using a co‐occurrence index and net relatedness index (NRI). We propose a framework that analyses each lineage separately and reveals the trend of ecological assembly at tree nodes. We found prevailing phylogenetic clustering for more ancient nodes and overdispersion in more recent tree nodes. Closely related species may thus rapidly evolve new environmental tolerances to radiate into distinct communities, while older lineages likely retain inherent environmental tolerances to occupy communities in similar environments, either through efficient dispersal mechanisms or the exclusion of older lineages with more divergent environmental tolerances. Our study illustrates the importance of disentangling the patterns of community assembly among lineages to better interpret the ecological role of traits. It also sheds light on studies reporting absence of phylogenetic signal, and opens new perspectives on the analysis of niche and trait conservatism across lineages.     

Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.     

Optimization-driven identification of genetic perturbations accelerates the convergence of model parameters in ensemble modeling of metabolic networks

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study, we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an Escherichia coli metabolic model of central metabolism by successively identifying single, double, and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.     

利用表型数据构建陆地棉核心种质

以5963份陆地棉种质资源为材料,根据品种主要突变性状和品种类型分组成11组群,在分组的基础上利用21个表型性状,用非加权类平均聚类分析法,构建了281份陆地棉核心种质,占全部种质资源总量的4.71%。利用不同性状的均值t测验、方差F测验、变异系数、多样性指数t检验、均值、极差、表型方差、变异系数、均值差异百分率、方差差异百分率、极差符合率、变异系数变化率、主成分分析等参数进行核心种质代表性检验和评价。结果表明,所构建的陆地棉核心种质可以代表全部种质的遗传多样性。     

Synthesising the trait information of European Chironomidae (Insecta: Diptera): Towards a new database

Chironomidae are among the most conspicuous and ecological diverse group of freshwater invertebrates. They may dominate unimpacted communities in abundance and biomass, accounting for more than 50% of macroinvertebrate species in standing and flowing waters. In deep zones of eutrophic lakes and highly human-impacted streams, they are often the only family of aquatic insects remaining. In bioassessment programmes, Chironomids are often identified at the family and subfamily levels, due to difficulties in the taxonomic identification of larvae resulting from a high intrinsic morphological similarity. This may potentially result in bias as, similarly to Ephemeroptera, Trichoptera or Plecoptera, Chironomidae species, which are replaced along natural and human-impacted gradients due to differences in their ecological requirements. Recently, multiple trait-based approaches have been proposed to complement taxonomic-based assessment of streams and rivers using macroinvertebrates. However, the lack of specific trait information for Chironomidae prevents their use in the functional assessment of communities. Therefore, here, we aimed to: (1) develop a trait database for European Chironomidae genera that can be used in future bioassessment and ecological studies; (2) evaluate, by multivariate analyses, whether our new database provides additional information on Chironomidae compared to the trait information provided in the commonly used European trait database (Tachet et al., 2010); and (3) determine whether the new information on Eltonian traits (proxy to biological traits) translates the most accepted phylogenetic relationships among Chironomidae subfamilies. We gathered information on 744 species and 178 genera, for 37 traits covering 186 trait categories, and found substantial differences between our database and the commonly used European trait database. In addition, available information on traits was not always in agreement with phylogenetic relationships among subfamilies. Orthocladiinae and Chironominae which are considered sister groups in evolutionary terms actually showed confident trait relatedness based on Eltonian traits tree while the remaining relationships between subfamilies are questionable. In addition, different traits can occur in closely related taxa depending on the environmental drivers operating on their habitats. Our study reveals that the usually accepted redundancy within the Chironomidae family and subfamilies must be a product of averaging the information from finer taxonomic resolution added to the substantial lack of information for this insect group.