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Title: H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing
Accurately modeling protein 3D structure is essential for the design of functional proteins. An important sub-task of structure modeling is protein side-chain packing: predicting the conformation of side-chains (rotamers) given the protein’s backbone structure and amino-acid sequence. Conventional approaches for this task rely on expensive sampling procedures over hand-crafted energy functions and rotamer libraries. Recently, several deep learning methods have been developed to tackle the problem in a data-driven way, albeit with vastly different formulations (from image-to-image translation to directly predicting atomic coordinates). Here, we frame the problem as a joint regression over the side-chains’ true degrees of freedom: the dihedral $$\chi$$ angles. We carefully study possible objective functions for this task, while accounting for the underlying symmetries of the task. We propose Holographic Packer (H-Packer), a novel two-stage algorithm for side-chain packing built on top of two light-weight rotationally equivariant neural networks. We evaluate our method on CASP13 and CASP14 targets. H-Packer is computationally efficient and shows favorable performance against conventional physics-based algorithms and is competitive against alternative deep learning solutions.  more » « less
Award ID(s):
2045054
PAR ID:
10512225
Author(s) / Creator(s):
; ; ;
Editor(s):
Knowles, David A; Mostafavi, Sara
Publisher / Repository:
PMLR
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
240
ISSN:
2640-3498
Page Range / eLocation ID:
230--249
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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