mlsanityData to 13 Oct 2026

Learning the shape of protein micro-environments with a holographic convolutional neural network

arXiv 2211.02936 ↗First posted 5 Nov 2022physics.bio-ph cs.LG q-bio.QMcited by 4

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Abstract (arXiv)

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 16 of its 88 references that are in the map. First posted on arXiv 5 Nov 2022.

Builds on

16 of 88 references.

References · 16

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4 more
  1. 2018Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Networkcited by 170
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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packingcited by 6
  2. 2023Pairing interacting protein sequences using masked language modelingcited by 1
  3. 2024HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability predictioncited by 1
  4. 2024Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loopscited by 0