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. 2019 Aug 8;10:715.
doi: 10.3389/fgene.2019.00715. eCollection 2019.

Precise Prediction of Calpain Cleavage Sites and Their Aberrance Caused by Mutations in Cancer

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Free PMC article

Precise Prediction of Calpain Cleavage Sites and Their Aberrance Caused by Mutations in Cancer

Ze-Xian Liu et al. Front Genet. .
Free PMC article

Abstract

As a widespread post-translational modification of proteins, calpain-mediated cleavage regulates a broad range of cellular processes, including proliferation, differentiation, cytoskeletal reorganization, and apoptosis. The identification of proteins that undergo calpain cleavage in a site-specific manner is the necessary foundation for understanding the exact molecular mechanisms and regulatory roles of calpain-mediated cleavage. In contrast with time-consuming and labor-intensive experimental methods, computational approaches for detecting calpain cleavage sites have attracted wide attention due to their efficiency and convenience. In this study, we established a novel computational tool named DeepCalpain (http://deepcalpain.cancerbio.info/) for predicting the potential calpain cleavage sites by adopting deep neural network and the particle swarm optimization algorithm. Through critical evaluation and comparison, DeepCalpain exhibited superior performance against other existing tools. Meanwhile, we found that protein interactions could enrich the calpain-substrate regulatory relationship. Since calpain-mediated cleavage was critical for cancer development and progression, we comprehensively analyzed the calpain cleavage associated mutations across 11 cancers with the help of DeepCalpain, which demonstrated that the calpain-mediated cleavage events were affected by mutations and heavily implicated in the regulation of cancer cells. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of calpain cleavage in biological pathways and different cancer types, which might open new avenues for the diagnosis and treatment of cancers.

Keywords: calpain; cancer mutation; cleavage site; deep learning; prediction.

Figures

Figure 1
Figure 1
Overall methodology. Highlighted are experimentally identified calpain cleavage sites extracted from PubMed by text mining; multi-network deep-learning and PSO algorithm; and the integrative analysis of the connections between calpain-mediated cleavage and cancers.
Figure 2
Figure 2
(A) The preferences for the amino acids around the calpain cleavage sites and non-calpain cleavage sites. (B–C) Comparison of the surface accessibility (B) and disorder information between calpain cleavage sites and non-calpain cleavage sites. (D) The 4-, 6-, 8-, and 10-fold cross-validations results for calpain. (E–G) Comparison of the models of calpain (E), µ-calpain (F), and m-calpain (G) with the existing tools, and the dots were the Sn and Sp values adopted from the related literatures.
Figure 3
Figure 3
Connections between calpain cleavage sites and genetic variants. (A) Mutations preferentially occur at the regions around calpain cleavage sites. (B) Calpains were differential expressed across cancers. (C) Summary of the distribution of missense variations across cancers. (D) Summary of proteins cleavage aberrant triggered by mutations. (E) Summary of mutation site types across cancers.
Figure 4
Figure 4
Systematic analysis of the impact of calpain cleavage–related mutation sites and proteins. (A) CCRM proteins were significantly enriched in cancer-related pathways. (B) CCRM sites were more conserved than other mutations. (C) CCRM sites showed a preference to be enriched in known functional domain regions. (D–E) Patients with at least six CCRM sites had significantly worse clinical prognostic in HNSC (D) and LIHC (E).

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