Abstract
Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein–compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.
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Abbreviations
- MSA:
-
multiple sequence alignment
- MD:
-
molecular dynamics
- RNN:
-
residual neural network
- GNN:
-
graphic neural network
- CNN:
-
convolutional neural network
- DIO:
-
differences between the input and output
- NMR:
-
nuclear magnetic resonance
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Funding
This research was partially supported as a Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant number JP19am0101110.
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Tsuchiya, Y., Tomii, K. Neural networks for protein structure and function prediction and dynamic analysis. Biophys Rev 12, 569–573 (2020). https://doi.org/10.1007/s12551-020-00685-6
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DOI: https://doi.org/10.1007/s12551-020-00685-6