An alignment-free domain architecture similarity search (ADASS) algorithm for inferring homology between multi-domain proteins

Annotations of the genes and their products are largely guided by inferring homology. Sequence similarity is the primary measure used for annotation purpose however, the domain content and order were given less importance albeit the fact that domain insertion, deletion, positional changes can bring in functional varieties. Of late, several methods developed quantify domain architecture similarity depending on alignments of their sequences and are focused on only homologous proteins. We present an alignment-free domain architecture-similarity search (ADASS) algorithm that identifies proteins that share very poor sequence similarity yet having similar domain architectures. We introduce a “singlet matching-triplet comparison” method in ADASS, wherein triplet of domains is compared with other triplets in a pair-wise comparison of two domain architectures. Different events in the triplet comparison are scored as per a scoring scheme and an average pairwise distance score (Domain Architecture Distance score - DAD Score) is calculated between protein domains architectures. We use domain architectures of a selected domain termed as centric domain and cluster them based on DAD score. The algorithm has high Positive Prediction Value (PPV) with respect to the clustering of the sequences of selected domain architectures. A comparison of domain architecture based dendrograms using ADASS method and an existing method revealed that ADASS can classify proteins depending on the extent of domain architecture level similarity. ADASS is more relevant in cases of proteins with tiny domains having little contribution to the overall sequence similarity but contributing significantly to the overall function.     

An improved hypergeometric probability method for identification of functionally linked proteins using phylogenetic profiles

Predicting functions of proteins and alternatively spliced isoforms encoded in a genome is one of the important applications of bioinformatics in the post-genome era. Due to the practical limitation of experimental characterization of all proteins encoded in a genome using biochemical studies, bioinformatics methods provide powerful tools for function annotation and prediction. These methods also help minimize the growing sequence-to-function gap. Phylogenetic profiling is a bioinformatics approach to identify the influence of a trait across species and can be employed to infer the evolutionary history of proteins encoded in genomes. Here we propose an improved phylogenetic profile-based method which considers the co-evolution of the reference genome to derive the basic similarity measure, the background phylogeny of target genomes for profile generation and assigning weights to target genomes. The ordering of genomes and the runs of consecutive matches between the proteins were used to define phylogenetic relationships in the approach. We used Escherichia coli K12 genome as the reference genome and its 4195 proteins were used in the current analysis. We compared our approach with two existing methods and our initial results show that the predictions have outperformed two of the existing approaches. In addition, we have validated our method using a targeted protein-protein interaction network derived from protein-protein interaction database STRING. Our preliminary results indicates that improvement in function prediction can be attained by using coevolution-based similarity measures and the runs on to the same scale instead of computing them in different scales. Our method can be applied at the whole-genome level for annotating hypothetical proteins from prokaryotic genomes.     

An integrated view on the role of receptor mosaics at perisynaptic level: focus on adenosine A2A,dopamine D2, cannabinoid CB1, and metabotropic glutamate mGlu5 receptors

The available evidence for receptor–receptor interactions between adenosine A_2A, dopamine D₂, cannabinoid CB₁, and metabotropic glutamate mGlu₅ receptors (A_2A, D₂, CB₁, and mGlu₅, respectively) is revised under the “receptor mosaic” perspective. Furthermore, the concept of “hub receptor” is defined in accordance with informatics and it is tentatively illustrated in the case of the hypothesized tetramer formed by the above mentioned receptors. On the basis of some biochemical features of the four receptors and of a bioinformatics analysis, an objective deduction of their “similarity” has been obtained. To this aim the Canberra, Euclidean and Chebyshev multivariate distance metrics have been used. It is interesting to note that A_2A and D₂ are the most different ones, while CB₁ and mGlu₅ are the most similar ones among the four receptors analyzed. Finally, by means of a bioinformatics analysis based on different approaches the possible binding sites mediating G-protein-coupled receptor (GPCR) interactions have been indicated. It is interesting to note that in some instances accordance has been found between the bioinformatics indications and the available experimental data.     

基于轮廓相似度的土壤肥力时空变异性判别

采用几何轮廓相似度模型,分别对彰武县境内柳河流域所辖乡村1985年与2009年土壤肥力等级进行判别．结果表明：研究区所辖43个乡村土壤肥力普遍处于中下等水平;与1985年相比,2009年有15个乡村土壤肥力级别降低即土壤肥力变差,9个乡村土壤肥力级别升高即土壤肥力变好,19个乡村土壤肥力级别未发生变化．该模型可在缺乏多标度数据分布信息或小样本条件下难以估计统计特征的应用问题中,通过分析多标度数据的几何特征对数据进行判别;且模型采用了凹函数求平均值,从而减小了一些突变型数据对判别的干扰．因此,采用几何轮廓相似度模型判别土壤肥力时空变异性准确且简便可行．     

兰坪铅锌矿区植被恢复初期土壤种子库与地上植被关系的研究

尾矿废弃地是一种极端的生态系统,其植被恢复的研究将丰富传统的生态学理论。该研究通过野外植被调查与室内萌发实验相结合的方法,探讨了兰坪铅锌矿区植被恢复初期不同群落类型地上植被、土壤种子库及其相互关系。结果显示:（1）与对照群落（云南松林、高山栎灌丛）相比,尾矿区恢复期各群落（早熟禾人工草地、魁蒿群落、马桑灌丛）地上植被及土壤种子库的物种数、物种多样性均较低。（2）植被恢复时间较短的2个群落（魁蒿群落、人工草地）土壤种子库较地上植被物种多样性高。（3）尾矿区恢复期各群落地上植被及土壤种子库的优势种均主要由风播、种子繁殖的植物组成,菊科、禾本科占较大比例,这些植物在尾矿区植被恢复初期起重要作用。（4）尾矿区恢复期各群落土壤种子库与地上植被的物种相似性较高,各群落之间地上植被及土壤种子库的相似性则较低。研究表明,尾矿区恢复初期土壤种子库与地上植被紧密联系,群落改造方式、恢复时间对土壤种子库具有重要影响。     

A protein structural class prediction method based on novel features

In this study, a 12-dimensional feature vector is constructed to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Among the 12 features, 6 novel features are specially designed to improve the prediction accuracies for α/β and α + β classes based on the distributions of α-helices and β-strands and the characteristics of parallel β-sheets and anti-parallel β-sheets. To evaluate our method, the jackknife cross-validating test is employed on two widely-used datasets, 25PDB and 1189 datasets with sequence similarity lower than 40% and 25%, respectively. The performance of our method outperforms the recently reported methods in most cases, and the 6 newly-designed features have significant positive effect to the prediction accuracies, especially for α/β and α + β classes.     

Biogeography of parasite abundance: latitudinal gradient and distance decay of similarity in the abundance of fleas and mites,parasitic on small mammals in the Palearctic,at three spatial scales

We tested whether biogeographic patterns characteristic for biological communities can also apply to populations and investigated geographic patterns of variation in abundance of ectoparasites (fleas and mites) collected from bodies of their small mammalian hosts (rodents and shrews) in the Palearctic at continental, regional and local scales. We asked whether (i) there is a relationship between latitude and abundance and (ii) similarity in abundance follows a distance decay pattern or it is better explained by variation in extrinsic biotic and abiotic factors. We analysed the effect of latitude on mean intraspecific abundance using general linear models including proportional abundance of its principal host as an additional predictor variable. Then, we examined the relative effect of geographic distance, biotic and abiotic dissimilarities among regions, subregions or localities on the intraspecific dissimilarity in abundance among regions, subregions or localities using Generalized Dissimilarity Modelling. We found no relationship between latitude and intraspecific flea or mite abundance. In both taxa, environmental dissimilarity explained the largest part of the deviance of spatial variation in abundance, whereas the effect of the dissimilarity in the principal host abundance was of secondary importance and the effect of geographic distance was minor. These patterns were generally consistent across the three spatial scales, although environmental variation and dissimilarity in principal host abundance were equally important at the local scale in fleas but not in mites. We conclude that biogeographic patterns related to latitude and geographic distance do not apply to spatial variation of ectoparasite abundance. Instead, the geographic distribution of abundance in arthropod ectoparasites depends on their responses, mainly to the off-host environment and to a lesser extent the abundance of their principal hosts.     

Geographical and intrapopulation variation in the diet of a threatened marine predator,Pontoporia blainvillei (Cetacea)

Understanding diet variation is a major concern when developing conservation guidelines for threatened species, especially for marine predators whose prey availability can be reduced by commercial fisheries. Diet can vary in geographically structured populations due to variation in prey availability and within a location due to the effects of season, sex, age, and individual. However, these sources of variation are seldom considered together in dietary studies. We analyzed diet variation at the geographical and intrapopulation levels in the franciscana dolphin (Pontoporia blainvillei) by analyzing samples of stomach contents from individuals incidentally caught by artisanal fisheries. We investigated the geographical (Northern, Central, and Southern regions of the São Paulo State coast, Brazil) and intrapopulation effects of season, sex, and age. We used the leave‐one‐out cross‐validation method to test for significance of the proportional similarity index, which measures the overlap between diet compositions. We found that diet varied across different levels, from the geographical to the individual level, including the effects of season, sex, and age. Diet variation as a function of age suggests an ontogenetic diet shift. Our findings indicate that ecological processes within local stocks should inform management at the local geographic scale. Evidence for ecological differences between franciscana stocks is of great significance for the conservation of this threatened species.     

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease–lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.     

Assessment of structural and functional similarity of biosimilar products: Rituximab as a case study

Biosimilars are products that are similar in terms of quality, safety, and efficacy to an already licensed reference/ innovator product and are expected to offer improved affordability. The most significant source of reduction in the cost of development of a biosimilar is the reduced clinical examination that it is expected to undergo as compared to the innovator product. However, this clinical relief is predicated on the assumption that there is analytical similarity between the biosimilar and the innovator product. As a result, establishing analytical similarity is arguably the most important step towards successful development of a biosimilar. Here, we present results from an analytical similarity exercise that was performed with five biosimilars of rituximab (Ristova®, Roche), a chimeric mouse/ human monoclonal antibody biotherapeutic, that are available on the Indian market. The results show that, while the biosimilars exhibited similarity with respect to protein structure and function, there were significant differences with respect to size heterogeneity, charge heterogeneity and glycosylation pattern.