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.

BackgroundNext-generation sequencing technologies allow genomes to be sequenced more quickly and less expensively than ever before. However, as sequencing technology has improved, the difficulty of de novo genome assembly has increased, due in large part to the shorter reads generated by the new technologies. The use of mated sequences (referred to as mate-pairs) is a standard means of disambiguating assemblies to obtain a more complete picture of the genome without resorting to manual finishing. Here, we examine the effectiveness of mate-pair information in resolving repeated sequences in the DNA (a paramount issue to overcome). While it has been empirically accepted that mate-pairs improve assemblies, and a variety of assemblers use mate-pairs in the context of repeat resolution, the effectiveness of mate-pairs in this context has not been systematically evaluated in previous literature.ResultsWe show that, in high-coverage prokaryotic assemblies, libraries of short mate-pairs (about 4-6 times the read-length) more effectively disambiguate repeat regions than the libraries that are commonly constructed in current genome projects. We also demonstrate that the best assemblies can be obtained by 'tuning' mate-pair libraries to accommodate the specific repeat structure of the genome being assembled - information that can be obtained through an initial assembly using unpaired reads. These results are shown across 360 simulations on 'ideal' prokaryotic data as well as assembly of 8 bacterial genomes using SOAPdenovo. The simulation results provide an upper-bound on the potential value of mate-pairs for resolving repeated sequences in real prokaryotic data sets. The assembly results show that our method of tuning mate-pairs exploits fundamental properties of these genomes, leading to better assemblies even when using an off -the-shelf assembler in the presence of base-call errors.ConclusionsOur results demonstrate that dramatic improvements in prokaryotic genome assembly quality can be achieved by tuning mate-pair sizes to the actual repeat structure of a genome, suggesting the possible need to change the way sequencing projects are designed. We propose that a two-tiered approach - first generate an assembly of the genome with unpaired reads in order to evaluate the repeat structure of the genome; then generate the mate-pair libraries that provide most information towards the resolution of repeats in the genome being assembled - is not only possible, but likely also more cost-effective as it will significantly reduce downstream manual finishing costs. In future work we intend to address the question of whether this result can be extended to larger eukaryotic genomes, where repeat structure can be quite different.

Background: Genome assembly is difficult due to repeated sequences within the genome, which create ambiguities and cause the final assembly to be broken up into many separate sequences (contigs). Long range linking information, such as mate-pairs or mapping data, is necessary to help assembly software resolve repeats, thereby leading to a more complete reconstruction of genomes. Prior work has used optical maps for validating assemblies and scaffolding contigs, after an initial assembly has been produced. However, optical maps have not previously been used within the genome assembly process. Here, we use optical map information within the popular de Bruijn graph assembly paradigm to eliminate paths in the de Bruijn graph which are not consistent with the optical map and help determine the correct reconstruction of the genome. Results: We developed a new algorithm called AGORA: Assembly Guided by Optical Restriction Alignment. AGORA is the first algorithm to use optical map information directly within the de Bruijn graph framework to help produce an accurate assembly of a genome that is consistent with the optical map information provided. Our simulations on bacterial genomes show that AGORA is effective at producing assemblies closely matching the reference sequences. Additionally, we show that noise in the optical map can have a strong impact on the final assembly quality for some complex genomes, and we also measure how various characteristics of the starting de Bruijn graph may impact the quality of the final assembly. Lastly, we show that a proper choice of restriction enzyme for the optical map may substantially improve the quality of the final assembly. Conclusions: Our work shows that optical maps can be used effectively to assemble genomes within the de Bruijn graph assembly framework. Our experiments also provide insights into the characteristics of the mapping data that most affect the performance of our algorithm, indicating the potential benefit of more accurate optical mapping technologies, such as nano-coding.

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.

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.

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 KSR, 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.

No abstract

Knowledge of how proteins interact with DNA is essential for understanding gene regulation. While 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 rCLAMPS (Recognition Code Learning via Automated Mapping of Protein-DNA Structural interfaces), 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 demonstrate 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 towards 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.