Protein model quality assessment using rotation-equivariant, hierarchical neural networks

• URL: http://arxiv.org/abs/2011.13557v1
• Date: Fri, 27 Nov 2020 05:03:53 GMT
• Title: Protein model quality assessment using rotation-equivariant, hierarchical neural networks
• Authors: Stephan Eismann, Patricia Suriana, Bowen Jing, Raphael J.L. Townshend, Ron O. Dror
• Abstract summary: We present a novel deep learning approach to assess the quality of a protein model. Our method achieves state-of-the-art results in scoring protein models submitted to recent rounds of CASP.
• Score: 8.373439916313018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A major bottleneck involves selecting the most accurate structural model among a large pool of candidates, a task addressed in model quality assessment. Here, we present a novel deep learning approach to assess the quality of a protein model. Our network builds on a point-based representation of the atomic structure and rotation-equivariant convolutions at different levels of structural resolution. These combined aspects allow the network to learn end-to-end from entire protein structures. Our method achieves state-of-the-art results in scoring protein models submitted to recent rounds of CASP, a blind prediction community experiment. Particularly striking is that our method does not use physics-inspired energy terms and does not rely on the availability of additional information (beyond the atomic structure of the individual protein model), such as sequence alignments of multiple proteins.

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