Surana, S. | bioRxiv Science

bioRxiv2021

Tissue specific targeting of DNA nanodevices in a multicellular living organism

Nucleic acid nanodevices present great potential as agents for logic-based therapeutic intervention as well as in basic biology. Often, however, the disease targets that need corrective action are localized in specific organs and thus realizing the full potential of DNA nanodevices also requires ways to target them to specific cell-types in vivo. Here we show that by exploiting either endogenous or synthetic receptor-ligand interactions and by leveraging the biological barriers presented by the organism, we can target extraneously introduced DNA nanodevices to specific cell types in C. elegans, with sub-cellular precision. The amenability of DNA nanostructures to tissue-specific targeting in vivo significantly expands their utility in biomedical applications and discovery biology.

biochemistry↗

bioRxiv2021

PandoraGAN: Generating antiviral peptides using Generative Adversarial Network

The continuous increase in pathogenic viruses and the intensive laboratory research emphasizes the need for cost and time efficient drug development. This accelerates research for alternate drug candidates like antiviral peptides(AVP) that have therapeutic and prophylactic potential and gaining attention in recent times. However, diversity in their sequences, limited and non-uniform characterization often limit their applications. Isolating newer peptide backbones with required characteristics is a cumbersome process with many design-test-build cycles. Advanced deep learning approaches such as generative adversarial networks (GAN) can be helpful to expedite the initial stage of developing novel peptide drugs. In this study, we developed PandoraGAN that uses a manually curated training dataset of 130 highly active peptides that includes peptides from known databases (such as AVPdb) and literature to generate novel antiviral peptides. The underlying architecture in PandoraGAN is able to learn a good representation of the implicit properties of antiviral peptides. The generated sequences from PandoraGAN are validated based on physico-chemical properties. They are also compared with the training dataset statistically using Pearsons correlation and Mann-Whitney U-test. We therefore confirm that PandoraGAN is capable of generating a novel antiviral peptide backbone showing similar properties to that of the known highly active antiviral peptides. This approach exhibits a potential to discover novel patterns of AVP which may have not been seen earlier with traditional methods. To our knowledge this is the first ever use of GAN models for antiviral peptides across the viral spectrum.

bioinformatics↗