Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.     

The accuracy of absolute differential abundance analysis from relative count data

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. For example, in the differential abundance setting, compositional effects in relative abundance data may give rise to spurious differences (false positives) when considered from the absolute perspective. In practice however, relative abundances are often transformed by renormalization strategies intended to compensate for these effects and the scope of the practical problem remains unclear. We used simulated data to explore the consistency of differential abundance calling on renormalized relative abundances versus absolute abundances and find that, while overall consistency is high, with a median sensitivity (true positive rates) of 0.91 and specificity (1—false positive rates) of 0.89, consistency can be much lower where there is widespread change in the abundance of features across conditions. We confirm these findings on a large number of real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, where data sets with the greatest change between experimental conditions are also those with the highest false positive rates. Finally, we evaluate the predictive utility of summary features of relative abundance data themselves. Estimates of sparsity and the prevalence of feature-level change in relative abundance data give reasonable predictions of discrepancy in differential abundance calling in simulated data and can provide useful bounds for worst-case outcomes in real data.     

A topological data analytic approach for discovering biophysical signatures in protein dynamics

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.     

GnRH-II analogs for selective activation and inhibition of non-mammalian and type-II mammalian GnRH receptors

Recently, we identified three types of non-mammalian gonadotropin-releasing hormone receptors (GnRHR) in the bullfrog (designated bfGnRHR-1-3), and a mammalian type-II GnRHR in green monkey cell lines (denoted gmGnRHR-2). All these receptors responded better to GnRH-II than GnRH-I, while mammalian type-I GnRHR showed greater sensitivity to GnRH-I than GnRH-II. In the present study, we designed new GnRH-II analogs and examined whether they activated or inhibited non-mammalian and mammalian type-II GnRHRs. [D-Ala6]GnRH-II, with D-Ala substituted for Gly6 in GnRH-II, increased inositol phosphate (IP) production in cells stably expressing non-mammalian GnRHRs more effectively than native GnRH-II. However, it exhibited lower activity for mammalian type-I GnRHR than GnRH-I itself. Trptorelix-1, a GnRH-II antagonist, inhibited GnRH-induced IP production in cells expressing non-mammalian GnRHRs more effectively than Cetrorelix, a GnRH-I antagonist. Trptorelix-1, however, had lower potency for mammalian type-I GnRHR than Cetrorelix. Ligand-receptor binding assays revealed that [D-Ala6]GnRH-II and Trptorelix-1 have higher affinities for non-mammalian GnRHRs but lower affinities for mammalian type-I GnRHR than GnRH-II and Cetrorelix, respectively. Moreover, [D-Ala6]GnRH-II and Trptorelix-1 had a higher affinity for gmGnRHR-2 than GnRH-II and Cetrorelix, respectively. These results indicate that [D-Ala6]GnRH-II and Trptorelix-1 are highly effective agonist and antagonist, respectively, for non-mammalian and type-II mammalian GnRHRs.     

Differential desensitization and internalization of three different bullfrog gonadotropin-releasing hormone receptors

We previously demonstrated the presence of three distinct types of the gonadotropin-releasing hormone receptor (GnRHR) in a bullfrog (denoted bfGnRHR-1, bfGnRHR-2, and bfGnRHR-3). The bfGnRHRs exhibited differential tissue distribution and ligand selectivity. In the present study, we demonstrated the desensitization and internalization kinetics of these receptors in both transiently-transfected HEK293 cells and retrovirus-mediated stable cells. The time-course accumulation of the inositol phosphate in response to GnRH revealed that bfGnRHR-1 and -2 were rapidly desensitized, whereas bfGnRHR-3 was slowly desensitized. A comparison of the internalization kinetics revealed the most rapid rate and highest extent of internalization of bfGnRHR-2 among the three receptors. Interestingly, the mechanisms that underlie the receptor internalization appear to differ from each other. Internalization of bfGnRHR-1 was dependent on both dynamin and beta-arrestin, whereas those of bfGnRHR-2 and -3 were dependent on dynamin, but not on arrestin. These results, therefore, suggest that differential regulatory mechanisms for desensitization and internalization of the GnRHR are involved in diverse cellular and physiological responses to GnRH stimulation.     

Not Just an Oil Slick: How the Energetics of Protein-Membrane Interactions Impacts the Function and Organization of Transmembrane Proteins

The membrane environment, its composition, dynamics, and remodeling, have been shown to participate in the function and organization of a wide variety of transmembrane (TM) proteins, making it necessary to study the molecular mechanisms of such proteins in the context of their membrane settings. We review some recent conceptual advances enabling such studies, and corresponding computational models and tools designed to facilitate the concerted experimental and computational investigation of protein-membrane interactions. To connect productively with the high resolution achieved by cognate experimental approaches, the computational methods must offer quantitative data at an atomistically detailed level. We show how such a quantitative method illuminated the mechanistic importance of a structural characteristic of multihelical TM proteins, that is, the likely presence of adjacent polar and hydrophobic residues at the protein-membrane interface. Such adjacency can preclude the complete alleviation of the well-known hydrophobic mismatch between TM proteins and the surrounding membrane, giving rise to an energy cost of residual hydrophobic mismatch. The energy cost and biophysical formulation of hydrophobic mismatch and residual hydrophobic mismatch are reviewed in the context of their mechanistic role in the function of prototypical members of multihelical TM protein families: 1), LeuT, a bacterial homolog of mammalian neurotransmitter sodium symporters; and 2), rhodopsin and the β1- and β2-adrenergic receptors from the G-protein coupled receptor family. The type of computational analysis provided by these examples is poised to translate the rapidly growing structural data for the many TM protein families that are of great importance to cell function into ever more incisive insights into mechanisms driven by protein-ligand and protein-protein interactions in the membrane environment.     

Not Just an Oil Slick: How the Energetics of Protein-Membrane Interactions Impacts the Function and Organization of Transmembrane Proteins

The membrane environment, its composition, dynamics, and remodeling, have been shown to participate in the function and organization of a wide variety of transmembrane (TM) proteins, making it necessary to study the molecular mechanisms of such proteins in the context of their membrane settings. We review some recent conceptual advances enabling such studies, and corresponding computational models and tools designed to facilitate the concerted experimental and computational investigation of protein-membrane interactions. To connect productively with the high resolution achieved by cognate experimental approaches, the computational methods must offer quantitative data at an atomistically detailed level. We show how such a quantitative method illuminated the mechanistic importance of a structural characteristic of multihelical TM proteins, that is, the likely presence of adjacent polar and hydrophobic residues at the protein-membrane interface. Such adjacency can preclude the complete alleviation of the well-known hydrophobic mismatch between TM proteins and the surrounding membrane, giving rise to an energy cost of residual hydrophobic mismatch. The energy cost and biophysical formulation of hydrophobic mismatch and residual hydrophobic mismatch are reviewed in the context of their mechanistic role in the function of prototypical members of multihelical TM protein families: 1), LeuT, a bacterial homolog of mammalian neurotransmitter sodium symporters; and 2), rhodopsin and the β1- and β2-adrenergic receptors from the G-protein coupled receptor family. The type of computational analysis provided by these examples is poised to translate the rapidly growing structural data for the many TM protein families that are of great importance to cell function into ever more incisive insights into mechanisms driven by protein-ligand and protein-protein interactions in the membrane environment.     

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.