Sequence-based peptide identification, generation, and property prediction with deep learning: a review-Reference-Cited by-同舟云学术

Sequence-based peptide identification, generation, and property prediction with deep learning: a review

Published:2021 Issue:6 Volume:6 Page:406-428
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Chen Xumin¹²³⁴^ORCID,Li Chen¹²³⁴^ORCID,Bernards Matthew T.⁵⁶⁷⁸^ORCID,Shi Yao¹²³⁴⁹^ORCID,Shao Qing¹⁰¹¹¹²⁸^ORCID,He Yi¹²³⁴¹³^ORCID

Affiliation:

1. College of Chemical and Biological Engineering

2. Zhejiang University

3. Hangzhou

4. PR China

5. Department of Chemical and Biological Engineering

6. University of Idaho

7. Moscow 83844

8. USA

9. Key Laboratory of Biomass Chemical Engineering of Ministry of Education

10. Chemical and Materials Engineering Department

11. University of Kentucky

12. Lexington

13. Department of Chemical Engineering

Abstract

This article reviews recent work that uses deep learning algorithms to identify and generate functional peptides as well as predict their biological properties.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2021/ME/D0ME00161A

Reference198 articles.

1. De novo design of picomolar SARS-CoV-2 miniprotein inhibitors

2. Generation and use of synthetic peptide combinatorial libraries for basic research and drug discovery

3. A new type of synthetic peptide library for identifying ligand-binding activity

4. Protein Design by Directed Evolution

5. Machine learning in bioinformatics

Cited by 25 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advances in the application of machine learning to boiling heat transfer: A review;International Journal of Heat and Fluid Flow;2024-09

2. Attribute-guided prototype network for few-shot molecular property prediction;Briefings in Bioinformatics;2024-07-25

3. Combinations of Analytical and Machine Learning Methods in a Single Simulation Framework for Amphoteric Molecules Detection;IEEE Sensors Letters;2024-07

4. HELM-GPT: de novo macrocyclic peptide design using generative pre-trained transformer;Bioinformatics;2024-06

5. Molecular strategies to enhance the keratinase gene expression and its potential implications in poultry feed industry;Poultry Science;2024-05