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Generating antimicrobial peptides using generative adversarial networks.
Poster Title: Generating antimicrobial peptides using generative adversarial networks.
Submitted on 02 Aug 2022
Author(s): Khondamir Rustamov
Affiliations: Fergana State University
This poster was presented at Computational biology and artificial intelligence for personalized medicine-2022
Poster Views: 77
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Poster Information
Abstract: In this study we investigated the performance of unrolled GAN in generating antimicrobial peptides. We estimated our GAN using common machine learning algorithms and by molecular dynamics simulations. In this study we demonstrated that our GAN can generate the high percent of peptides estimated as antimicrobial.Summary: The aim of this study is to investigate the performance of unrolled generative adversarial networks (GAN) in generating antimicrobial peptides (AMP).
References: 1. Pirtskhalava, M.; Gabrielian, A.; Cruz, P.; Griggs, H. L.; Squires, R. B.; Hurt, D. E.; Grigolava, M.; Chubinidze, M.; Gogoladze, G.; Vishnepolsky, B.; et al. DBAASP v.2: an enhanced database of structure and antimicrobial/cytotoxic activity of natural and synthetic peptides. Nucleic Acids Res. 2016, 44, D1104−D1112.
2. Wang, Z.; Wang, G. APD: the antimicrobial peptide database. Nucleic Acids Res. 2004, 32, D590−D592.
3. Waghu FH, Barai RS, Gurung P, Idicula-Thomas S. CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides. Nucleic Acids Res. 2015.
4. Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
5. Müller A. T. et al. (2017) modlAMP: Python for antimicrobial peptides, Bioinformatics 33, (17), 2753-2755.
6. S. Jo, T. Kim, V.G. Iyer, and W. Im (2008) CHARMM-GUI: A Web-based Graphical User Interface for CHARMM. J. Compute. Chem. 29:1859-1865.
7. Abr
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