H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing

Gian Marco Visani, William Galvin, Michael Pun, Armita Nourmohammad
Proceedings of the 18th Machine Learning in Computational Biology meeting, PMLR 240:230-249, 2024.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v240-visani24a, title = {H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing}, author = {Visani, Gian Marco and Galvin, William and Pun, Michael and Nourmohammad, Armita}, booktitle = {Proceedings of the 18th Machine Learning in Computational Biology meeting}, pages = {230--249}, year = {2024}, editor = {Knowles, David A. and Mostafavi, Sara}, volume = {240}, series = {Proceedings of Machine Learning Research}, month = {30 Nov--01 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v240/visani24a/visani24a.pdf}, url = {https://proceedings.mlr.press/v240/visani24a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing %A Gian Marco Visani %A William Galvin %A Michael Pun %A Armita Nourmohammad %B Proceedings of the 18th Machine Learning in Computational Biology meeting %C Proceedings of Machine Learning Research %D 2024 %E David A. Knowles %E Sara Mostafavi %F pmlr-v240-visani24a %I PMLR %P 230--249 %U https://proceedings.mlr.press/v240/visani24a.html %V 240 %X 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.
APA
Visani, G.M., Galvin, W., Pun, M. & Nourmohammad, A.. (2024). H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing. Proceedings of the 18th Machine Learning in Computational Biology meeting, in Proceedings of Machine Learning Research 240:230-249 Available from https://proceedings.mlr.press/v240/visani24a.html.

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