Please join us for our next RNA CASP SIG with Chaitanya K. Joshi:

„gRNAde - a Geometric Deep Learning pipeline for 3D RNA inverse design”

[Zoom](https://urldefense.proofpoint.com/v2/url?u=https-3A__stanford.zoom.us_j_93445935624-3Fpwd-3DK0VUWk0zaVNMZlU1U0xUMS8vSWUwZz09&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=0lUjW57N1liiDVO5wzD9g5qjic3xcIQ5O7wLU7r81hA&m=arbubYGVlHU7r5U62CgEmarGmxHnOtTysQwN9aCJsuHl9sQym0QFMDa2Gn41dEdZ&s=3rWBZ-vu-LrB-wgVAUts27AvWDWJFR6IEQNM23lG4eM&e=) link Tuesday January 30th Pacific Time 8 am / Eastern Time 11 am / Central European Time: 5 pm / China Standard Time: 11 pm

If you have recommendations on topics of discussion or speakers, please feel free to email us as well.

We have also recently implemented a [schedule](https://urldefense.proofpoint.com/v2/url?u=https-3A__tinyurl.com_rna-2Dsig-2Dschedule&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=0lUjW57N1liiDVO5wzD9g5qjic3xcIQ5O7wLU7r81hA&m=arbubYGVlHU7r5U62CgEmarGmxHnOtTysQwN9aCJsuHl9sQym0QFMDa2Gn41dEdZ&s=xrR8IIYhN9IQ0VlVjc_-o3oEldm_GbVPpvHEIGeihHw&e=) to view past and upcoming seminars, as well as a calendar [(google](https://urldefense.proofpoint.com/v2/url?u=https-3A__tinyurl.com_rna-2Dsig-2Dcalendar&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=0lUjW57N1liiDVO5wzD9g5qjic3xcIQ5O7wLU7r81hA&m=arbubYGVlHU7r5U62CgEmarGmxHnOtTysQwN9aCJsuHl9sQym0QFMDa2Gn41dEdZ&s=lkoboo6d_x60maIZC4BobSkhETuYI2_H2Yk2Q3ZJDSI&e=) [outlook)](https://urldefense.proofpoint.com/v2/url?u=https-3A__tinyurl.com_rna-2Dsig-2Dcal-2Dics&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=0lUjW57N1liiDVO5wzD9g5qjic3xcIQ5O7wLU7r81hA&m=arbubYGVlHU7r5U62CgEmarGmxHnOtTysQwN9aCJsuHl9sQym0QFMDa2Gn41dEdZ&s=RF0zJUjFA35qyo92Uq6Yt-fty5C1Nm2J5WJJDb5uJwA&e=) which can be added to automatically have events added to your calendar. Hopefully these will help everyone keep up to date.

See you soon,

Rachael Kretsch (Rhiju Das and Wah Chiu labs @Stanford)
Marcin Magnus (Elena Rivas lab @Harvard)

For recording see playlist on [YouTube @CASPRNASIG](https://www.youtube.com/@CASPRNASIG).

Zoom link: https://stanford.zoom.us/j/93445935624?pwd=K0VUWk0zaVNMZlU1U0xUMS8vSWUwZz09

# Abstract

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this talk, I will present gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. Under the hood, gRNAde is a Graph Neural Network that generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2011), gRNAde obtains higher native sequence recovery rates (54% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We envision gRNAde to be useful for inverse design of structured RNAs of interest for therapeutics and biotechnology, including riboswitches, aptamers, and ribozymes.

# Bio

Chaitanya K. Joshi is a 3rd year PhD student at the Department of Computer Science, University of Cambridge, supervised by Prof. Pietro Liò. His research explores the intersection of Geometric Deep Learning and Graph Neural Networks for applications in biomolecule modelling & design. He previously did an undergraduate degree in Computer Science from Nanyang Technological University and worked as a Research Engineer at A*STAR in Singapore.