Evidence map›Paper›PMID 29860277›Full record

ArticleBriefings in bioinformatics2019

Toward more accurate prediction of caspase cleavage sites: a comprehensive review of current methods, tools and features.

Yu Bao, Simone Marini, Takeyuki Tamura, Mayumi Kamada, Shingo Maegawa, Hiroshi Hosokawa, Jiangning Song, Tatsuya Akutsu

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
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  7. Review
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yu BaoBioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.
Simone MariniDepartment of Computational Medicine and Bioinformatics, University of Michigan, 1241 E. Catherine St., 5940 Buhl, Ann Arbor 48109-5618, USA.
Takeyuki TamuraBioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.
Mayumi KamadaGraduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto 606-8507, Japan.
Shingo MaegawaGraduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
Hiroshi HosokawaGraduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
Jiangning SongMonash Biomedicine Discovery Institute, Monash Centre for Data Science and ARC Centre of Excellence in Advance Molecular Imaging, Monash University, Melbourne, VIC 3800, Australia.
Tatsuya AkutsuBioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan.

Funding

New Tricks for 'Old' Drugs: PK/PD of Polymyxin Nonantibiotic CombinationsR01AI111965 · NIAID · MONASH UNIVERSITY · PI LI, JIAN · 2014 to 2018
$4.3M
NIAID NIH HHS R01 AI111965
6 · The paper itself

Abstract

As one of the few irreversible protein posttranslational modifications, proteolytic cleavage is involved in nearly all aspects of cellular activities, ranging from gene regulation to cell life-cycle regulation. Among the various protease-specific types of proteolytic cleavage, cleavages by casapses/granzyme B are considered as essential in the initiation and execution of programmed cell death and inflammation processes. Although a number of substrates for both types of proteolytic cleavage have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remains to be fully characterized. To tackle this issue and complement experimental efforts for substrate identification, systematic bioinformatics studies of known cleavage sites provide important insights into caspase/granzyme B substrate specificity, and facilitate the discovery of novel substrates. In this article, we review and benchmark 12 state-of-the-art sequence-based bioinformatics approaches and tools for caspases/granzyme B cleavage prediction. We evaluate and compare these methods in terms of their input/output, algorithms used, prediction performance, validation methods and software availability and utility. In addition, we construct independent data sets consisting of caspases/granzyme B substrates from different species and accordingly assess the predictive power of these different predictors for the identification of cleavage sites. We find that the prediction results are highly variable among different predictors. Furthermore, we experimentally validate the predictions of a case study by performing caspase cleavage assay. We anticipate that this comprehensive review and survey analysis will provide an insightful resource for biologists and bioinformaticians who are interested in using and/or developing tools for caspase/granzyme B cleavage prediction.

Indexed as

CaspasesHumansProteolysisSubstrate SpecificityCaspasescaspasecleavage sitesprediction tool

Identifiers

PMID29860277
PMCPMC6917222

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.