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

Gian Marco Visani, William Galvin, Michael N Pun, Armita Nourmohammad

Abstract readPreprint

In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gian Marco VisaniPaul G. Allen School of Computer Science and Engineering, University of Washington.

William GalvinPaul G. Allen School of Computer Science and Engineering, University of Washington.

Michael N PunDepartment of Physics, University of Washington.

Armita NourmohammadPaul G. Allen School of Computer Science and Engineering, University of Washington.

Funding

Learning a molecular shape space for the adaptive immune systemR35GM142795 · NIGMS · UNIVERSITY OF WASHINGTON · PI NOURMOHAMMAD, ARMITA · 2021 to 2025

$1.8M

NIGMS NIH HHS R35 GM142795

6 · The paper itself

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

Identifiers

PMID38013891

PMCPMC10680869

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.