Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.

In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolutional neural network (3DCNN) is an expansion of the 2DCNN and has been applied in several fields, including object recognition. The 3DCNN is also used for MQA tasks, but the performance is low due to several technical limitations related to protein tertiary structures, such as orientation alignment. We proposed a novel single-model MQA method based on local structure quality evaluation using a deep neural network containing 3DCNN layers. The proposed method first assesses the quality of local structures for each residue and then evaluates the quality of whole structures by integrating estimated local qualities. We analyzed the model using the CASP11, CASP12, and 3D-Robot datasets and compared the performance of the model with that of the previous 3DCNN method based on whole protein structures. The proposed method showed a significant improvement compared to the previous 3DCNN method for multiple evaluation measures. We also compared the proposed method to other state-of-the-art methods. Our method showed better performance than the previous 3DCNN-based method and comparable accuracy as the current best single-model methods; particularly, in CASP11 stage2, our method showed a Pearson coefficient of 0.486, which was better than those of the best single-model methods (0.366-0.405). A standalone version of the proposed method and data files are available at https://github.com/ishidalab-titech/3DCNN_MQA.

Improving low-accuracy protein structures using enhanced sampling techniques.

In this paper, we report results of using enhanced sampling and blind selection techniques for high-accuracy protein structural refinement. By combining a parallel continuous simulated tempering (PCST) method, previously developed by Zang et al. [J. Chem. Phys. 141, 044113 (2014)], and the structure based model (SBM) as restraints, we refined 23 targets (18 from the refinement category of the CASP10 and 5 from that of CASP12). We also designed a novel model selection method to blindly select high-quality models from very long simulation trajectories. The combined use of PCST-SBM with the blind selection method yielded final models that are better than initial models. For Top-1 group, 7 out of 23 targets had better models (greater global distance test total scores) than the critical assessment of structure prediction participants. For Top-5 group, 10 out of 23 were better. Our results justify the crucial position of enhanced sampling in protein structure prediction and refinement and demonstrate that a considerable improvement of low-accuracy structures is achievable with current force fields.

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12.

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Epigallocatechin Gallate Reduces Amyloid ß-Induced Neurotoxicity via Inhibiting Endoplasmic Reticulum Stress-Mediated Apoptosis.

SCOPE: We investigated the role of endoplasmic reticulum (ER) stress in the protective effects of EGCG against the neuronal apoptosis in Aß1-42 -induced SH-SY5Y cells and APP/PS1 transgenic mice. METHODS AND RESULTS: Cell viability (CCK8 assay), flow cytometry, Hoechst 33258 staining, immunohistochemistry, transmission electron microscopy (TEM), and western blotting were used. EGCG prevented Aß1-42-induced toxicity in SH-SY5Y cells, increased cell viability, and decreased apoptosis in a dose-dependent manner. In a subsequent mechanism study, it was found that this effect contributed to the down-regulation of GRP78, CHOP, cleaved-caspase-12 and -3. Moreover, EGCG also reduced the cytotoxicity induced by tunicamycin (TM) and thapsigargin (TG), two ER stress activators. Consistent with the in vitro study, EGCG inhibited neuronal apoptosis in the cortex of APP/PS1 transgenic mice, with the mitigation of ER abnormal ultrastructural swelling and the downregulation of ER-stress-associated proteins. CONCLUSION: These results indicate that EGCG attenuates the neurotoxicity in Alzheimer's disease (AD) via a novel mechanism that involves inhibition of ER-stress-associated neuronal apoptosis in vitro and in vivo, suggesting the tremendous potential of EGCG for use in a nutritional preventive strategy against AD.

Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.

Motivation: Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. Results: We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy. Availability and implementation: Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2. Contact: saulo.deoliveira@dtc.ox.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Single-cell imaging of inflammatory caspase dimerization reveals differential recruitment to inflammasomes.

The human inflammatory caspases, including caspase-1, -4, -5 and -12, are considered as key regulators of innate immunity protecting from sepsis and numerous inflammatory diseases. Caspase-1 is activated by proximity-induced dimerization following recruitment to inflammasomes but the roles of the remaining inflammatory caspases in inflammasome assembly are unclear. Here, we use caspase bimolecular fluorescence complementation to visualize the assembly of inflammasomes and dimerization of inflammatory caspases in single cells. We observed caspase-1 dimerization induced by the coexpression of a range of inflammasome proteins and by lipospolysaccharide (LPS) treatment in primary macrophages. Caspase-4 and -5 were only dimerized by select inflammasome proteins, whereas caspase-12 dimerization was not detected by any investigated treatment. Strikingly, we determined that certain inflammasome proteins could induce heterodimerization of caspase-1 with caspase-4 or -5. Caspase-5 homodimerization and caspase-1/-5 heterodimerization was also detected in LPS-primed primary macrophages in response to cholera toxin subunit B. The subcellular localization and organization of the inflammasome complexes varied markedly depending on the upstream trigger and on which caspase or combination of caspases were recruited. Three-dimensional imaging of the ASC (apoptosis-associated speck-like protein containing a caspase recruitment domain)/caspase-1 complexes revealed a large spherical complex of ASC with caspase-1 dimerized on the outer surface. In contrast, NALP1 (NACHT leucine-rich repeat protein 1)/caspase-1 complexes formed large filamentous structures. These results argue that caspase-1, -4 or -5 can be recruited to inflammasomes under specific circumstances, often leading to distinctly organized and localized complexes that may impact the functions of these proteases.

Purification of catalytically active caspase-12 and its biochemical characterization.

Caspase-12, mainly detected in endoplasmic reticulum (ER), has been suggested to play a role in ER-mediated apoptosis and inflammatory caspase activation pathway. Cleavage of the prodomain by caspase-3/-7 at the carboxyl terminus of Asp94 or m-calpain at the carboxyl terminus of Lys158 was reported to be a part of caspase-12-involved apoptosis. We biochemically characterized the prodomain-free forms of caspase-12 and the equivalent enzymes; Deltapro1(G95-D419), rev-Deltapro1[(T319-N419)-(G95-D318), a reverse form of Deltapro1] and rev-Deltapro2[(T319-N419)-(T159-D318)]. The three variants showed comparable activities which were dependent on salt concentration and pH. Auto-proteolytic cleavage was observed at two sites (carboxyl termini of Asp318 and Asp320) in Deltapro1. Constitutively active forms of caspase-12 (rev-Deltapro1 and rev-Deltapro2) could induce cell death in cells transfected with the corresponding expression vectors, but no cleavage of caspase-3, DFF45 or Bid was observed, indicating caspase-12 may mediate a distinct apoptotic pathway rather than caspase-8 or -9-mediated cell death.

E2-25K/Hip-2 regulates caspase-12 in ER stress-mediated Abeta neurotoxicity.

Amyloid-beta (Abeta) neurotoxicity is believed to contribute to the pathogenesis of Alzheimer's disease (AD). Previously we found that E2-25K/Hip-2, an E2 ubiquitin-conjugating enzyme, mediates Abeta neurotoxicity. Here, we report that E2-25K/Hip-2 modulates caspase-12 activity via the ubiquitin/proteasome system. Levels of endoplasmic reticulum (ER)-resident caspase-12 are strongly up-regulated in the brains of AD model mice, where the enzyme colocalizes with E2-25K/Hip-2. Abeta increases expression of E2-25K/Hip-2, which then stabilizes caspase-12 protein by inhibiting proteasome activity. This increase in E2-25K/Hip-2 also induces proteolytic activation of caspase-12 through its ability to induce calpainlike activity. Knockdown of E2-25K/Hip-2 expression suppresses neuronal cell death triggered by ER stress, and thus caspase-12 is required for the E2-25K/Hip-2-mediated cell death. Finally, we find that E2-25K/Hip-2-deficient cortical neurons are resistant to Abeta toxicity and to the induction of ER stress and caspase-12 expression by Abeta. E2-25K/Hip-2 is thus an essential upstream regulator of the expression and activation of caspase-12 in ER stress-mediated Abeta neurotoxicity.