Pioneer transcription factors initiate cell-fate changes by binding to silent target genes. They are among the first factors to bind key regulatory sites and facilitate chromatin opening. Here, we identify an additional role for pioneer factors. In early Caenorhabditis elegans foregut development, the pioneer factor PHA-4/FoxA binds promoters and recruits RNA polymerase II (Pol II), often in a poised configuration in which Pol II accumulates near transcription start sites. At a later developmental stage, PHA-4 promotes chromatin opening. We found many more genes with poised RNA polymerase than had been observed previously in unstaged embryos, revealing that early embryos accumulate poised Pol II and that poising is dynamic. Our results suggest that Pol II recruitment, in addition to chromatin opening, is an important feature of PHA-4 pioneer factor activity.

Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for side effects of approved drugs or candidates, as well as de-orphans phenotypic hits. For the rapid identification of protein-ligand interactions, we here present a novel chemogenomics algorithm for the prediction of protein-ligand interactions using a new machine learning approach and novel class of descriptor. The algorithm applies Bayesian Additive Regression Trees (BART) on a newly proposed proteochemical space, termed the bow-pharmacological space. The space spans three distinctive sub-spaces that cover the protein space, the ligand space, and the interaction space. Thereby, the model extends the scope of classical target prediction or chemogenomic modelling that relies on one or two of these subspaces. Our model demonstrated excellent prediction power, reaching accuracies of up to 94.5–98.4% when evaluated on four human target datasets constituting enzymes, nuclear receptors, ion channels, and G-protein-coupled receptors . BART provided a reliable probabilistic description of the likelihood of interaction between proteins and ligands, which can be used in the prioritization of assays to be performed in both discovery and vigilance phases of small molecule development.

Deep neural networks (DNNs) hold promise for functional genomics prediction, but their generalization capability may be limited by the amount of available data. To address this, we propose EvoAug, a suite of evolution-inspired augmentations that enhance the training of genomic DNNs by increasing genetic variation. Random transformation of DNA sequences can potentially alter their function in unknown ways, so we employ a fine-tuning procedure using the original non-transformed data to preserve functional integrity. Our results demonstrate that EvoAug substantially improves the generalization and interpretability of established DNNs across prominent regulatory genomics prediction tasks, offering a robust solution for genomic DNNs.