Intrinsic flexibility of DNA has hampered the development of efficient protein−DNA docking methods. In this study we extend HADDOCK (High Ambiguity Driven DOCKing) [C. Dominguez, R. Boelens and A. M. J. J. Bonvin (2003) J. Am. Chem. Soc. 125, 1731–1737] to explicitly deal with DNA flexibility. HADDOCK uses non-structural experimental data to drive the docking during a rigid-body energy minimization, and semi-flexible and water refinement stages. The latter allow for flexibility of all DNA nucleotides and the residues of the protein at the predicted interface. We evaluated our approach on the monomeric repressor−DNA complexes formed by bacteriophage 434 Cro, the Escherichia coli Lac headpiece and bacteriophage P22 Arc. Starting from unbound proteins and canonical B-DNA we correctly predict the correct spatial disposition of the complexes and the specific conformation of the DNA in the published complexes. This information is subsequently used to generate a library of pre-bent and twisted DNA structures that served as input for a second docking round. The resulting top ranking solutions exhibit high similarity to the published complexes in terms of root mean square deviations, intermolecular contacts and DNA conformation. Our two-stage docking method is thus able to successfully predict protein−DNA complexes from unbound constituents using non-structural experimental data to drive the docking.
Tomato is the most consumed vegetable in the world. Increasing its natural resistance and resilience is key for ensuring food security within a changing climate. Plant breeders improve those traits by generating crosses of cultivated tomatoes with their wild relatives. Specific allele introgression relying on meiotic recombination, is hampered by structural divergence between parental genomes. However, previous studies of interspecific tomato hybridization focused in single cross or lacked resolution due to prohibitive sequencing costs of large segregating populations. Here, we used pooled-pollen sequencing to reveal unprecedented details of recombination patterns in five interspecific tomato hybrids. We detected hybrid-specific recombination coldspots that underscore the influence of structural divergence in shaping recombination landscape. Crossover regions and coldspots show strong association with specific TE superfamilies exhibiting differentially accessible chromatin between somatic and meiotic cells. We also found gene complexes associated with metabolic processes, stress resistance and domestication syndrome traits, revealing undesired consequences of recombination suppression to phenotypes. Finally, we demonstrate that by using resequencing data of wild and domesticated tomato populations, we can screen for alternative parental genomes to overcome recombination barriers. Overall, our results will allow breeders better informed decisions on generating disease-resistant and climate-resilient tomato.
Exome sequencing is now mainstream in clinical practice, however, identification of pathogenic Mendelian variants remains time consuming, partly because limited accuracy of current computational prediction methods leaves much manual classification. Here we introduce CAPICE, a new machine-learning based method for prioritizing pathogenic variants, including SNVs and short InDels, that outperforms best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily integrated into diagnostic pipelines and is available as free and open source command-line software, file of pre-computed scores, and as a web application with web service API.
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