Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning

Jingkai Wen

2022

Abstract

As the speed of finding new proteins exceeds that of structural analysis, traditional experimental ways are time-consuming and cannot meet the need to decipher the structure of proteins in a relatively short time, which leads to the appearance of protein structure prediction. Protein structure prediction uses deposited protein structures to predict the newfound and has developed fast with the increase of computational resources and the refinement of algorithms. This review introduces protein structure prediction based on machine learning, including sequence encoding and feature extraction. After that, we focus on deep learning and interpret several common methods used in deep learning algorithms, including sequence alignment, residues contact profile. Finally, we introduce several representative algorithms and their methods.

Paper Citation

in Harvard Style

Wen J. (2022). Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning. In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB, ISBN 978-989-758-595-1, pages 308-315. DOI: 10.5220/0011203900003443

in Bibtex Style

@conference{icbeb22,
author={Jingkai Wen},
title={Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning},
booktitle={Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,},
year={2022},
pages={308-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011203900003443},
isbn={978-989-758-595-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics - Volume 1: ICBEB,
TI - Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning
SN - 978-989-758-595-1
AU - Wen J.
PY - 2022
SP - 308
EP - 315
DO - 10.5220/0011203900003443