Predicting the conformations of the silk protein through deep learning-Reference-Cited by-同舟云学术

Predicting the conformations of the silk protein through deep learning

Published:2021 Issue:8 Volume:146 Page:2490-2498
ISSN:0003-2654
Container-title:The Analyst
language:en
Short-container-title:Analyst

Author:

Jiang Mingrui¹²³⁴,Shu Ting¹²³⁴,Ye Chao¹²³⁴,Ren Jing¹²³⁴,Ling Shengjie¹²³⁴^ORCID

Affiliation:

1. School of Physical Science and Technology

2. ShanghaiTech University

3. Shanghai

4. China

Abstract

Deep learning models are developed to identify the silk proteins and evaluate their relative content of each conformation from FTIR spectra. These models are highly accurate and time-efficient and promise to process massive FTIR data sets.

Funder

National Natural Science Foundation of China

Hefei Science Center, Chinese Academy of Sciences

ShanghaiTech University

State Key Laboratory for Modification of Chemical Fibers and Polymer Materials

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrochemistry,Spectroscopy,Environmental Chemistry,Biochemistry,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/AN/D1AN00290B

Reference32 articles.

1. New Opportunities for an Ancient Material

2. Nanofibrils in nature and materials engineering

3. Design, Fabrication, and Function of Silk‐Based Nanomaterials

4. Understanding Secondary Structures of Silk Materials via Micro- and Nano-Infrared Spectroscopies

5. Analysis of the Structure of Bombyx mori Silk Fibroin by NMR

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