Structure-Function Prediction of Insect Odorant Binding Proteins

This study concerns the application of bioinformatic tools for the elucidation of the biological function of insect general odorant and pheromone binding proteins (GOBPs / PBPs). These proteins are thought to function as transporters of volatile odorant molecules to olfactory receptors (ORs) situated in olfactory receptor neurons (ORNs) in insect antennae. Activation of ORNs by the odorant molecules gives rise to action potentials resulting in spatially defined patterns of glomerular activity in the brain, odour discrimination and concomitant behavioural response of the insect. The extent to which OBPs are critical for olfactory discrimination remains unclear. Numerous hypotheses have been postulated regarding the ability of OBPs to discriminate specific odorants and/or pheromones as well as their playing a role in the activation of odorant-responsive chemosensory neurons, in functioning as selective filters in odour recognition or participating in signal termination by inactivating odorant molecules. In silico binding studies of ligands and pheromones on OBPs derived from crystallographic studies or de novo homology modelling have been conducted primarily by docking and molecular dynamic (MD) simulations. It is shown that results obtained from such studies can provide useful insights and testable hypotheses with regard to the biochemical function of OBPs. Docking and MD simulations corroborate experimental evidence that the B. mori general odorant binding protein (BmorGOBP2) has considerably higher affinity than the B. mori pheromone binding protein (BmorPBPI) for the pheromonal components bombykol and bombykal and predict that this is also true for the modelled M. sexta proteins (MsexGOBP2 and MsexPBP1). In addition, steered molecular dynamics (SMD) simulations predict ligand entry and exit pathways into and out of BmorGOBP2. In addition, docking and molecular dynamics (MD) simulations with the highly homologous odorant binding proteins from A. gambiae (AgamOBP1), A. aegypti (AaegOBP1) and C. quinquefasciatus (CguiOBP1) provide evidence of differential capacity of these proteins to select ligands with specific structural characteristics

New insights on repellent recognition by <i>Anopheles gambiae</i> odorant-binding protein 1

It is generally recognized that insect odorant binding proteins (OBPs) mediate the solubilisation and transport of hydrophobic odorant molecules and contribute to the sensitivity of the insect olfactory system. However, the exact mechanism by which OBPs deliver odorants to olfactory receptors and their role, if any, as selectivity filters for specific odorants, are still a matter of debate. In the case of Anopheles gambiae recent studies indicate that ligand discrimination is effected through the formation of heterodimers such as AgamOBP1 and AgamOBP4 (odorant binding proteins 1 and 4 from Anopheles gambiae). Furthermore, AgamOBPs have been reported to be promiscuous in binding more than one ligand simultaneously and repellents such as DEET (N,N-diethyl-3-toluamide) and 6-MH (6-methyl-5-hepten-2-one) interact directly with mosquito OBPs and/or compete for the binding of attractive odorants thus disrupting OBP heterodimerisation. In this paper, we propose mechanisms of action of DEET and 6-MH. We also predict that ligand binding can occur in several locations of AgamOBP1 with partial occupancies and propose structural features appropriate for repellent pharmacophores

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Regulation of biotechnologies in LDCs : implications for technology development and transfer

The diffusion of biotechnology in most countries of the developing world is likely to take the form of embedded technologies (transgenic seed). In some countries with an incipient science and technology capacity diffusion is likely to take place via technological spin-offs and the commercial activities of multinational enterprises. Although regulation plays a critical role in technology transfer, there is lack of capacity to exercise regulatory oversight effectively. The result of this is likely to be loss of public confidence in the technology with consequent implications for technology transfer

A Comparative Evaluation of the Structural and Dynamic Properties of Insect Odorant Binding Proteins

Insects devote a major part of their metabolic resources to the production of odorant binding proteins (OBPs). Although initially, these proteins were implicated in the solubilisation, binding and transport of semiochemicals to olfactory receptors, it is now recognised that they may play diverse, as yet uncharacterised, roles in insect physiology. The structures of these OBPs, the majority of which are known as &ldquo;classical&rdquo; OBPs, have shed some light on their potential functional roles. However, the dynamic properties of these proteins have received little attention despite their functional importance. Structural dynamics are encoded in the native protein fold and enable the adaptation of proteins to substrate binding. This paper provides a comparative review of the structural and dynamic properties of OBPs, making use of sequence/structure analysis, statistical and theoretical physics-based methods. It provides a new layer of information and additional methodological tools useful in unravelling the relationship between structure, dynamics and function of insect OBPs. The dynamic properties of OBPs, studied by means of elastic network models, reflect the similarities/dissimilarities observed in their respective structures and provides insights regarding protein motions that may have important implications for ligand recognition and binding. Furthermore, it was shown that the OBPs studied in this paper share conserved structural &lsquo;core&rsquo; that may be of evolutionary and functional importance

Genetically Modified Organisms : A Guide to Biosafety

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Thermodynamic cycle for the calculation of the binding free energies between chains A and B of AgamOBP1 in the gas phase, , and in solution .

<p>The solvation free energies of chain A, chain B, and of the dimer are , , and , respectively. For the DEET and 6-MH complexes, chain A = chain A + ligand and chain B = chain B + ligand.</p

Identifying mouse developmental essential genes using machine learning

The genes that are required for organismal survival are annotated as ‘essential genes’. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies on mouse development, we developed a supervised machine learning classifier based on phenotype data from mouse knockout experiments. We used this classifier to predict the essentiality of mouse genes lacking experimental data. Validation of our predictions against a blind test set of recent mouse knockout experimental data indicated a high level of accuracy (>80%). We also validated our predictions for other mouse mutagenesis methodologies, demonstrating that the predictions are accurate for lethal phenotypes isolated in random chemical mutagenesis screens and embryonic stem cell screens. The biological functions that are enriched in essential and non-essential genes have been identified, showing that essential genes tend to encode intracellular proteins that interact with nucleic acids. The genome distribution of predicted essential and non-essential genes was analysed, demonstrating that the density of essential genes varies throughout the genome. A comparison with human essential and non-essential genes was performed, revealing conservation between human and mouse gene essentiality status. Our genome-wide predictions of mouse essential genes will be of value for the planning of mouse knockout experiments and phenotyping assays, for understanding the functional processes required during mouse development, and for the prioritisation of disease candidate genes identified in human genome and exome sequence datasets

pH-induced conformational changes of AgamOBP1 (cyan depicts the conformation at pH5).

<p>pH-induced conformational changes of AgamOBP1 (cyan depicts the conformation at pH5).</p