SVM-based prediction of caspase substrate cleavage sites

BACKGROUND: Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. RESULTS: A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P(1)' and P(2)' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. CONCLUSION: This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites

Calpain Cleavage Prediction Using Multiple Kernel Learning

Calpain, an intracellular -dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org

Developing a powerful In Silico tool for the discovery of novel caspase-3 substrates: a preliminary screening of the human proteome

<p>Abstract</p> <p>Background</p> <p>Caspases are a family of cysteinyl proteases that regulate apoptosis and other biological processes. Caspase-3 is considered the central executioner member of this family with a wide range of substrates. Identification of caspase-3 cellular targets is crucial to gain further insights into the cellular mechanisms that have been implicated in various diseases including: cancer, neurodegenerative, and immunodeficiency diseases. To date, over 200 caspase-3 substrates have been identified experimentally. However, many are still awaiting discovery.</p> <p>Results</p> <p>Here, we describe a powerful bioinformatics tool that can predict the presence of caspase-3 cleavage sites in a given protein sequence using a Position-Specific Scoring Matrix (PSSM) approach. The present tool, which we call CAT3, was built using 227 confirmed caspase-3 substrates that were carefully extracted from the literature. Assessing prediction accuracy using 10 fold cross validation, our method shows AUC (area under the ROC curve) of 0.94, sensitivity of 88.83%, and specificity of 89.50%. The ability of CAT3 in predicting the precise cleavage site was demonstrated in comparison to existing state-of-the-art tools. In contrast to other tools which were trained on cleavage sites of various caspases as well as other similar proteases, CAT3 showed a significant decrease in the false positive rate. This cost effective and powerful feature makes CAT3 an ideal tool for high-throughput screening to identify novel caspase-3 substrates.</p> <p>The developed tool, CAT3, was used to screen 13,066 human proteins with assigned gene ontology terms. The analyses revealed the presence of many potential caspase-3 substrates that are not yet described. The majority of these proteins are involved in signal transduction, regulation of cell adhesion, cytoskeleton organization, integrity of the nucleus, and development of nerve cells.</p> <p>Conclusions</p> <p>CAT3 is a powerful tool that is a clear improvement over existing similar tools, especially in reducing the false positive rate. Human proteome screening, using CAT3, indicate the presence of a large number of possible caspase-3 substrates that exceed the anticipated figure. In addition to their involvement in various expected functions such as cytoskeleton organization, nuclear integrity and adhesion, a large number of the predicted substrates are remarkably associated with the development of nerve tissues.</p

In silico prediction of the granzyme B degradome

10.1186/1471-2164-12-S3-S1110th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet)12SUPPL. 3S1

A multi-factor model for caspase degradome prediction

<p>Abstract</p> <p>Background</p> <p>Caspases belong to a class of cysteine proteases which function as critical effectors in cellular processes such as apoptosis and inflammation by cleaving substrates immediately after unique tetrapeptide sites. With hundreds of reported substrates and many more expected to be discovered, the elucidation of the caspase degradome will be an important milestone in the study of these proteases in human health and disease. Several computational methods for predicting caspase cleavage sites have been developed recently for identifying potential substrates. However, as most of these methods are based primarily on the detection of the tetrapeptide cleavage sites - a factor necessary but not sufficient for predicting <it>in vivo </it>substrate cleavage - prediction outcomes will inevitably include many false positives.</p> <p>Results</p> <p>In this paper, we show that structural factors such as the presence of disorder and solvent exposure in the vicinity of the cleavage site are important and can be used to enhance results from cleavage site prediction. We constructed a two-step model incorporating cleavage site prediction and these factors to predict caspase substrates. Sequences are first predicted for cleavage sites using CASVM or GraBCas. Predicted cleavage sites are then scored, ranked and filtered against a cut-off based on their propensities for locating in disordered and solvent exposed regions. Using an independent dataset of caspase substrates, the model was shown to achieve greater positive predictive values compared to CASVM or GraBCas alone, and was able to reduce the false positives pool by up to 13% and 53% respectively while retaining all true positives. We applied our prediction model on the family of receptor tyrosine kinases (RTKs) and highlighted several members as potential caspase targets. The results suggest that RTKs may be generally regulated by caspase cleavage and in some cases, promote the induction of apoptotic cell death - a function distinct from their role as transducers of survival and growth signals.</p> <p>Conclusion</p> <p>As a step towards the prediction of <it>in vivo </it>caspase substrates, we have developed an accurate method incorporating cleavage site prediction and structural factors. The multi-factor model augments existing methods and complements experimental efforts to define the caspase degradome on the systems-wide basis.</p

LabCaS: Labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields

The calpain family of Ca 2+ ‐dependent cysteine proteases plays a vital role in many important biological processes which is closely related with a variety of pathological states. Activated calpains selectively cleave relevant substrates at specific cleavage sites, yielding multiple fragments that can have different functions from the intact substrate protein. Until now, our knowledge about the calpain functions and their substrate cleavage mechanisms are limited because the experimental determination and validation on calpain binding are usually laborious and expensive. In this work, we aim to develop a new computational approach (LabCaS) for accurate prediction of the calpain substrate cleavage sites from amino acid sequences. To overcome the imbalance of negative and positive samples in the machine‐learning training which have been suffered by most of the former approaches when splitting sequences into short peptides, we designed a conditional random field algorithm that can label the potential cleavage sites directly from the entire sequences. By integrating the multiple amino acid features and those derived from sequences, LabCaS achieves an accurate recognition of the cleave sites for most calpain proteins. In a jackknife test on a set of 129 benchmark proteins, LabCaS generates an AUC score 0.862. The LabCaS program is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/LabCaS . Proteins 2013. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97155/1/24217_ftp.pd

In Silico prediction of the Caspase degradome

Ph.DDOCTOR OF PHILOSOPH

Pripper: prediction of caspase cleavage sites from whole proteomes

Peer reviewe

Identification of an Amino-Terminal Fragment of Apolipoprotein E4 that Localizes to Neurofibrillary Tangles of the Alzheimer’s Disease Brain

Although the risk factor for harboring the apolipoprotein E4 (apoE4) allele in late-onset Alzheimer’s disease (AD) is well known, the mechanism by which apoE4 contributes to AD pathogenesis has yet to be clarified. Preferential cleavage of the ApoE4 isoform relative to other polymorphic forms appears to be significant, as the resulting fragments are associated with hallmarks of AD. To examine the possible role of apoE4 proteolysis in AD, we designed a site-directed antibody directed at position D172, which would yield a predicted amino-terminal fragment previously identified in AD brain extracts. Western blot analysis utilizing this novel antibody, termed the amino-terminal apoE4 cleavage fragment (nApoE4CF) Ab consistently identified the predicted amino-terminal fragment (~18 kDa) in several commercially available forms of human recombinant apoE4 purified from E. coli. Mass spectrometry confirmed the identity of this 18 kDa fragment as being an amino-terminal fragment of apoE4. Immunohistochemical experiments indicated the nApoE4CF Ab specifically labeled neurofibrillary tangles (NFTs) in AD frontal cortex sections that colocalized with the mature tangle marker PHF-1. Taken together, these results suggest a novel cleavage event of apoE4, generating an amino-terminal fragment that localizes within NFTs of the AD brain

Caspase-8-mediated PAR-4 cleavage is required for TNFα-induced apoptosis

The tumor suppressor protein prostate apoptosis response-4 (PAR-4) is silenced in a subset of human cancers and its down-regulation serves as a mechanism for cancer cell survival following chemotherapy. PAR-4 re-expression selectively causes apoptosis in cancer cells but how its pro-apoptotic functions are controlled and executed precisely is currently unknown. We demonstrate here that UV-induced apoptosis results in a rapid caspase-dependent PAR-4 cleavage at EEPD131¯G, a sequence that was preferentially recognized by caspase-8. To investigate the effect on cell growth for this cleavage event we established stable cell lines that express wild-type-PAR-4 or the caspase cleavage resistant mutant PAR-4 D131G under the control of a doxycycline-inducible promoter. Induction of the wild-type protein but not the mutant interfered with cell proliferation, predominantly through induction of apoptosis. We further demonstrate that TNFα-induced apoptosis leads to caspase-8-dependent PAR-4-cleavage followed by nuclear accumulation of the C-terminal PAR-4 (132-340) fragment, which then induces apoptosis. Taken together, our results indicate that the mechanism by which PAR-4 orchestrates the apoptotic process requires cleavage by caspase-8