Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets

Transcriptomic analysis of human norovirus NS1-2 protein highlights a multifunctional role in murine monocytes

Background

The GII.4 Sydney 2012 strain of human norovirus (HuNoV) is a pandemic strain that is responsible for the majority of norovirus outbreaks in healthcare settings. The function of the non-structural (NS)1-2 protein from HuNoV is unknown.

Results

In silico analysis of human norovirus NS1-2 protein showed that it shares features with the murine NS1-2 protein, including a disordered region, a transmembrane domain and H-box and NC sequence motifs. The proteins also contain caspase cleavage and phosphorylation sites, indicating that processing and phosphorylation may be a conserved feature of norovirus NS1-2 proteins. In this study, RNA transcripts of human and murine norovirus full-length and the disordered region of NS1-2 were transfected into monocytes, and next generation sequencing was used to analyse the transcriptomic profile of cells expressing virus proteins. The profiles were then compared to the transcriptomic profile of MNV-infected cells.

Conclusions

RNAseq analysis showed that NS1-2 proteins from human and murine noroviruses affect multiple immune systems (chemokine, cytokine, and Toll-like receptor signaling) and intracellular pathways (NFκB, MAPK, PI3K-Akt signaling) in murine monocytes. Comparison to the transcriptomic profile of MNV-infected cells indicated the pathways that NS1-2 may affect during norovirus infection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3417-4) contains supplementary material, which is available to authorized users.

Novel extracellular and nuclear caspase-1 and inflammasomes propagate inflammation and regulate gene expression: a comprehensive database mining study

Background

Caspase-1 is present in the cytosol as an inactive zymogen and requires the protein complexes named “inflammasomes” for proteolytic activation. However, it remains unclear whether the proteolytic activity of caspase-1 is confined only to the cytosol where inflammasomes are assembled to convert inactive pro-caspase-1 to active caspase-1.

Methods

We conducted meticulous data analysis methods on proteomic, protein interaction, protein intracellular localization, and gene expressions of 114 experimentally identified caspase-1 substrates and 38 caspase-1 interaction proteins in normal physiological conditions and in various pathologies.

Results

We made the following important findings: (1) Caspase-1 substrates and interaction proteins are localized in various intracellular organelles including nucleus and secreted extracellularly; (2) Caspase-1 may get activated in situ in the nucleus in response to intra-nuclear danger signals; (3) Caspase-1 cleaves its substrates in exocytotic secretory pathways including exosomes to propagate inflammation to neighboring and remote cells; (4) Most of caspase-1 substrates are upregulated in coronary artery disease regardless of their subcellular localization but the majority of metabolic diseases cause no significant expression changes in caspase-1 nuclear substrates; and (5) In coronary artery disease, majority of upregulated caspase-1 extracellular substrate-related pathways are involved in induction of inflammation; and in contrast, upregulated caspase-1 nuclear substrate-related pathways are more involved in regulating cell death and chromatin regulation.

Conclusions

Our identification of novel caspase-1 trafficking sites, nuclear and extracellular inflammasomes, and extracellular caspase-1-based inflammation propagation model provides a list of targets for the future development of new therapeutics to treat cardiovascular diseases, inflammatory diseases, and inflammatory cancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13045-016-0351-5) contains supplementary material, which is available to authorized users.

Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

Background

Aptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies.

Results

In this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden’s Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden’s Index of 0.451.

Conclusions

These results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1087-5) contains supplementary material, which is available to authorized users.

Crysalis: an integrated server for computational analysis and design of protein crystallization

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.

Combinatorial Screening Identifies Novel Promiscuous Matrix Metalloproteinase Activities that Lead to Inhibition of the Therapeutic Target IL-13

The practical realization of disease modulation by catalytic degradation of a therapeutic target protein suffers from the difficulty to identify candidate proteases, or to engineer their specificity. We identified 23 measurable, specific, and new protease activities using combinatorial screening of 27 human proteases against 24 therapeutic protein targets. We investigate the cleavage of monocyte chemoattractant protein 1, interleukin-6 (IL-6), and IL-13 by matrix metalloproteinases (MMPs) and serine proteases, and demonstrate that cleavage of IL-13 leads to potent inhibition of its biological activity in vitro. MMP-8 degraded human IL-13 most efficiently in vitro and ex vivo in human IL-13 transgenic mouse bronchoalveolar lavage. Hence, MMP-8 is a therapeutic protease lead against IL-13 for inflammatory conditions whereby reported genetic and genomics data suggest an involvement of MMP-8. This work describes the first exploitation of human enzyme promiscuity for therapeutic applications, and reveals both starting points for protease-based therapies and potential new regulatory networks in inflammatory disease.

Uterine endoplasmic reticulum stress-unfolded protein response regulation of gestational length is caspase-3 and -7-dependent

We previously identified myometrial caspase-3 (CASP3) as a potential regulator of uterine quiescence. We also determined that during pregnancy, the functional activation of uterine CASP3 is likely governed by an integrated endoplasmic reticulum stress response (ERSR) and is consequently limited by an increased unfolded protein response (UPR). The present study examined the functional relevance of uterine UPR-ERSR in maintaining myometrial quiescence and regulating the timing of parturition. In vitro analysis of the human uterine myocyte hTERT-HM cell line revealed that tunicamycin (TM)-induced ERSR modified uterine myocyte contractile responsiveness. Accordingly, alteration of in vivo uterine UPR-ERSR using a pregnant mouse model significantly modified gestational length. We determined that "normal" gestational activation of the ERSR-induced CASP3 and caspase 7 (CASP7) maintains uterine quiescence through previously unidentified proteolytic targeting of the gap junction protein, alpha 1(GJA1); however, surprisingly, TM-induced uterine ERSR triggered an exaggerated UPR that eliminated uterine CASP3 and 7 tocolytic action precociously. These events allowed for a premature increase in myometrial GJA1 levels, elevated contractile responsiveness, and the onset of preterm labor. Importantly, a successful reversal of the magnified ERSR-induced preterm birth phenotype could be achieved by pretreatment with 4-phenylbutrate, a chaperone protein mimic.

Progress and challenges in predicting protein methylation sites

Protein methylation catalyzed by methyltransferases carries many important biological functions. Methylation and their regulatory enzymes are involved in a variety of human disease states, raising the possibility that abnormally methylated proteins can be disease markers and methyltransferases are potential therapeutic targets. Identification of methylation sites is a prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles that have been implicated in the pathological processes. Due to various limitations of experimental methods, in silico approaches for identifying novel methylation sites have become increasingly popular. In this review, we summarize the progress in the prediction of protein methylation sites from the dataset, feature representation, prediction algorithm and online resources in the past ten years. We also discuss the challenges that are faced while developing novel predictors in the future. The development and application of methylation site prediction is a promising field of systematic biology, provided that protein methyltransferases, species and functional information will be taken into account.

A combined cheminformatic and bioinformatic approach to address the proteolytic stability challenge in peptide-based drug discovery

We have created models to predict cleavage sites for several human proteases including caspase-1, caspase-3, caspase-6, caspase-7, cathepsin B, cathepsin D, cathepsin G, cathepsin K, cathepsin L, elastase-2, granzyme A, granzyme B, matrix metallopeptidase-2 (MMP2), MMP7, MMP9, thrombin, and trypsin-1. Rather than representing the sequence pattern around the potential cleavage site through a series of flags with each flag representing one of the 20 standard amino acids, we first represent each amino acid by its calculated properties. For these calculated properties, we use validated cheminformatic descriptors, such as molecular weight, logP, and polar surface area, of the individual amino acids. Finally, the cleavage site-specific descriptors are calculated through various combinations of the individual amino acid descriptors for the residues surrounding the cleavage site. Some of these combinations do not take into account the location of the residue, as long as it is in a prescribed neighborhood of the potential cleavage site, whereas others are sensitive to the precise order of the residues in the sequence. The key advantage of this approach is that it allows one to perform meaningful calculations with nonstandard amino acids for which little or no data exists. Finally, using both docking and molecular dynamics simulations, we examine the potential for and limitations of protease crystal structures to impact the design of proteolytically stable peptides.

Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs

Prokaryotic proteins are regulated by pupylation, a type of post-translational modification that contributes to cellular function in bacterial organisms. In pupylation process, the prokaryotic ubiquitin-like protein (Pup) tagging is functionally analogous to ubiquitination in order to tag target proteins for proteasomal degradation. To date, several experimental methods have been developed to identify pupylated proteins and their pupylation sites, but these experimental methods are generally laborious and costly. Therefore, computational methods that can accurately predict potential pupylation sites based on protein sequence information are highly desirable. In this paper, a novel predictor termed as pbPUP has been developed for accurate prediction of pupylation sites. In particular, a sophisticated sequence encoding scheme [i.e. the profile-based composition of k-spaced amino acid pairs (pbCKSAAP)] is used to represent the sequence patterns and evolutionary information of the sequence fragments surrounding pupylation sites. Then, a Support Vector Machine (SVM) classifier is trained using the pbCKSAAP encoding scheme. The final pbPUP predictor achieves an AUC value of 0.849 in10-fold cross-validation tests and outperforms other existing predictors on a comprehensive independent test dataset. The proposed method is anticipated to be a helpful computational resource for the prediction of pupylation sites. The web server and curated datasets in this study are freely available at http://protein.cau.edu.cn/pbPUP/.

Caspase cleavage sites in the human proteome: CaspDB, a database of predicted substrates

Caspases are enzymes belonging to a conserved family of cysteine-dependent aspartic-specific proteases that are involved in vital cellular processes and play a prominent role in apoptosis and inflammation. Determining all relevant protein substrates of caspases remains a challenging task. Over 1500 caspase substrates have been discovered in the human proteome according to published data and new substrates are discovered on a daily basis. To aid the discovery process we developed a caspase cleavage prediction method using the recently published curated MerCASBA database of experimentally determined caspase substrates and a Random Forest classification method. On both internal and external test sets, the ranking of predicted cleavage positions is superior to all previously developed prediction methods. The in silico predicted caspase cleavage positions in human proteins are available from a relational database: CaspDB. Our database provides information about potential cleavage sites in a verified set of all human proteins collected in Uniprot and their orthologs, allowing for tracing of cleavage motif conservation. It also provides information about the positions of disease-annotated single nucleotide polymorphisms, and posttranslational modifications that may modulate the caspase cleaving efficiency.

Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features

Lysine acetylation is a reversible post-translational modification, playing an important role in cytokine signaling, transcriptional regulation, and apoptosis. To fully understand acetylation mechanisms, identification of substrates and specific acetylation sites is crucial. Experimental identification is often time-consuming and expensive. Alternative bioinformatics methods are cost-effective and can be used in a high-throughput manner to generate relatively precise predictions. Here we develop a method termed as SSPKA for species-specific lysine acetylation prediction, using random forest classifiers that combine sequence-derived and functional features with two-step feature selection. Feature importance analysis indicates functional features, applied for lysine acetylation site prediction for the first time, significantly improve the predictive performance. We apply the SSPKA model to screen the entire human proteome and identify many high-confidence putative substrates that are not previously identified. The results along with the implemented Java tool, serve as useful resources to elucidate the mechanism of lysine acetylation and facilitate hypothesis-driven experimental design and validation.