Accurate and efficient gp120 V3 loop structure based models for the determination of HIV-1 co-receptor usage

<p>Abstract</p> <p>Background</p> <p>HIV-1 targets human cells expressing both the CD4 receptor, which binds the viral envelope glycoprotein gp120, as well as either the CCR5 (R5) or CXCR4 (X4) co-receptors, which interact primarily with the third hypervariable loop (V3 loop) of gp120. Determination of HIV-1 affinity for either the R5 or X4 co-receptor on host cells facilitates the inclusion of co-receptor antagonists as a part of patient treatment strategies. A dataset of 1193 distinct gp120 V3 loop peptide sequences (989 R5-utilizing, 204 X4-capable) is utilized to train predictive classifiers based on implementations of random forest, support vector machine, boosted decision tree, and neural network machine learning algorithms. An <it>in silico </it>mutagenesis procedure employing multibody statistical potentials, computational geometry, and threading of variant V3 sequences onto an experimental structure, is used to generate a feature vector representation for each variant whose components measure environmental perturbations at corresponding structural positions.</p> <p>Results</p> <p>Classifier performance is evaluated based on stratified 10-fold cross-validation, stratified dataset splits (2/3 training, 1/3 validation), and leave-one-out cross-validation. Best reported values of sensitivity (85%), specificity (100%), and precision (98%) for predicting X4-capable HIV-1 virus, overall accuracy (97%), Matthew's correlation coefficient (89%), balanced error rate (0.08), and ROC area (0.97) all reach critical thresholds, suggesting that the models outperform six other state-of-the-art methods and come closer to competing with phenotype assays.</p> <p>Conclusions</p> <p>The trained classifiers provide instantaneous and reliable predictions regarding HIV-1 co-receptor usage, requiring only translated V3 loop genotypes as input. Furthermore, the novelty of these computational mutagenesis based predictor attributes distinguishes the models as orthogonal and complementary to previous methods that utilize sequence, structure, and/or evolutionary information. The classifiers are available online at <url>http://proteins.gmu.edu/automute</url>.</p

Women’s preferences for men’s facial masculinity: Trade-off accounts revisited

Studies on mate preferences have demonstrated that women’s perception of male attractiveness is sensitive to men’s facial masculinity, and that women’s preferences for facial masculinity are subject to individual differences, such as own condition. These individual differences have been linked to potential trade-offs that women face given the hypothesized benefits and costs that masculinity may cue in a potential partner. Whereas most studies based conclusions regarding such trade-offs on shifts in mean preferences for a feminized vs. masculinized face shape, here we directly investigated attractiveness as a function of different levels of masculinity. Using computer-graphic methods, we manipulated the facial masculinity of men’s 3D faces to vary between extremely feminine and hypermasculine, and assessed women’s preferences for these different masculinity levels in the light of individual differences in self-rated attractiveness, financial worries, pathogen disgust sensitivity, self-reported health and relationship status. Our findings show that some individual differences shift preferences towards a generally lower or higher masculinity level, whereas others affect the tolerance to low vs. high levels of masculinity. We suggest that the use of preference curves allows for a more comprehensive investigation of how and why women’s preferences for masculinity may shift under different contexts