In this paper we propose an ensemble of texture descriptors for analyzing virus textures in transmission electron microscopy images. Specifically, we present several novel multi-quinary (MQ) codings of local binary pattern (LBP) variants: the MQ version of the dense LBP, the MQ version of the rotation invariant co-occurrence among adjacent LBPs, and the MQ version of the LBP histogram Fourier. To reduce computation time as well as to improve performance, a feature selection approach is utilized to select the thresholds used in the MQ approaches. In addition, we propose new variants of descriptors where two histograms, instead of the standard one histogram, are produced for each descriptor. The two histograms (one for edge pixels and the other for non-edge pixels) are calculated for training two different SVMs, whose results are then combined by sum rule. We show that a bag of features approach works well with this problem. Our experiments, using a publicly available dataset of 1500 images with 15 classes and same protocol as in previous works, demonstrate the superiority of our new proposed ensemble of texture descriptors. The MATLAB code of our approach is available at https://www.dei.unipd.it/node/2357.