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Biological Chemistry

Editor-in-Chief: Brüne, Bernhard

Editorial Board: Buchner, Johannes / Lei, Ming / Ludwig, Stephan / Thomas, Douglas D. / Turk, Boris / Wittinghofer, Alfred

IMPACT FACTOR 2018: 3.014
5-year IMPACT FACTOR: 3.162

CiteScore 2018: 3.09

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Source Normalized Impact per Paper (SNIP) 2018: 0.820

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Volume 393, Issue 9


Understanding the substrate specificity of conventional calpains

Hiroyuki Sorimachi
  • Corresponding author
  • Calpain Project, Department of Advanced Science for Biomolecules, Tokyo Metropolitan Institute of Medical Science, Tokyo 156-8506, Japan
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Hiroshi Mamitsuka
  • Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Yasuko Ono
  • Calpain Project, Department of Advanced Science for Biomolecules, Tokyo Metropolitan Institute of Medical Science, Tokyo 156-8506, Japan
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar


Calpains are intracellular Ca2+-dependent Cys proteases that play important roles in a wide range of biological phenomena via the limited proteolysis of their substrates. Genetic defects in calpain genes cause lethality and/or functional deficits in many organisms, including humans. Despite their biological importance, the mechanisms underlying the action of calpains, particularly of their substrate specificities, remain largely unknown. Studies show that certain sequence preferences influence calpain substrate recognition, and some properties of amino acids have been related successfully to substrate specificity and to the calpains’ 3D structure. The full spectrum of this substrate specificity, however, has not been clarified using standard sequence analysis algorithms, e.g., the position-specific scoring-matrix method. More advanced bioinformatics techniques were used recently to identify the substrate specificities of calpains and to develop a predictor for calpain cleavage sites, demonstrating the potential of combining empirical data acquisition and machine learning. This review discusses the calpains’ substrate specificities, introducing the benefits of bioinformatics applications. In conclusion, machine learning has led to the development of useful predictors for calpain cleavage sites, although the accuracy of the predictions still needs improvement. Machine learning has also elucidated information about the properties of calpains’ substrate specificities, including a preference for sequences over secondary structures and the existence of a substrate specificity difference between two similar conventional calpains, which has never been indicated biochemically.

Keywords: calpain; multiple kernel learning; PSSM; structure-function relationship; substrate specificity; support vector machine

About the article

Hiroyuki Sorimachi

Hiroyuki Sorimachi was born in Yokohama and started working on calpains in 1988 at the Tokyo Metropolitan Institute of Medical Science (Rinshoken) under the supervision of late Prof. Koichi Suzuki, after receiving his BSc in Biochemistry from the University of Tokyo. In 1992, he moved to the University of Tokyo as a Prof. Suzuki’s assistant professor, and obtained his PhD at the same University. He continued studying calpains, and in 1997 moved to the Graduate School of Agriculture and Life Sciences at the University of Tokyo as an associate professor. In 2004, he returned to Rinshoken, which has been reorganized to Igakuken since 2011, as a project leader of Calpain Project. His research interests include biochemistry and genetics of all kinds of calpains

Hiroshi Mamitsuka

Hiroshi Mamitsuka was born in a small port town near Tokyo, Japan. He obtained his BSc in Biophysics and Biochemistry from the University of Tokyo, while his PhD degree was in Information Sciences from the same university. He joined Kyoto University after working on research in machine learning and data mining with NEC Research Laboratories for more than 10 years. Currently he is a professor at the Bioinformatics Center at the Institute for Chemical Research of Kyoto University and jointly appointed at the Graduate School of Pharmaceutical Sciences of the same university. His research interest is in machine learning and data mining over graphs, particularly biological networks, and is now extended to machine learningbased systems biology.

Yasuko Ono

Yasuko Ono obtained her PhD in Protein Science from the University of Tokyo in 1999. She then explored the research area focusing on muscle-specific calpain (at the University of Tokyo) and other muscle regulatory proteins (at the University of Arizona, USA) as a post-doctoral fellow supported by the Japan Society for the Promotion of Science (JSPS) and the Japan Science and Technology Agency (JST). Since 2004, she has been a researcher in Calpain Project organized by Dr. Hiroyuki Sorimachi in Rinshoken, which is now known as Tokyo Metropolitan Institute of Medical Science (Igakuken). Her research interests include biochemistry and genetics of muscle-specific calpain and PalB-type calpain, and the creation of analytical tools for calpains.

Corresponding author: Hiroyuki Sorimachi, Calpain Project, Department of Advanced Science for Biomolecules, Tokyo Metropolitan Institute of Medical Science, Tokyo 156-8506, Japan

Received: 2012-02-29

Accepted: 2012-07-23

Published in Print: 2012-09-01

Citation Information: , Volume 393, Issue 9, Pages 853–871, ISSN (Online) 1437-4315, ISSN (Print) 1431-6730, DOI: https://doi.org/10.1515/hsz-2012-0143.

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