Improving Classical Substructure-Based Virtual Screening to Handle Extrapolation Challenges

Target-oriented substructure-based virtual screening (sSBVS) of molecules is a promising approach in drug discovery. Yet, there are doubts whether sSBVS is suitable also for extrapolation, that is, for detecting molecules that are very different from those used for training. Herein, we evaluate the predictive power of classic virtual screening methods, namely, similarity searching using Tanimoto coefficient (MTC) and Naive Bayes (NB). As could be expected, these classic methods perform better in interpolation than in extrapolation tasks. Consequently, to enhance the predictive ability for extrapolation tasks, we introduce the Shadow approach, in which inclusion relations between substructures are considered, as opposed to the classic sSBVS methods that assume independence between substructures. Specifically, we discard contributions from substructures included in (“shaded” by) others which are, in turn, included in the molecule of interest. Indeed, the Shadow classifier significantly outperforms both MTC (<i>pValue</i> = 3.1 × 10^–16) and NB (<i>pValue</i> = 3.5 × 10^–9) in detecting hits sharing low similarity with the training active molecules

miRNA different regulation between genes according to 3’UTR shortening.

<p>Two genes, Gene 1 and Gene 2 contain a binding site for same miRNA and are hence potentially subject to its regulation. In one physiological condition <b>(A)</b> the two genes feature mostly the long 3’ UTR, and as the binding site is close to its center, the miRNA can exert little to none of its regulatory effect on the two genes. Upon switch to the second condition <b>(B)</b>, Expression of the miRNA is induced. In that condition, Gene 1 undergoes 3’ UTR shortening and its binding site now becomes closer to the UTR’s end, while Gene 2 remains unmodified. Hence, Gene 1, but not Gene 2, now becomes fully accessible to repression by the induced miRNA, and the levels of its short transcript, although upregulated by the shortening, remain low because of the effective targeting of the miRNA. In this way selective 3’ UTR shortening may serve as a dynamic means to differentiate between different targets of the same miRNA, providing the network with additional regulatory flexibility.</p

3’UTR Shortening Potentiates MicroRNA-Based Repression of Pro-differentiation Genes in Proliferating Human Cells

<div><p>Most mammalian genes often feature alternative polyadenylation (APA) sites and hence diverse 3’UTR lengths. Proliferating cells were reported to favor APA sites that result in shorter 3’UTRs. One consequence of such shortening is escape of mRNAs from targeting by microRNAs (miRNAs) whose binding sites are eliminated. Such a mechanism might provide proliferation-related genes with an expression gain during normal or cancerous proliferation. Notably, miRNA sites tend to be more active when located near both ends of the 3’UTR compared to those located more centrally. Accordingly, miRNA sites located near the center of the full 3’UTR might become more active upon 3'UTR shortening. To address this conjecture we performed 3' sequencing to determine the 3' ends of all human UTRs in several cell lines. Remarkably, we found that conserved miRNA binding sites are preferentially enriched immediately upstream to APA sites, and this enrichment is more prominent in pro-differentiation/anti-proliferative genes. Binding sites of the miR17-92 cluster, upregulated in rapidly proliferating cells, are particularly enriched just upstream to APA sites, presumably conferring stronger inhibitory activity upon shortening. Thus 3’UTR shortening appears not only to enable escape from inhibition of growth promoting genes but also to potentiate repression of anti-proliferative genes.</p></div

miRNA binding sites located 5’ to APA sites are probably selected for miRNA targeting.

<p><b>(A)</b> Conservation profile of 30 bases around conserved miRNA binding sites which are located in the 300 bases 5’ to APA sites. Two conservation scoring systems are displayed—PhastCons and PyhloP. <b>(B,C,D)</b> P_CT conservation scores (B) and Context++ scores (C,D) of miRNA binding sites located 300 bases 5’ or 3’ (before and after) APA sites, for genes with at least 500 nucleotides from each side of the APA site. For the context++ scores the miRNA binding sites are divided to conserved (C) and non-conserved (D).</p

miRNAs and genes enriched for conserved binding sites upstream to APA sites.

<p><b>(A)</b> Conserved and non-conserved miRNA binding sites for genes with APA site and at least 1000 3’ UTR bases around it are divided in different groups according to codon usage correlation. <b>(B)</b> Heat map representing the mean fold change (log2 scale) of each miRNA in the miRNA array experiment from the control (primary cells) sample. <b>(C)</b> Conserved binding sites around the APA site for miRNAs in the miR-17-92 cluster and for all miRNAs. Only genes with at least 1000 3’ UTR bases before and after the APA site were considered for the analysis. <b>(D)</b> Conserved binding sites upstream the long 3’UTR end for miRNAs in the miR-17-92 cluster and for all miRNAs. Only genes with at least 1000 bases 5’ to the long 3’UTR and with APA site were considered for the analysis. * indicates p-value<0.05 for the difference between conserved miRNA binding sites between Pro-Diff. and Pro-Prolif. Genes (A), or between all miRNAs and miRNAs from the miR-17-92 cluster (C,D).</p