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Title: Multiscale graph neural networks for protein residue contact map prediction
Machine learning (ML) is revolutionizing protein structural analysis, including an important subproblem of predicting protein residue contact maps, i.e., which ami-no-acid residues are in close spatial proximity given the amino-acid sequence of a protein. Despite recent progresses in ML-based protein contact prediction, predict-ing contacts with a wide range of distances (commonly classified into short-, me-dium- and long-range contacts) remains a challenge. Here, we propose a multiscale graph neural network (GNN) based approach taking a cue from multiscale physics simulations, in which a standard pipeline involving a recurrent neural network (RNN) is augmented with three GNNs to refine predictive capability for short-, medium- and long-range residue contacts, respectively. Test results on the Pro-teinNet dataset show improved accuracy for contacts of all ranges using the pro-posed multiscale RNN+GNN approach over the conventional approach, including the most challenging case of long-range contact prediction.  more » « less
Award ID(s):
2118061
NSF-PAR ID:
10423189
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of International Conference on Artificial Intelligence, ICAI
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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