Prediction of GPCR-G protein coupling specificity using features of sequences and biological functions

Understanding the coupling specificity between G protein-coupled receptors （GPCRs） and specific classes of G proteins is important for further elucidation of receptor functions within a cell. Increasing information on GPCR sequences and the G protein family would facilitate prediction of the coupling properties of GPCRs. In this study, we describe a novel approach for predicting the coupling specificity between GPCRs and G proteins. This method uses not only GPCR sequences but also the functional knowledge generated by natural language processing, and can achieve 92.2% prediction accuracy by using the C4.5 algorithm. Furthermore, rules related to GPCR-G protein coupling are generated. The combination of sequence analysis and text mining improves the prediction accuracy for GPCR-G protein coupling specificity, and also provides clues for understanding GPCR signaling.     

In silico analyses of neuropeptide-like protein (NLP) profiles in parasitic nematodes

Nematode parasite infections cause disease in humans and animals and threaten global food security by reducing productivity in livestock and crop farming. The escalation of anthelmintic resistance in economically important nematode parasites underscores the need for the identification of novel drug targets in these worms. Nematode neuropeptide signalling is an attractive system for chemotherapeutic exploitation, with neuropeptide G-protein coupled receptors (NP-GPCRs) representing the lead targets. In order to successfully validate NP-GPCRs for parasite control it is necessary to characterise their function and importance to nematode biology. This can be aided through identification of receptor activating ligand(s) via deorphanisation. Such efforts require the identification of all neuropeptide ligands within parasites. Here we mined the genomes of nine therapeutically relevant pathogenic nematodes to characterise the neuropeptide-like protein complements and demonstrate that: (i) parasitic nematodes possess a reduced complement of neuropeptide-like protein-encoding genes relative to Caenorhabditis elegans; (ii) parasite neuropeptide-like protein profiles are broadly conserved between nematode clades; (iii) five Ce-nlps are completely conserved across the nematode species examined; (iv) the extent and position of neuropeptide-like protein-motif conservation is variable; (v) novel RPamide-encoding genes are present in parasitic nematodes; (vi) novel Allatostatin-C-like peptide encoding genes are present in both C. elegans and parasitic nematodes; (vii) novel neuropeptide-like protein families are absent in C. elegans; and (viii) highly conserved nematode neuropeptide-like proteins are bioactive. These data highlight the complexity of nematode neuropeptide-like proteins and reveal the need for nomenclature revision in this diverse neuropeptide family. The identification of neuropeptide-like protein ligands, and characterisation of those with functional relevance, advance our understanding of neuropeptide signalling to support exploitation of the neuropeptidergic system as an anthelmintic target.     

A text-mining perspective on the requirements for electronically annotated abstracts

We propose that the combination of human expertise and automatic text-mining systems can be used to create a first generation of electronically annotated information (EAI) that can be added to journal abstracts and that is directly related to the information in the corresponding text. The first experiments have concentrated on the annotation of gene/protein names and those of organisms, as these are the best resolved problems. A second generation of systems could then attempt to address the problems of annotating protein interactions and protein/gene functions, a more difficult task for text-mining systems. EAI will permit easier categorization of this information, it will help in the evaluation of papers for their curation in databases, and it will be invaluable for maintaining the links between the information in databases and the facts described in text. Additionally, it will contribute to the efforts towards completing database information and creating collections of annotated text that can be used to train new generations of text-mining systems. The recent introduction of the first meta-server for the annotation of biological text, with the possibility of collecting annotations from available text-mining systems, adds credibility to the technical feasibility of this proposal.     

Physico-chemical principles of cAMP-dependent protein phosphorylation. Catalysis of phosphoryl group transfer to nucleophilic agents

The specificity of pig brain protein kinase towards high- and low-Mr 'analogs' of protein substrate was studied. Solvolysis of the phosphoenzyme intermediate by various nucleophilic agents is shown. The transition state structure of the phosphoryl transfer reaction is discussed.     

Data reconciliation and parameter estimation in flux-balance analysis

Flux blance analysis (FBA) has been shown to be a very effective tool to interpret and predict the metabolism of various microorganisms when the set of available measurements is not sufficient to determine the fluxes within the cell. In this methodology, an underdetermined stoichiometric model is solved using a linear programming (LP) approach. The predictions of FBA models can be improved if noisy measurements are checked for consistency, and these in turn are used to estimate model parameters. In this work, a formal methodology for data reconciliation and parameter estimation with underdetermined stoichiometric models is developed and assessed. The procedure is formulated as a nonlinear optimization problem, where the LP is transformed into a set of nonlinear constraints. However, some of these constraints violate standard regularity conditions, making the direct numerical solution very difficult. Hence, a barrier formulation is used to represent these constraints, and an iterative procedure is defined that allows solving the problem to the desired degree of convergence. This methodology is assessed using a stoichiometric yeast model. The procedure is used for data reconciliation where more reliable estimations of noisy measurements are computed. On the other hand, assuming unknown biomass composition, the procedure is applied for simultaneous data reconciliation and biomass composition estimation. In both cases it is verified that the f measurements required to get unbiased and reliable estimations is reduced if the LP approach is included as additional constraints in the optimization.     

Hairpins in bookstacks: information retrieval from biomedical text

Current advances in high-throughput biology are accompanied by a tremendous increase in the number of related publications. Much biomedical information is reported in the vast amount of literature. The ability to rapidly and effectively survey the literature is necessary for both the design and the interpretation of large-scale experiments, and for curation of structured biomedical knowledge in public databases. Given the millions of published documents, the field of information retrieval, which is concerned with the automatic identification of relevant documents from large text collections, has much to offer. This paper introduces the basics of information retrieval, discusses its applications in biomedicine, and presents traditional and non-traditional ways in which it can be used.     

毛果杨 NLP 基因家族生物信息学分析与鉴定简

NLP基因家族是一类特殊的转录因子,豆科植物根瘤的形成依赖于该基因家族的存在,在非豆科植物中具有调节植物硝酸盐吸收以及同化的功能。通过对毛果杨（Populus trichocarpa）基因组的生物信息学分析,共鉴定出14个毛果杨NLP基因家族成员,这些成员具有低亲水性的特点,基因结构保守,都含有RWP-RK以及PB1两个保守结构域。通过细胞定位预测,所有成员都定位在细胞核中。直系同源与旁系同源进化分析显示,NLP基因家族成员在漫长的进化过程中经历了严格的选择。染色体定位分析表明,毛果杨NLP基因家族成员坐落在毛果杨9条染色体之上,成员数量的扩增来自于杨柳科染色体自身的扩增事件。芯片数据分析结果显示,NLP基因家族成员在嫩叶,根和雄花中表达,部分基因在木质部以及种子萌发过程之中表达,但所有成员均不在成熟叶片中表达。     

The NIN‐like protein 5 (ZmNLP5) transcription factor is involved in modulating the nitrogen response in maize

Maize exhibits marked growth and yield response to supplemental nitrogen (N). Here, we report the functional characterization of a maize NIN‐like protein ZmNLP5 as a central hub in a molecular network associated with N metabolism. Predominantly expressed and accumulated in roots and vascular tissues, ZmNLP5 was shown to rapidly respond to nitrate treatment. Under limited N supply, compared with that of wild‐type (WT) seedlings, the zmnlp5 mutant seedlings accumulated less nitrate and nitrite in the root tissues and ammonium in the shoot tissues. The zmnlp5 mutant plants accumulated less nitrogen than the WT plants in the ear leaves and seed kernels. Furthermore, the mutants carrying the transgenic ZmNLP5 cDNA fragment significantly increased the nitrate content in the root tissues compared with that of the zmnlp5 mutants. In the zmnlp5 mutant plants, loss of the ZmNLP5 function led to changes in expression for a significant number of genes involved in N signalling and metabolism. We further show that ZmNLP5 directly regulates the expression of nitrite reductase 1.1 (ZmNIR1.1) by binding to the nitrate‐responsive cis‐element at the 5′ UTR of the gene. Interestingly, a natural loss‐of‐function allele of ZmNLP5 in Mo17 conferred less N accumulation in the ear leaves and seed kernels resembling that of the zmnlp5 mutant plants. Our findings show that ZmNLP5 is involved in mediating the plant response to N in maize.