UNLEASH THE POTENTIAL OF GAN MODEL TO GENERATE SYNTHETIC PROTEIN SEQUENCES

UNLEASH THE POTENTIAL OF GAN MODEL TO GENERATE SYNTHETIC PROTEIN SEQUENCES | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 17 January 2025 V1 Latest version Share on UNLEASH THE POTENTIAL OF GAN MODEL TO GENERATE SYNTHETIC PROTEIN SEQUENCES Author : m Authors Info & Affiliations https://doi.org/10.22541/au.173713534.49587019/v1 321 views 133 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Motivation: Developing a de facto method to generate synthetic protein sequences is a challenging task that ensures confidence in protein engineering, provides functional insights, and aids in target identification. We highlight the protein sequence design, focusing on optimizing the synthetic sequences and their validity by encouraging the immense potential in various domains. Methods: In this framework, first we create the Ramachandran plot, which has been designed to identify the conformational region space for protein. Then, the model is tailored to generate synthetic proteins using Bernoulli data distribution that preserve essential structural or sequence features to ensure the diversity in the training data set. The adversarial loss is evaluated to differentiate between generator-discriminator optimizations. The probability of a protein being real or synthetic is encouraged by the FID score. Results: Our experimental results demonstrate the potential of a GAN model for protein data augmentation to expand the datasets significantly. The model generalizes to a native approach; we evaluate the discriminator loss and real sequences loss as decreasing abruptly, while generator loss and real sequences loss decrease for a while. FID score along with pre-trained model BERT, scores from 91.8821079 to 0.4509833. Our findings demonstrate the tractability and improve the dataset for protein sequence design. Graphical Abstract: Supplementary Material File (protein_sequence_generate.pdf) Download 1017.78 KB Information & Authors Information Version history V1 Version 1 17 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords deep learning discriminator loss gan generator loss protein sequences Authors Affiliations m View all articles by this author Metrics & Citations Metrics Article Usage 321 views 133 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation m. UNLEASH THE POTENTIAL OF GAN MODEL TO GENERATE SYNTHETIC PROTEIN SEQUENCES. Authorea . 17 January 2025. DOI: https://doi.org/10.22541/au.173713534.49587019/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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