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Please join us for our next RNA CASP SIG with Daisuke Kihara, PhD 

“NuFold: RNA Structure Prediction Method Using Deep Learning with Flexible Nucleobase Center Representation”

[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 April 9; 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

We developed NuFold, a deep neural network-based RNA structure prediction method. Nufold was trained end-to-end; it takes the input RNA sequence and outputs the 3D structure of RNA from the single network.  NuFold incorporates a nucleobase center representation, which enables flexible conformation of ribose rings. NuFold clearly outperformed template- and energy-based methods and showed comparable results with deep-learning-based methods in most of the targets. We discuss various modifications we explored to improve the modeling results. 
  
# BIO

Our research area is bioinformatics; bioinformatics is beginning to have a large impact on the field of biology now that more and more structure, sequence, gene expression data, and pathway data have become available in the past decade. These large data enable us to employ comprehensive analysis of protein sequences/structures, genomes, and pathways. Our enthusiasm for scientific research comes from a desire to have a global view of protein folding; the relationship between protein function, sequence, and structure; and evolution of protein families, pathways, and organisms. Now complete genome sequences of more than 210 organisms are available in public databases. Using these genome data, we can directly investigate evolution of organisms/protein families, gene transfer, or mechanism of coding principle of pathways in a genome. Linking protein tertiary structure information which comes from both experimental data and prediction will add another important aspect in our understanding. Therefore, in practical terms our main research interest is to develop computational methods to predict and analyze protein structure/function, pathway structure and their applications in genome-scale, or pathway/network scale.

# Keywords
(Bioinformatics, computational biology) protein tertiary structure prediction/comparison, protein-protein docking, protein-ligand docking, protein function prediction, protein sequence analysis, metabolic/regulatory pathway analysis.

https://www.bio.purdue.edu/People/profile/dkihara.html