Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy.

Summary Plant recognition of pathogen-associated molecular patterns (PAMPs) such as bacterial flagellin-derived flg22 triggers rapid activation of mitogen-activated protein kinases (MAPKs) and generation of reactive oxygen species (ROS). Arabidopsis has at least four PAMP/pathogen-responsive MAPKs: MPK3, MPK6, MPK4, and MPK11. It was speculated that these MAPKs may function downstream of ROS in plant immunity because of their activation by exogenously added H2O2. MPK3/MPK6 or their orthologs in other plant species were also reported to be involved in ROS burst from the plant respiratory burst oxidase homologue (Rboh) of human neutrophil gp91phox. However, detailed genetic analysis is lacking. Using a chemical genetic approach, we generated another conditional loss-of-function mpk3 mpk6 double mutant. Together with the conditionally rescued mpk3 mpk6 double mutant reported previously, we demonstrate that flg22-triggered ROS burst is independent of MPK3/MPK6. In Arabidopsis mutant lacking a functional AtRbohD, flg22-induced ROS burst was completely blocked. However, the activation of MPK3/MPK6 was not affected. Based on these results, we conclude that the rapid ROS burst and MPK3/MPK6 activation are two independent early signaling events downstream of FLS2 in plant immunity. We also found that MPK4 negatively impacts the flg22-induced ROS burst. In addition, salicylic acid-pretreatment enhances AtRbohD-mediated ROS burst, which is again independent of MPK3/MPK6 based on the analysis of mpk3 mpk6 double mutant. The establishment of a mpk3 mpk6 double mutant system using the chemical genetic approach offers us a powerful tool to investigate the function of MPK3/MPK6 in plant defense signaling pathway.

Summary Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database, using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases, respectively. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.

Protein-peptide interactions play a crucial role in a variety of cellular processes. The protein-peptide complex structure is a key to understand the mechanisms underlying protein-peptide interactions and is critical for peptide therapeutic development. We present a user-friendly protein-peptide docking server, MDockPeP. Starting from a peptide sequence and a protein receptor structure, the MDockPeP Server globally docks the all-atom, flexible peptide to the protein receptor. The produced modes are then evaluated with a statistical potential-based scoring function, ITScorePeP. This method was systematically validated using the peptiDB benchmarking database. At least one near-native peptide binding mode was ranked among top 10 (or top 500) in 59% (85%) of the bound cases, and in 40.6% (71.9%) of the challenging unbound cases. The server can be used for both protein-peptide complex structure prediction and initial-stage sampling of the protein-peptide binding modes for other docking or simulation methods. MDockPeP Server is freely available at http://zougrouptoolkit.missouri.edu/mdockpep.