ArticleProceedings of the National Academy of Sciences of the United States of America2023
An end-to-end deep learning method for protein side-chain packing and inverse folding.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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.
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.
Who cites it
32 citing papers in PubMed.
- The MODNAP benchmark set for modeling ribose-based modified nucleic acid and nucleic acid-protein complexes: a case study with AlphaFold 3.Nucleic acids research · 2026Article
- In-Silico identification and optimization of therapeutic peptides against breast cancer via transcriptomic profiling.Molecular diversity · 2026Article
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A Bayesian Framework with Dirichlet Priors and Spatial Smoothing for Protein Rotamer Prediction.International journal of molecular sciences · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Flexible protein-ligand docking with diffusion-based side-chain packing.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Enhancing functional proteins through multimodal inverse folding with ABACUS-T.Nature communications · 2025Article
- Predicting side chain conformations in folded proteins by AlphaFold: Perspective and challenges.Biophysical journal · 2025Article
- Ensemble-conditioned protein sequence design with Caliby.bioRxiv : the preprint server for biology · 2025Article
- AlphaFold modeling uncovers global structural features of class I and class II fungal hydrophobins.Protein science : a publication of the Protein Society · 2025Article
- A structure-oriented kinetics dataset of enzyme-substrate interactions.Scientific data · 2025Article
- Highly conserved brain vascular receptor ALPL mediates transport of engineered AAV vectors across the blood-brain barrier.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- CATH-ddG: towards robust mutation effect prediction on protein-protein interactions out of CATH homologous superfamily.Bioinformatics (Oxford, England) · 2025Article
- Sidechain conditioning and modeling for full-atom protein sequence design with FAMPNN.Proceedings of machine learning research · 2025Article
- To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era.Briefings in bioinformatics · 2025Article
- Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.Biomolecules · 2025Review
- FlowPacker: protein side-chain packing with torsional flow matching.Bioinformatics (Oxford, England) · 2025Article
- Dissecting AlphaFold2's capabilities with limited sequence information.Bioinformatics advances · 2025Article
- Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to tackle this problem, but their speed or accuracy is still unsatisfactory. To address this, we present AttnPacker, a deep learning (DL) method for directly predicting protein side-chain coordinates. Unlike existing methods, AttnPacker directly incorporates backbone 3D geometry to simultaneously compute all side-chain coordinates without delegating to a discrete rotamer library or performing expensive conformational search and sampling steps. This enables a significant increase in computational efficiency, decreasing inference time by over 100× compared to the DL-based method DLPacker and physics-based RosettaPacker. Tested on the CASP13 and CASP14 native and nonnative protein backbones, AttnPacker computes physically realistic side-chain conformations, reducing steric clashes and improving both rmsd and dihedral accuracy compared to state-of-the-art methods SCWRL4, FASPR, RosettaPacker, and DLPacker. Different from traditional PSCP approaches, AttnPacker can also codesign sequences and side chains, producing designs with subnative Rosetta energy and high in silico consistency.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.