Theoretical and Natural Science

- The Open Access Proceedings Series for Conferences


Theoretical and Natural Science

Vol. 6, 03 August 2023


Open Access | Article

Protein prediction algorithms: homology modeling, AlphaFold, and Foldit

Songhan Duan 1 , Runcheng Ke 2 , Junyi Xiang * 3
1 Jilin University
2 No.8 Middle School of Beijing
3 Shanghai World Foreign Language Academy

* Author to whom correspondence should be addressed.

Theoretical and Natural Science, Vol. 6, 306-310
Published 03 August 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Songhan Duan, Runcheng Ke, Junyi Xiang. Protein prediction algorithms: homology modeling, AlphaFold, and Foldit. TNS (2023) Vol. 6: 306-310. DOI: 10.54254/2753-8818/6/20230263.

Abstract

Protein, one of the most basic structures of biological molecules, have its own four level structure that corresponds with its function. The structures make every protein unique and diverse. Studies of protein must be based on the understanding on protein's structure. Thus, methods must be applied to predict the protein structure. Old methods include homology modeling that are both expensive and time consuming. With the development of modern technology, new methods such as Foldit and AlphaFold was invented. The report would introduce these methods and comparisons would be made between these methods.The introduction aims to improve the understanding about protein prediction for relative researchers.

Keywords

protein structures, algorithms, homology modeling, alphaFold, FOLDIT

References

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the International Conference on Modern Medicine and Global Health (ICMMGH 2023)
ISBN (Print)
978-1-915371-65-2
ISBN (Online)
978-1-915371-66-9
Published Date
03 August 2023
Series
Theoretical and Natural Science
ISSN (Print)
2753-8818
ISSN (Online)
2753-8826
DOI
10.54254/2753-8818/6/20230263
Copyright
03 August 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated