ArticleProteins2024
Invariant point message passing for protein side chain packing.
Article in Proteins, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.Proceedings of machine learning research · 2025Article
- PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model.Protein science : a publication of the Protein Society · 2025Article
- To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era.Briefings in bioinformatics · 2025Article
- FlowPacker: protein side-chain packing with torsional flow matching.Bioinformatics (Oxford, England) · 2025Article
- Amino-Acid Characteristics in Protein Native State Structures.Biomolecules · 2024Article
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Authors and funding
2 authors.
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Abstract
Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using
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Registered trials
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 Socratic graph.