Dataset Information

The VHSE-based prediction of proteasomal cleavage sites.

ABSTRACT: Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to construct prediction models by support vector machine (SVM). For both in vivo and in vitro datasets, the performance of VHSE-based method is comparatively better than that of the well-known PAProC, MAPPP, and NetChop methods. The results reveal that the hydrophobic property of 10 residues both upstream and downstream of the cleavage site is a dominant factor affecting in vivo and in vitro cleavage specificities, followed by residue's electronic and steric properties. Furthermore, the difference in hydrophobic potential between residues flanking the cleavage site is proposed to favor substrate cleavages. Overall, the interpretable VHSE-based method provides a preferable way to predict proteasomal cleavage sites.

SUBMITTER: Xie J

PROVIDER: S-EPMC3767653 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The VHSE-based prediction of proteasomal cleavage sites.

Xie Jiangan J Xu Zhiling Z Zhou Shangbo S Pan Xianchao X Cai Shaoxi S Yang Li L Mei Hu H

PloS one 20130909 9

Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to c ...[more]

PMID: 24040264

Similar Datasets

Project description:Removal of moderately oxidized proteins is mainly carried out by the proteasome, while highly modified proteins are no longer degradable. However, in the case of proteins modified by nitration of tyrosine residues to 3-nitrotyrosine (NO2Y), the role of the proteasome remains to be established. For this purpose, degradation assays and mass spectrometry analyses were performed using isolated proteasome and purified fractions of native cytochrome c (Cyt c) and tyrosine nitrated proteoforms (NO2Y74-Cyt c and NO2Y97-Cyt c). While Cyt c treated under mild conditions with hydrogen peroxide was preferentially degraded by the proteasome, NO2Y74- and NO2Y97-Cyt c species did not show an increased degradation rate with respect to native Cyt c. Peptide mapping analysis confirmed a decreased chymotrypsin-like cleavage at C-terminal of NO2Y sites within the protein, with respect to unmodified Y residues. Additionally, studies with the proteasome substrate suc-LLVY-AMC (Y-AMC) and its NO2Y-containing analog, suc-LLVNO2Y-AMC (NO2Y-AMC) were performed, both using isolated 20S-proteasome and astrocytoma cell lysates as the proteasomal source. Comparisons of both substrates showed a significantly decreased proteasome activity towards NO2Y-AMC. Moreover, NO2Y-AMC, but not Y-AMC degradation rates, were largely diminished by increasing the reaction pH, suggesting an inhibitory influence of the additional negative charge contained in NO2Y-AMC secondary to nitration. The mechanism of slowing of proteasome activity in NO2Y-contaning peptides was further substantiated in studies using the phenylalanine and nitro-phenylalanine peptide analog substrates. Finally, degradation rates of Y-AMC and NO2Y-AMC with proteinase K were the same, demonstrating the selective inability of the proteasome to readily cleave at nitrotyrosine sites. Altogether, data indicate that the proteasome has a decreased capability to cleave at C-terminal of NO2Y residues in proteins with respect to the unmodified residues, making this a possible factor that decreases the turnover of oxidized proteins, if they are not unfolded, and facilitating the accumulation of nitrated proteins.

Project description:BackgroundCaspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments.ResultsThree different pattern recognition classifiers were developed for predicting caspase cleavage sites from protein sequences. Evaluation of the classifiers with quality measures indicated that all of the three classifiers performed well in predicting caspase cleavage sites, and when combining different classifiers the accuracy increased further. A new tool, Pripper, was developed to utilize the classifiers and predict the caspase cut sites from an arbitrary number of input sequences. A database was constructed with the developed tool, and it was used to identify caspase target proteins from tandem mass spectrometry data from two different proteomic experiments. Both known caspase cleavage products as well as novel cleavage products were identified using the database demonstrating the usefulness of the tool. Pripper is not restricted to predicting only caspase cut sites, but it gives the possibility to scan protein sequences for any given motif(s) and predict cut sites once a suitable cut site prediction model for any other protease has been developed. Pripper is freely available and can be downloaded from http://users.utu.fi/mijopi/Pripper.ConclusionsWe have developed Pripper, a tool for reading an arbitrary number of proteins in FASTA format, predicting their caspase cleavage sites and outputting the cleaved sequences to a new FASTA format sequence file. We show that Pripper is a valuable tool in identifying novel caspase target proteins from modern proteomics experiments.

Project description:Spider neurotoxins are commonly used as pharmacological tools and are a popular source of novel compounds with therapeutic and agrochemical potential. Since venom peptides are inherently toxic, the host spider must employ strategies to avoid adverse effects prior to venom use. It is partly for this reason that most spider toxins encode a protective proregion that upon enzymatic cleavage is excised from the mature peptide. In order to identify the mature toxin sequence directly from toxin transcripts, without resorting to protein sequencing, the propeptide cleavage site in the toxin precursor must be predicted bioinformatically. We evaluated different machine learning strategies (support vector machines, hidden Markov model and decision tree) and developed an algorithm (SpiderP) for prediction of propeptide cleavage sites in spider toxins. Our strategy uses a support vector machine (SVM) framework that combines both local and global sequence information. Our method is superior or comparable to current tools for prediction of propeptide sequences in spider toxins. Evaluation of the SVM method on an independent test set of known toxin sequences yielded 96% sensitivity and 100% specificity. Furthermore, we sequenced five novel peptides (not used to train the final predictor) from the venom of the Australian tarantula Selenotypus plumipes to test the accuracy of the predictor and found 80% sensitivity and 99.6% 8-mer specificity. Finally, we used the predictor together with homology information to predict and characterize seven groups of novel toxins from the deeply sequenced venom gland transcriptome of S. plumipes, which revealed structural complexity and innovations in the evolution of the toxins. The precursor prediction tool (SpiderP) is freely available on ArachnoServer (http://www.arachnoserver.org/spiderP.html), a web portal to a comprehensive relational database of spider toxins. All training data, test data, and scripts used are available from the SpiderP website.

Dataset Information

The VHSE-based prediction of proteasomal cleavage sites.

Publications

The VHSE-based prediction of proteasomal cleavage sites.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets