Feedback GAN for DNA optimizes protein functions

Gupta, Anvita; Zou, James

doi:10.1038/s42256-019-0017-4

Cited by 158 publications

(163 citation statements)

References 18 publications

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“…This requires the distribution of actives to be fixed beforehand. Another approach is to use the same kind of strategy in an iterated way [28], fine-tuning a generative model on its most successful outputs. Other approaches of this kind with better statistical grounding exist [29,30], but we are unaware of their application to molecule generation in this setting.…”

Section: Molecular Optimizationmentioning

confidence: 99%

OptiMol : Optimization of binding affinities in chemical space for drug discovery

Boitreaud

Mallet

Oliver

et al. 2020

Preprint

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Ligand-based drug design has recently benefited from the boost of deep generative models. These models enable extensive explorations of the chemical space, and provide a platform for molecular optimization. However, current state of the art methods do not leverage the structure of the target, which is known to play a key role in the interaction. We propose an optimization pipeline that leverages complementary structure-based and ligand-based methods. Instead of performing docking on a fixed drug bank, we iteratively select promising compounds in the whole chemical space using a ligand-centered generative model. Molecular docking is then used as an oracle to guide compound optimization. This allows to iteratively generate leads that better fit the target structure, in a closed optimization loop, without prior knowledge about bio-actives. For this purpose, we introduce a new graph to selfies VAE which is competitive with the state of the art and benefits from faster decoding than graph to graph methods. We also successfully optimize the generation of molecules towards high docking scores, enabling a ten-fold augmentation of high-scoring compounds found with a fixed computational budget.

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Section: Molecular Optimizationmentioning

confidence: 99%

OptiMol : Optimization of binding affinities in chemical space for drug discovery

Boitreaud

Mallet

Oliver

et al. 2020

Preprint

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“…Implementing the ProteinSeqGAN in generating the whole viral protein sequences is a promising extension, which may be more effective and comprehensive in predicting whole genome viral mutations. Besides, the framework can be applied to not only viral antigen generation but also predicting other sequences, like RNA or DNA chains [55].…”

Section: Broader Impactmentioning

confidence: 99%

Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction

Wang

Yadav

Magar

et al. 2020

Preprint

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Viral pandemics are emerging as a serious global threat to public health, like the recent outbreak of COVID-19. Viruses, especially those belonging to a large family of +ssRNA viruses, have a high possibility of mutating by inserting, deleting, or substituting one or multiple genome segments. It is of great importance for human health worldwide to predict the possible virus mutations, which can effectively avoid the potential second outbreak. In this work, we develop a GAN-based multi-class protein sequence generative model, named ProteinSeqGAN. Given the viral species, the generator is modeled on RNNs to predict the corresponding antigen epitope sequences synthesized by viral genomes. Additionally, a Graphical Protein Autoencoder (GProAE) built upon VAE is proposed to featurize proteins bioinformatically. GProAE, as a multi-class discriminator, also learns to evaluate the goodness of protein sequences and predict the corresponding viral species. Further experiments show that our ProteinSeqGAN model can generate valid antigen protein sequences from both bioinformatics and statistics perspectives, which can be promising predictions of virus mutations.

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“…Deep learning offers one route to better capture the complex relationships between sequence and protein behavior and has been the focus of many recent publications. [38][39][40][41][42] Within the context of discovery and libraries, the generative models such as Generative Adversarial Networks (GANs) 43,44 and autoencoder networks (AEs) 45 are of particular interest as they have been shown to be viable for generating unique sequences of proteins 46,47 and nanobodies 48 and antibody CDRs 49 . But these efforts focus on short sequences of proteins or portions of antibodies.…”

Section: Introductionmentioning

confidence: 99%

Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks

Amimeur

Shaver

Ketchem

et al. 2020

Preprint

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We demonstrate the use of a Generative Adversarial Network (GAN), trained from a set of over 400,000 light and heavy chain human antibody sequences, to learn the rules of human antibody formation. The resulting model surpasses common in silico techniques by capturing residue diversity throughout the variable region, and is capable of generating extremely large, diverse libraries of novel antibodies that mimic somatically hypermutated human repertoire response. This method permits us to rationally design de novo humanoid antibody libraries with explicit control over various properties of our discovery library. Through transfer learning, we are able to bias the GAN to generate molecules with key properties of interest such as improved stability and developability, lower predicted MHC Class II binding, and specific complementarity-determining region (CDR) characteristics. These approaches also provide a mechanism to better study the complex relationships between antibody sequence and molecular behavior, both in vitro and in vivo . We validate our method by successfully expressing a proof-of-concept library of nearly 100,000 GAN-generated antibodies via phage display. We present the sequences and homology-model structures of example generated antibodies expressed in stable CHO pools and evaluated across multiple biophysical properties. The creation of discovery libraries using our in silico approach allows for the control of pharmaceutical properties such that these therapeutic antibodies can provide a more rapid and cost-effective response to biological threats.

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Feedback GAN for DNA optimizes protein functions

Cited by 158 publications

References 18 publications

OptiMol : Optimization of binding affinities in chemical space for drug discovery

OptiMol : Optimization of binding affinities in chemical space for drug discovery

Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction

Designing Feature-Controlled Humanoid Antibody Discovery Libraries Using Generative Adversarial Networks

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