ICar-PseCp: Identify Carbonylation Sites in Proteins by Monte Carlo Sampling and Incorporating Sequence Coupled Effects into General PseAAC

Overview

Journal Oncotarget

Specialty Oncology

Date 2016 May 7

PMID 27153555

Citations 47

Authors

Jianhua Jia

Zi Liu

Xuan Xiao

Bingxiang Liu

Kuo-Chen Chou

Affiliations

Soon will be listed here.

Abstract

Carbonylation is a posttranslational modification (PTM or PTLM), where a carbonyl group is added to lysine (K), proline (P), arginine (R), and threonine (T) residue of a protein molecule. Carbonylation plays an important role in orchestrating various biological processes but it is also associated with many diseases such as diabetes, chronic lung disease, Parkinson's disease, Alzheimer's disease, chronic renal failure, and sepsis. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of K, P, R, or T, which ones can be carbonylated, and which ones cannot? To address this problem, we have developed a predictor called iCar-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition, and balancing out skewed training dataset by Monte Carlo sampling to expand positive subset. Rigorous target cross-validations on a same set of carbonylation-known proteins indicated that the new predictor remarkably outperformed its existing counterparts. For the convenience of most experimental scientists, a user-friendly web-server for iCar-PseCp has been established at http://www.jci-bioinfo.cn/iCar-PseCp, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the formulation and approach presented here can also be used to analyze many other problems in computational proteomics.

Citing Articles

A novel two-way rebalancing strategy for identifying carbonylation sites.

Chen L, Jing X, Hao Y, Liu W, Zhu X, Han W BMC Bioinformatics. 2023; 24(1):429.

PMID: 37957582 PMC: 10644465. DOI: 10.1186/s12859-023-05551-2.

In silico prediction of post-translational modifications in therapeutic antibodies.

Vatsa S MAbs. 2022; 14(1):2023938.

PMID: 35040751 PMC: 8791605. DOI: 10.1080/19420862.2021.2023938.

CarSite-II: an integrated classification algorithm for identifying carbonylated sites based on K-means similarity-based undersampling and synthetic minority oversampling techniques.

Zuo Y, Lin J, Zeng X, Zou Q, Liu X BMC Bioinformatics. 2021; 22(1):216.

PMID: 33902446 PMC: 8077735. DOI: 10.1186/s12859-021-04134-3.

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.

Ahmad W, Arafat E, Taherzadeh G, Sharma A, Dipta S, Dehzangi A IEEE Access. 2020; 8:77888-77902.

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iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments Chou's 5-steps Rule and Pseudo Components.

Barukab O, Khan Y, Khan S, Chou K Curr Genomics. 2020; 20(4):306-320.

PMID: 32030089 PMC: 6983959. DOI: 10.2174/1389202920666190819091609.

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