RBPJ/CBF1 interacts with L3MBTL3/MBT1 to promote repression of Notch signaling via histone demethylase KDM1A/LSD1.

Notch signaling is an evolutionarily conserved signal transduction pathway that is essential for metazoan development. Upon ligand binding, the Notch intracellular domain (NOTCH ICD) translocates into the nucleus and forms a complex with the transcription factor RBPJ (also known as CBF1 or CSL) to activate expression of Notch target genes. In the absence of a Notch signal, RBPJ acts as a transcriptional repressor. Using a proteomic approach, we identified L3MBTL3 (also known as MBT1) as a novel RBPJ interactor. L3MBTL3 competes with NOTCH ICD for binding to RBPJ In the absence of NOTCH ICD, RBPJ recruits L3MBTL3 and the histone demethylase KDM1A (also known as LSD1) to the enhancers of Notch target genes, leading to H3K4me2 demethylation and to transcriptional repression. Importantly, in vivo analyses of the homologs of RBPJ and L3MBTL3 in Drosophila melanogaster and Caenorhabditis elegans demonstrate that the functional link between RBPJ and L3MBTL3 is evolutionarily conserved, thus identifying L3MBTL3 as a universal modulator of Notch signaling in metazoans.

Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade.

We report the structure prediction results of a new composite pipeline for template-based modeling (TBM) in the 11th CASP experiment. Starting from multiple structure templates identified by LOMETS based meta-threading programs, the QUARK ab initio folding program is extended to generate initial full-length models under strong constraints from template alignments. The final atomic models are then constructed by I-TASSER based fragment reassembly simulations, followed by the fragment-guided molecular dynamic simulation and the MQAP-based model selection. It was found that the inclusion of QUARK-TBM simulations as an intermediate modeling step could help improve the quality of the I-TASSER models for both Easy and Hard TBM targets. Overall, the average TM-score of the first I-TASSER model is 12% higher than that of the best LOMETS templates, with the RMSD in the same threading-aligned regions reduced from 5.8 to 4.7 Å. Nevertheless, there are nearly 18% of TBM domains with the templates deteriorated by the structure assembly pipeline, which may be attributed to the errors of secondary structure and domain orientation predictions that propagate through and degrade the procedures of template identification and final model selections. To examine the record of progress, we made a retrospective report of the I-TASSER pipeline in the last five CASP experiments (CASP7-11). The data show no clear progress of the LOMETS threading programs over PSI-BLAST; but obvious progress on structural improvement relative to threading templates was witnessed in recent CASP experiments, which is probably attributed to the integration of the extended ab initio folding simulation with the threading assembly pipeline and the introduction of atomic-level structure refinements following the reduced modeling simulations. Proteins 2016; 84(Suppl 1):233-246. © 2015 Wiley Periodicals, Inc.

Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc.

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project.

Alternative splicing allows a single gene to produce multiple transcript-level splice isoforms from which the translated proteins may show differences in their expression and function. Identifying the major functional or canonical isoform is important for understanding gene and protein functions. Identification and characterization of splice isoforms is a stated goal of the HUPO Human Proteome Project and of neXtProt. Multiple efforts have catalogued splice isoforms as "dominant", "principal", or "major" isoforms based on expression or evolutionary traits. In contrast, we recently proposed highest connected isoforms (HCIs) as a new class of canonical isoforms that have the strongest interactions in a functional network and revealed their significantly higher (differential) transcript-level expression compared to nonhighest connected isoforms (NCIs) regardless of tissues/cell lines in the mouse. HCIs and their expression behavior in the human remain unexplored. Here we identified HCIs for 6157 multi-isoform genes using a human isoform network that we constructed by integrating a large compendium of heterogeneous genomic data. We present examples for pairs of transcript isoforms of ABCC3, RBM34, ERBB2, and ANXA7. We found that functional networks of isoforms of the same gene can show large differences. Interestingly, differential expression between HCIs and NCIs was also observed in the human on an independent set of 940 RNA-seq samples across multiple tissues, including heart, kidney, and liver. Using proteomic data from normal human retina and placenta, we showed that HCIs are a promising indicator of expressed protein isoforms exemplified by NUDFB6 and M6PR. Furthermore, we found that a significant percentage (20%, p = 0.0003) of human and mouse HCIs are homologues, suggesting their conservation between species. Our identified HCIs expand the repertoire of canonical isoforms and are expected to facilitate studying main protein products, understanding gene regulation, and possibly evolution. The network is available through our web server as a rich resource for investigating isoform functional relationships (http://guanlab.ccmb.med.umich.edu/hisonet). All MS/MS data were available at ProteomeXchange Web site (http://www.proteomexchange.org) through their identifiers (retina: PXD001242, placenta: PXD000754).

Mapping monomeric threading to protein-protein structure prediction.

The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD <2.5 Å by SPRING is 134% and 167% higher than these competing methods. SPRING is controlled with ZDOCK on 77 docking benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein-protein interactions due to the high speed and accuracy.

Extending Protein Domain Boundary Predictors to Detect Discontinuous Domains.

A variety of protein domain predictors were developed to predict protein domain boundaries in recent years, but most of them cannot predict discontinuous domains. Considering nearly 40% of multidomain proteins contain one or more discontinuous domains, we have developed DomEx to enable domain boundary predictors to detect discontinuous domains by assembling the continuous domain segments. Discontinuous domains are predicted by matching the sequence profile of concatenated continuous domain segments with the profiles from a single-domain library derived from SCOP and CATH, and Pfam. Then the matches are filtered by similarity to library templates, a symmetric index score and a profile-profile alignment score. DomEx recalled 32.3% discontinuous domains with 86.5% precision when tested on 97 non-homologous protein chains containing 58 continuous and 99 discontinuous domains, in which the predicted domain segments are within ±20 residues of the boundary definitions in CATH 3.5. Compared with our recently developed predictor, ThreaDom, which is the state-of-the-art tool to detect discontinuous-domains, DomEx recalled 26.7% discontinuous domains with 72.7% precision in a benchmark with 29 discontinuous-domain chains, where ThreaDom failed to predict any discontinuous domains. Furthermore, combined with ThreaDom, the method ranked number one among 10 predictors. The source code and datasets are available at https://github.com/xuezhidong/DomEx.