BIO Web Conf.
Volume 41, 2021The 4th International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering (BioMIC 2021)
|Number of page(s)||7|
|Section||Bioinformatics and Data Mining|
|Published online||22 December 2021|
A Review of Protein Structure Prediction using Deep Learning
Universitas Pertamina, Jl Teuku Nyak Arief Simprug, Kebayoran Lama Jakarta Selatan 12220, Indonesia
2 Institut Teknologi Bandung, Jl. Ganesa No.10 Lb. Siliwangi Coblong Bandung, Jawa Barat 40132, Indonesia
* Corresponding author: email@example.com
Proteins are macromolecules composed of 20 types of amino acids in a specific order. Understanding how proteins fold is vital because its 3-dimensional structure determines the function of a protein. Prediction of protein structure based on amino acid strands and evolutionary information becomes the basis for other studies such as predicting the function, property or behaviour of a protein and modifying or designing new proteins to perform certain desired functions. Machine learning advances, particularly deep learning, are igniting a paradigm shift in scientific study. In this review, we summarize recent work in applying deep learning techniques to tackle problems in protein structural prediction. We discuss various deep learning approaches used to predict protein structure and future achievements and challenges. This review is expected to help provide perspectives on problems in biochemistry that can take advantage of the deep learning approach. Some of the unanswered challenges with current computational approaches are predicting the location and precision orientation of protein side chains, predicting protein interactions with DNA, RNA and other small molecules and predicting the structure of protein complexes.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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