Protein–ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions. Here we describe a flexible machine learning method, called ProBound, that accurately defines sequence recognition in terms of equilibrium binding constants or kinetic rates. This is achieved using a multi-layered maximum-likelihood framework that models both the molecular interactions and the data generation process. We show that ProBound quantifies transcription factor (TF) behavior with models that predict binding affinity over a range exceeding that of previous resources; captures the impact of DNA modifications and conformational flexibility of multi-TF complexes; and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with an assay called KD-seq, it determines the absolute affinity of protein–ligand interactions. We also apply ProBound to profile the kinetics of kinase–substrate interactions. ProBound opens new avenues for decoding biological networks and rationally engineering protein–ligand interactions.

Polycomb group (PcG) proteins are conserved epigenetic regulators that are linked to cancer in humans. However, little is known about how they control cell proliferation. Here, we report that mutant clones of the PcG gene polyhomeotic (ph) form unique single-cell-layer cavities that secrete three JAK/STAT pathway ligands, which in turn act redundantly to stimulate overproliferation of surrounding wild-type cells. Notably, different ph alleles cause different phenotypes at the cellular level. Although the ph-null allele induces non-autonomous overgrowth, an allele encoding truncated Ph induces both autonomous and non-autonomous overgrowth. We propose that PcG misregulation promotes tumorigenesis through several cellular mechanisms.

Highlights d DNA shape readout by homeodomain TF complexes is revealed using SELEX-seq assays d A shape readout-impaired homeodomain mutant destabilizes specific TF complexes d This mutant was exploited to map TF complex composition and function genome wide d The same mutation in an orthologous homeodomain is associated with a human disease

Liver regeneration is a complex process that involves a multitude of cellular functions, including primarily cell proliferation, apoptosis, inflammation, and metabolism. A number of signaling pathways that control these processes have been identified, and cross communication between them by direct protein-protein interactions has been shown to be crucial in orchestrating liver regeneration. Previously, we have identified a group of transcription factors capable of regulating liver cell growth and that may be involved in liver cancer development. The expression of some of their mouse counterpart genes was altered dramatically after liver injury and regeneration induced by CCl(4) in mice. In an effort to elucidate the molecular basis for liver regeneration through protein-protein interactions (PPI), a matrix mating Y2H approach was produced to generate a PPI network between a set of 32 regulatory proteins. Sixty-four interactions were identified, including 4 that had been identified previously. Ten of the interactions were further confirmed with GST pull-down and coimmunoprecipitation assays. Information provided by this PPI network may shed further light on the molecular mechanisms that regulate liver regeneration at the protein interaction level and ultimately identify regulatory factors that may serve as candidate drug targets for the treatment of liver diseases.

We describe a simple and efficient technique that allows scarless engineering of Drosophila genomic sequences near any landing site containing an inverted attP cassette, such as a MiMIC insertion. This 2-step method combines phiC31 integrase mediated site-specific integration and homing nuclease-mediated resolution of local duplications, efficiently converting the original landing site allele to modified alleles that only have the desired change(s). Dominant markers incorporated into this method allow correct individual flies to be efficiently identified at each step. In principle, single attP sites and FRT sites are also valid landing sites. Given the large and increasing number of landing site lines available in the fly community, this method provides an easy and fast way to efficiently edit the majority of the Drosophila genome in a scarless manner. This technique should also be applicable to other species.