A structural model for the chromophore-binding domain of ovine rhodopsin

A putative model for the structure of the relatively independent carboxyl-terminal domain of (rhod)opsin has been developed by use of a combination of several secondary structure prediction methods. The validity of this approach was confirmed by comparing the secondary structure for bacteriorhodopsin as predicted by these methods with its known low resolution structure. The resulting predicted structure agreed well with the experimental data. The model obtained for opsin incorporates two transmembrane α-helical rods linked by an intradiscal loop. Each of the helical sections is interrupted by a short irregular region. One of these includes the lysyl residue to which the chromophore 11-cis retinal is attached. The second non-regular segment, almost opposite the first, contains a cysteinyl and a tryptophanyl residue which may be involved in protein—chromophore interaction. The proposed structure of this whole domain could prove instructive in the elucidation of the primary events of visual transduction.     

Predicting the unexpected: using a qualitative model of a New Zealand dryland ecosystem to anticipate pest management outcomes

Pest management is expensive and there is often uncertainty about the benefits for the resources being protected. There can also be unintended consequences for other parts of the ecosystem, especially in complex food webs. In making decisions managers generally have to rely on qualitative information collected in a piecemeal fashion. A method to assist decision making is a qualitative modelling approach using fuzzy cognitive maps, a directed graphical model related to neural networks that can take account of interactions between pests and conservation assets in complex food webs. Using all available information on relationships between native and exotic resources and consumers, we generated hypotheses about potential consequences of single‐species and multi‐species pest control on the long‐term equilibrium abundances of other biotic components of an ecosystem. We applied the model to a dryland ecosystem in New Zealand because we had good information on its trophic structure, but the information on the strength of species interactions was imprecise. Our model suggested that pest control is unlikely to significantly boost native invertebrates and lizards in this ecosystem, suggesting that other forms of management may be required for these groups. Most of the pest control regimes tested resulted in greater abundances of at least one other pest species, which could potentially lead to other management problems. Some of the predictions were unexpected, such as more birds resulting from possum and mouse control. We also modelled the effects of an increase in invasive rabbits, which led to unexpected declines of stoats, weasels, mice and possums. These unexpected outcomes resulted from complex indirect pathways in the food web. Fuzzy cognitive maps allow rapid construction of prototype models of complex food webs using a wide range of data and expert opinion. Their utility lies in providing direction for future monitoring efforts and generating hypotheses that can be tested with field experiments.     

PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

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Testing prediction accuracy in short-term ecological studies

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.     

Computational Modeling of Protein Stability: Quantitative Analysis Reveals Solutions to Pervasive Problems

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Kinase–substrate Edge Biomarkers Provide A More Accurate Prognostic Prediction in ER-negative Breast Cancer

The estrogen receptor (ER)-negative breast cancer subtype is aggressive with few treatment options available. To identify specific prognostic factors for ER-negative breast cancer, this study included 705,729 and 1034 breast invasive cancer patients from the Surveillance, Epidemiology, and End Results (SEER) and The Cancer Genome Atlas (TCGA) databases, respectively. To identify key differential kinase–substrate node and edge biomarkers between ER-negative and ER-positive breast cancer patients, we adopted a network-based method using correlation coefficients between molecular pairs in the kinase regulatory network. Integrated analysis of the clinical and molecular data revealed the significant prognostic power of kinase–substrate node and edge features for both subtypes of breast cancer. Two promising kinase–substrate edge features, CSNK1A1–NFATC3 and SRC–OCLN, were identified for more accurate prognostic prediction in ER-negative breast cancer patients.     

LNRLMI: Linear neighbour representation for predicting lncRNA‐miRNA interactions

LncRNA and miRNA are key molecules in mechanism of competing endogenous RNAs(ceRNA), and their interactions have been discovered with important roles in gene regulation. As supplementary to the identification of lncRNA‐miRNA interactions from CLIP‐seq experiments, in silico prediction can select the most potential candidates for experimental validation. Although developing computational tool for predicting lncRNA‐miRNA interaction is of great importance for deciphering the ceRNA mechanism, little effort has been made towards this direction. In this paper, we propose an approach based on linear neighbour representation to predict lncRNA‐miRNA interactions (LNRLMI). Specifically, we first constructed a bipartite network by combining the known interaction network and similarities based on expression profiles of lncRNAs and miRNAs. Based on such a data integration, linear neighbour representation method was introduced to construct a prediction model. To evaluate the prediction performance of the proposed model, k‐fold cross validations were implemented. As a result, LNRLMI yielded the average AUCs of 0.8475 ± 0.0032, 0.8960 ± 0.0015 and 0.9069 ± 0.0014 on 2‐fold, 5‐fold and 10‐fold cross validation, respectively. A series of comparison experiments with other methods were also conducted, and the results showed that our method was feasible and effective to predict lncRNA‐miRNA interactions via a combination of different types of useful side information. It is anticipated that LNRLMI could be a useful tool for predicting non‐coding RNA regulation network that lncRNA and miRNA are involved in.