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bioRxiv · 10.1101/2020.06.26.174300

Protein Contact Map Denoising Using Generative Adversarial Networks

Maddhuri Venkata Subramaniya, S. R.·Terashi, G.·Jain, A.·Kagaya, Y.·Kihara, D.
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Abstract

Protein residue-residue contact prediction from protein sequence information has undergone substantial improvement in the past few years, which has made it a critical driving force for building correct protein tertiary structure models. Improving accuracy of contact predictions has, therefore, become the forefront of protein structure prediction. Here, we show a novel contact map denoising method, ContactGAN, which uses Generative Adversarial Networks (GAN) to refine predicted protein contact maps. ContactGAN was able to make a consistent and significant improvement over predictions made by recent contact prediction methods when tested on two datasets including protein structure modeling targets in CASP13. ContactGAN will be a valuable addition in the structure prediction pipeline to achieve an extra gain in contact prediction accuracy.

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Maddhuri Venkata Subramaniya, S. R., Terashi, G., Jain, A., Kagaya, Y., Kihara, D.. 2020-06-27. Protein Contact Map Denoising Using Generative Adversarial Networks. https://doi.org/10.1101/2020.06.26.174300

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