ArticleNature communications2023
ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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.
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
12 citing papers in PubMed.
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Evolution-guided protein design of IscB for persistent epigenome editing in vivo.Nature biotechnology · 2026Article
- Single-round evolution of RNA aptamers with GRAPE-LM.Nature biotechnology · 2026Article
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- Machine learning-guided evolution of pyrrolysyl-tRNA synthetase for improved incorporation efficiency of diverse noncanonical amino acids.Nature communications · 2025Article
- DivPro: diverse protein sequence design with direct structure recovery guidance.Bioinformatics (Oxford, England) · 2025Article
- Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.Physiology (Bethesda, Md.) · 2025Review
- Computational tools for plant genomics and breeding.Science China. Life sciences · 2024Review
- Context-aware geometric deep learning for protein sequence design.Nature communications · 2024Article
- A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.Briefings in bioinformatics · 2024Article
- ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention.Nature communications · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Inverse Protein Folding (IPF) is an important task of protein design, which aims to design sequences compatible with a given backbone structure. Despite the prosperous development of algorithms for this task, existing methods tend to rely on noisy predicted residues located in the local neighborhood when generating sequences. To address this limitation, we propose an entropy-based residue selection method to remove noise in the input residue context. Additionally, we introduce ProRefiner, a memory-efficient global graph attention model to fully utilize the denoised context. Our proposed method achieves state-of-the-art performance on multiple sequence design benchmarks in different design settings. Furthermore, we demonstrate the applicability of ProRefiner in redesigning Transposon-associated transposase B, where six out of the 20 variants we propose exhibit improved gene editing activity.
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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 OpenQuestion graph.