Literature DB >> 30576418

Research progress in protein posttranslational modification site prediction.

Wenying He1, Leyi Wei1, Quan Zou1,2.   

Abstract

Posttranslational modifications (PTMs) play an important role in regulating protein folding, activity and function and are involved in almost all cellular processes. Identification of PTMs of proteins is the basis for elucidating the mechanisms of cell biology and disease treatments. Compared with the laboriousness of equivalent experimental work, PTM prediction using various machine-learning methods can provide accurate, simple and rapid research solutions and generate valuable information for further laboratory studies. In this review, we manually curate most of the bioinformatics tools published since 2008. We also summarize the approaches for predicting ubiquitination sites and glycosylation sites. Moreover, we discuss the challenges of current PTM bioinformatics tools and look forward to future research possibilities.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords:  glycosylation; machine learning; posttranslational modification; ubiquitination

Mesh:

Substances:

Year:  2018        PMID: 30576418     DOI: 10.1093/bfgp/ely039

Source DB:  PubMed          Journal:  Brief Funct Genomics        ISSN: 2041-2649            Impact factor:   4.241


  8 in total

1.  PseAraUbi: predicting arabidopsis ubiquitination sites by incorporating the physico-chemical and structural features.

Authors:  Wei Wang; Yu Zhang; Dong Liu; HongJun Zhang; XianFang Wang; Yun Zhou
Journal:  Plant Mol Biol       Date:  2022-07-01       Impact factor: 4.335

Review 2.  Discovering the landscape of protein modifications.

Authors:  E Keith Keenan; Derek K Zachman; Matthew D Hirschey
Journal:  Mol Cell       Date:  2021-04-01       Impact factor: 17.970

3.  6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning.

Authors:  Qianfei Huang; Wenyang Zhou; Fei Guo; Lei Xu; Lichao Zhang
Journal:  PeerJ       Date:  2021-02-03       Impact factor: 2.984

Review 4.  Current methodologies in protein ubiquitination characterization: from ubiquitinated protein to ubiquitin chain architecture.

Authors:  Mingwei Sun; Xiaofei Zhang
Journal:  Cell Biosci       Date:  2022-08-12       Impact factor: 9.584

5.  Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs.

Authors:  Neeladri Sen; Ivan Anishchenko; Nicola Bordin; Ian Sillitoe; Sameer Velankar; David Baker; Christine Orengo
Journal:  Brief Bioinform       Date:  2022-07-18       Impact factor: 13.994

6.  An analytical study on the identification of N-linked glycosylation sites using machine learning model.

Authors:  Muhammad Aizaz Akmal; Muhammad Awais Hassan; Shoaib Muhammad; Khaldoon S Khurshid; Abdullah Mohamed
Journal:  PeerJ Comput Sci       Date:  2022-09-21

Review 7.  Glycosylation in Axonal Guidance.

Authors:  Sampada P Mutalik; Stephanie L Gupton
Journal:  Int J Mol Sci       Date:  2021-05-13       Impact factor: 5.923

Review 8.  ADP-Ribosylation Post-Translational Modification: An Overview with a Focus on RNA Biology and New Pharmacological Perspectives.

Authors:  Giuseppe Manco; Giuseppina Lacerra; Elena Porzio; Giuliana Catara
Journal:  Biomolecules       Date:  2022-03-13
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.