Rapid prediction of protein natural frequencies using graph neural networks-Reference-Cited by-同舟云学术

Rapid prediction of protein natural frequencies using graph neural networks

Published:2022 Issue:3 Volume:1 Page:277-285
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Guo Kai¹²^ORCID,Buehler Markus J.¹³⁴^ORCID

Affiliation:

1. Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave. 1-165, Cambridge, Massachusetts 02139, USA

2. Institute of High Performance Computing, A*STAR, Singapore 138632, Singapore

3. Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, Massachusetts 02139, USA

4. Center for Materials Science and Engineering, 77 Massachusetts Ave, Cambridge, Massachusetts 02139, USA

Abstract

We present a computational framework based on graph neural networks (GNNs) to predict the natural frequencies of proteins from primary amino acid sequences and contact/distance maps.

Funder

Office of Naval Research

Multidisciplinary University Research Initiative

Army Research Office

National Institutes of Health

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/DD/D1DD00007A

Reference57 articles.

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2. The Protein Data Bank