Learning probabilistic protein-DNA recognition codes from DNA-binding specificities using structural mappings.

Knowledge of how proteins interact with DNA is essential for understanding gene regulation. Although DNA-binding specificities for thousands of transcription factors (TFs) have been determined, the specific amino acid-base interactions comprising their structural interfaces are largely unknown. This lack of resolution hampers attempts to leverage these data in order to predict specificities for uncharacterized TFs or TFs mutated in disease. Here we introduce recognition code learning via automated mapping of protein-DNA structural interfaces (rCLAMPS), a probabilistic approach that uses DNA-binding specificities for TFs from the same structural family to simultaneously infer both which nucleotide positions are contacted by particular amino acids within the TF as well as a recognition code that relates each base-contacting amino acid to nucleotide preferences at the DNA positions it contacts. We apply rCLAMPS to homeodomains, the second largest family of TFs in metazoans and show that it learns a highly effective recognition code that can predict de novo DNA-binding specificities for TFs. Furthermore, we show that the inferred amino acid-nucleotide contacts reveal whether and how nucleotide preferences at individual binding site positions are altered by mutations within TFs. Our approach is an important step toward automatically uncovering the determinants of protein-DNA specificity from large compendia of DNA-binding specificities and inferring the altered functionalities of TFs mutated in disease.

Molecular mechanisms underlying cellular effects of human MEK1 mutations.

Terminal regions of Drosophila embryos are patterned by signaling through ERK, which is genetically deregulated in multiple human diseases. Quantitative studies of terminal patterning have been recently used to investigate gain-of-function variants of human MEK1, encoding the MEK kinase that directly activates ERK by dual phosphorylation. Unexpectedly, several mutations reduced ERK activation by extracellular signals, possibly through a negative feedback triggered by signal-independent activity of the mutant variants. Here we present experimental evidence supporting this model. Using a MEK variant that combines a mutation within the negative regulatory region with alanine substitutions in the activation loop, we prove that pathogenic variants indeed acquire signal-independent kinase activity. We also demonstrate that signal-dependent activation of these variants is independent of kinase suppressor of Ras, a conserved adaptor that is indispensable for activation of normal MEK. Finally, we show that attenuation of ERK activation by extracellular signals stems from transcriptional induction of Mkp3, a dual specificity phosphatase that deactivates ERK by dephosphorylation. These findings in the Drosophila embryo highlight its power for investigating diverse effects of human disease mutations.

Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination.

We are now in an era where protein-DNA interactions have been experimentally assayed for thousands of DNA-binding proteins. In order to infer DNA-binding specificities from these data, numerous sophisticated computational methods have been developed. These approaches typically infer DNA-binding specificities by considering interactions for each protein independently, ignoring related and potentially valuable interaction information across other proteins that bind DNA via the same structural domain. Here we introduce a framework for inferring DNA-binding specificities by considering protein-DNA interactions for entire groups of structurally similar proteins simultaneously. We devise both constrained optimization and label propagation algorithms for this task, each balancing observations at the individual protein level against dataset-wide consistency of interaction preferences. We test our approaches on two large, independent Cys2His2 zinc finger protein-DNA interaction datasets. We demonstrate that jointly inferring specificities within each dataset individually dramatically improves accuracy, leading to increased agreement both between these two datasets and with a fixed external standard. Overall, our results suggest that sharing protein-DNA interaction information across structurally similar proteins is a powerful means to enable accurate inference of DNA-binding specificities.

A systematic survey of the Cys2His2 zinc finger DNA-binding landscape.

Cys2His2 zinc fingers (C2H2-ZFs) comprise the largest class of metazoan DNA-binding domains. Despite this domain's well-defined DNA-recognition interface, and its successful use in the design of chimeric proteins capable of targeting genomic regions of interest, much remains unknown about its DNA-binding landscape. To help bridge this gap in fundamental knowledge and to provide a resource for design-oriented applications, we screened large synthetic protein libraries to select binding C2H2-ZF domains for each possible three base pair target. The resulting data consist of >160 000 unique domain-DNA interactions and comprise the most comprehensive investigation of C2H2-ZF DNA-binding interactions to date. An integrated analysis of these independent screens yielded DNA-binding profiles for tens of thousands of domains and led to the successful design and prediction of C2H2-ZF DNA-binding specificities. Computational analyses uncovered important aspects of C2H2-ZF domain-DNA interactions, including the roles of within-finger context and domain position on base recognition. We observed the existence of numerous distinct binding strategies for each possible three base pair target and an apparent balance between affinity and specificity of binding. In sum, our comprehensive data help elucidate the complex binding landscape of C2H2-ZF domains and provide a foundation for efforts to determine, predict and engineer their DNA-binding specificities.