Bio-informed Protein Sequence Generation for Multi-class Virus Mutation Prediction
Wang, Y.; Yadav, P.; Magar, R.; Barati farimani, A.
Show abstract
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.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- PRIEST - Predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information 97%
- GexMolGen: Cross-modal Generation of Hit-like Molecules via Large Language Model Encoding of Gene Expression Signatures 96%
- LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec 95%
Similar papers in this journal
- Improving protein function prediction with synthetic feature samples created by generative adversarial networks 95%
- Accelerating protein engineering with fitness landscape modeling and reinforcement learning 93%
- Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports 93%
Similar papers in this journal
Similar papers in this journal
- Learning universal knowledge graph embedding for predicting biomedical pairwise interactions 96%
- iDRKAN: Interpretable miRNA-Disease Association Prediction Based on Dual-Graph Representation Learning and Kolmogorov-Arnold Network 96%
- Context-Aware Hierarchical Fusion for Drug Relational Learning 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.