ABSTRACT
Computational drug design has the potential to save time and money by providing a better starting point for new drugs with a complete computational evaluation. We propose a peptide design system for protein targets based on a Generative Adversarial Network (GAN) called GANDALF (Generative Adversarial Network Drug-tArget Ligand Fructifier). GAN based methods have been developed for computational drug design but these can only generate small molecules, not peptides. Peptides are very complex macromolecules which makes them much more difficult than small molecules to generate. Our GANDALF methodology uses two networks to generate a new peptide sequence and structure. It also incorporates data such as active atoms not used in other methods. Active atoms are important because they interact via electron sharing when a target protein and a peptide bind to each other. We can identify the active atoms using our electron structure calculation (eCADD) program and the rules of interaction we have developed. Our method goes farther than comparable methods by generating a full peptide structure as well as predicting binding affinity. The results were validated using a multi-step process comparing the results with FDA approved drugs and our initial prototype method. We have generated multiple peptides for three targets of interest (PD-1, PDL-1, and CTLA-4) and have found that the best generated peptide for each target was comparable to the FDA approved drugs in binding affinity and fitness of 3D binding as well as show the generated peptides were unique from the existing FDA drugs.
- Rolf Apweiler, Amos Bairoch, Cathy H Wu, Winona C Barker, Brigitte Boeckmann, Serenella Ferro, Elisabeth Gasteiger, Hongzhan Huang, Rodrigo Lopez, Michele Magrane, et al. 2004. UniProt: the universal protein knowledgebase. Nucleic acids research 32, suppl_1 (2004), D115--D119.Google Scholar
- Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).Google Scholar
- Helen M Berman, John Westbrook, Zukang Feng, Gary Gilliland, Talapady N Bhat, Helge Weissig, Ilya N Shindyalov, and Philip E Bourne. 2000. The protein data bank. Nucleic acids research 28, 1 (2000), 235--242.Google Scholar
- Christies. 2018. Is artificial intelligence set to become art's next medium? https://www.christies.com/features/A-collaboration-between-two-artists- one-human-one-a-machine-9332-1.aspxGoogle Scholar
- Sander Dieleman, Aaron van den Oord, and Karen Simonyan. 2018. The challenge of realistic music generation: modelling raw audio at scale. In Advances in Neural Information Processing Systems. 7989--7999.Google Scholar
- Joseph A DiMasi, Henry G Grabowski, and Ronald W Hansen. 2016. Innovation in the pharmaceutical industry: new estimates of R&D costs. Journal of health economics 47 (2016), 20--33.Google ScholarCross Ref
- Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang. 2018. MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In Thirty-Second AAAI Conference on Artificial Intelligence.Google Scholar
- Joe Dundas, Zheng Ouyang, Jeffery Tseng, Andrew Binkowski, Yaron Turpaz, and Jie Liang. 2006. CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic acids research 34, suppl_2 (2006), W116-W118.Google Scholar
- US Food, Drug Administration, et al. 2016. Drugs@ FDA: FDA approved drug products.Google Scholar
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672--2680.Google Scholar
- Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang. 2018. Long text generation via adversarial training with leaked information. In Thirty-Second AAAI Conference on Artificial Intelligence.Google Scholar
- Axel Hoos. 2016. Development of immuno-oncology drugs---from CTLA4 to PD1 to the next generations. Nature reviews Drug discovery 15, 4 (2016), 235.Google Scholar
- Ferenc Huszár. 2015. How (not) to train your generative model: Scheduled sampling, likelihood, adversary? arXiv preprint arXiv:1511.05101 (2015).Google Scholar
- Brian Jiménez-García, Carles Pons, and Juan Fernández-Recio. 2013. pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring. Bioinformatics 29, 13 (2013), 1698--1699.Google ScholarCross Ref
- Naman Kohli, Daksha Yadav, Mayank Vatsa, Richa Singh, and Afzel Noore. 2017. Synthetic iris presentation attack using iDCGAN. In Biometrics (IJCB), 2017 IEEE International Joint Conference on. IEEE, 674--680.Google ScholarCross Ref
- Colin Lea, Rene Vidal, Austin Reiter, and Gregory D Hager. 2016. Temporal convolutional networks: A unified approach to action segmentation. In European Conference on Computer Vision. Springer, 47--54.Google ScholarCross Ref
- Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, and Jan M Kratochvil. 2019. CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks. Computational Astrophysics and Cosmology 6, 1 (2019), 1.Google ScholarCross Ref
- Augustus Odena, Christopher Olah, and Jonathon Shlens. 2017. Conditional image synthesis with auxiliary classifier gans. In Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2642--2651.Google ScholarDigital Library
- Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016).Google Scholar
- Anton Osokin, Anatole Chessel, Rafael E Carazo Salas, and Federico Vaggi. 2017. GANs for biological image synthesis. In Proceedings of the IEEE International Conference on Computer Vision. 2233--2242.Google ScholarCross Ref
- Santiago Pascual, Antonio Bonafonte, and Joan Serra. 2017. SEGAN: Speech enhancement generative adversarial network. arXiv preprint arXiv:1703.09452 (2017).Google Scholar
- Peter W Rose, Bojan Beran, Chunxiao Bi, Wolfgang F Bluhm, Dimitris Dimitropoulos, David S Goodsell, Andreas Prlić, Martha Quesada, Gregory B Quinn, John D Westbrook, et al. 2010. The RCSB Protein Data Bank: redesigned web site and web services. Nucleic acids research 39, suppl_1 (2010), D392--D401.Google Scholar
- Allison M Rossetto, Wenjin Zhou, Xiaodong Pang, and Linxiang Zhou. 2014. The full electron structure of the FKBP12/FK506 complex. Journal of Biomolecular Structure and Dynamics 33, 2 (2014), 388--394.Google ScholarCross Ref
- Aliaksandr Siarohin, Enver Sangineto, Stéphane Lathuilière, and Nicu Sebe. 2018. Deformable gans for pose-based human image generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 3408--3416.Google ScholarCross Ref
- Fabian Sievers, Andreas Wilm, David Dineen, Toby J Gibson, Kevin Karplus, Weizhong Li, Rodrigo Lopez, Hamish McWilliam, Michael Remmert, Johannes Söding, et al. 2011. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Molecular systems biology 7, 1 (2011).Google Scholar
- Marta M Stepniewska-Dziubinska, Piotr Zielenkiewicz, and Pawel Siedlecki. 2018. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics 1 (2018), 9.Google Scholar
- Lin Sun. 2018. Deeply learned representations for human action recognition.Google Scholar
- Lin Sun, Kui Jia, Dit-Yan Yeung, and Bertram E Shi. 2015. Human action recognition using factorized spatio-temporal convolutional networks. In Proceedings of the IEEE international conference on computer vision. 4597--4605.Google ScholarDigital Library
- Yaniv Taigman, Adam Polyak, and Lior Wolf. 2016. Unsupervised cross-domain image generation. arXiv preprint arXiv:1611.02200 (2016).Google Scholar
- Wei Ren Tan, Chee Seng Chan, Hernán E Aguirre, and Kiyoshi Tanaka. 2017. Art-GAN: Artwork synthesis with conditional categorical GANs. In Image Processing (ICIP), 2017 IEEE International Conference on. IEEE, 3760--3764.Google ScholarCross Ref
- Salman Sadullah Usmani, Gursimran Bedi, Jesse S Samuel, Sandeep Singh, Sourav Kalra, Pawan Kumar, Anjuman Arora Ahuja, Meenu Sharma, Ankur Gautam, and Gajendra PS Raghava. 2017. THPdb: Database of FDA-approved peptide and protein therapeutics. PloS one 12, 7 (2017), e0181748.Google ScholarCross Ref
- Konstantinos Vougioukas, Stavros Petridis, and Maja Pantic. 2018. End-to-end speech-driven facial animation with temporal gans. arXiv preprint arXiv:1805.09313 (2018).Google Scholar
- Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. 2018. High-resolution image synthesis and semantic manipulation with conditional gans. In Proceedings of the IEEE conference on computer vision and pattern recognition. 8798--8807.Google ScholarCross Ref
- Jingjing Xu, Xuancheng Ren, Junyang Lin, and Xu Sun. 2018. Diversity-Promoting GAN: A Cross-Entropy Based Generative Adversarial Network for Diversified Text Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 3940--3949. https://doi.org/10.18653/v1/D18--1428Google ScholarCross Ref
- Jingjing Xu, Xuancheng Ren, Junyang Lin, and Xu Sun. 2018. DP-GAN: diversity-promoting generative adversarial network for generating informative and diversified text. arXiv preprint arXiv.1802.01345 (2018).Google Scholar
- Li-Chia Yang, Szu-Yu Chou, and Yi-Hsuan Yang. 2017. MidiNet: A convolutional generative adversarial network for symbolic-domain music generation. arXiv preprint arXiv:1703.10847 (2017).Google Scholar
- Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. Seqgan: Sequence generative adversarial nets with policy gradient. In Thirty-First AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- Wenjin Zhou, Allison M Rossetto, Xiaodong Pang, and Linxiang Zhou. 2015. Computational full electron structure study of biological activity in Cyclophilin A. Journal of Biomolecular Structure and Dynamics (2015), 1--7.Google Scholar
Index Terms
- GANDALF: Peptide Generation for Drug Design using Sequential and Structural Generative Adversarial Networks
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