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CaSPredictor specifications


Unique identifier OMICS_11635
Name CaSPredictor
Alternative name Caspase Substrate Predictor
Software type Package/Module
Interface Graphical user interface
Restrictions to use None
Input data Protein sequences ranging from 50 to 2000 amino acids
Input format FASTA
Output data An output file (table text format) shows the candidate cleavage sites, the numerical amino acid positions and the scores in a decreasing order.
Output format Table text format
Operating system Windows
Programming languages Visual Basic
Computer skills Medium
Stability Stable
Maintained Yes


No version available


  • person_outline J. E. Belizário

Publication for Caspase Substrate Predictor

CaSPredictor citations


Knowledge transfer learning for prediction of matrix metalloprotease substrate cleavage sites

Sci Rep
PMCID: 5515926
PMID: 28720874
DOI: 10.1038/s41598-017-06219-7

[…] s well, but tend to focus on prediction of substrate-cleavage sites for specific families or types of proteases. For example, several computational methods such as Cascleave (v1.0 and v2.0), Pripper, CasPredictor, GrasBCas, CASVM, and PCCS, were developed to predict the cleavage sites of caspases and/or granzyme B. Consequently, the accurate prediction of diverse MMP substrate-cleavage sites remai […]


PROSPER: An Integrated Feature Based Tool for Predicting Protease Substrate Cleavage Sites

PLoS One
PMCID: 3510211
PMID: 23209700
DOI: 10.1371/journal.pone.0050300

[…] les based on the distribution of positive and negative samples and building empirical scoring functions to discriminate between the two classes. Tools falling in this category include PeptideCutter , CasPredictor , GraBCas , PoPS and SitePrediction . These methods usually either calculate a frequency score for the positions surrounding a potential cleavage site or use a similarity score based on […]


Prediction of Protein Cleavage Site with Feature Selection by Random Forest

PLoS One
PMCID: 3445488
PMID: 23029276
DOI: 10.1371/journal.pone.0045854

[…] s mentioned above, most other tools are proteinase specific, such as GPS-CCD for the prediction of calpain cleavage sites , GraBCas for the prediction of sites cleaved by granzyme B and caspases and CaSPredictor for caspase substrate prediction . Therefore, these tools are limited in applicability. In this work, we developed a novel predictor based on Random Forest algorithm (RF) using maximum re […]


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

BMC Bioinformatics
PMCID: 3324375
PMID: 22269041
DOI: 10.1186/1471-2105-13-14

[…] GraBCas [] is a tool that uses a position specific scoring matrix for caspases 1-9 and granzyme B, based on substrate specificities that were determined by positional scanning of synthetic peptides. CaSPredictor [] was developed based on the assumption that sequences rich in the amino acids Ser (S), Thr (T), Pro (P), Glu or Asp (D/E) (collectively called PEST) are favoured caspase cleavage sites. […]


A Sequence and Structure Based Method to Predict Putative Substrates, Functions and Regulatory Networks of Endo Proteases

PLoS One
PMCID: 2683571
PMID: 19492082
DOI: 10.1371/journal.pone.0005700

[…] While one would expect computational approaches to be an integral part of such investigation, very few in silico methods are currently available for the prediction of natural substrates of proteases. CaSPredictor for the prediction of caspase substrates , GraBCas for Granzyme B and caspase substrates are notable among them. These tools are classifiers designed for high accuracy and are based on k […]


SVM based prediction of caspase substrate cleavage sites

BMC Bioinformatics
PMCID: 1764470
PMID: 17254298
DOI: 10.1186/1471-2105-7-S5-S14
call_split See protocol

[…] As the CasPredictor method is unavailable from the published website, it was not tested. The performance of GrabCas was compared with the SVM method using the current datasets. As the GraBCas scoring matrice […]


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CaSPredictor institution(s)
Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
CaSPredictor funding source(s)
This work was supported by grants from FAPESP (proc. 01/01000-7, 02/09990-0) and CNPq.

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