ArticleBioinformatics (Oxford, England)2023
Accurate and efficient protein sequence design through learning concise local environment of residues.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 33 citations in OpenAlex.
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- GeoGAD: geometry-aware antibody design framework for complementarity-determining region precision engineering.Bioinformatics (Oxford, England) · 2026Article
- GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Preclinical Positron Emission Tomography Imaging of B7-H3 Expression Using Affibody Molecules Labeled with Gallium-68.ACS pharmacology & translational science · 2025Article
- Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.Proteins · 2025Article
- Impact of Radiometal Chelates on In Vivo Visualization of Immune Checkpoint Protein Using Radiolabeled Affibody Molecules.ACS pharmacology & translational science · 2025Article
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- PepCARES: A Comprehensive Advanced Refinement and Evaluation System for Peptide Design and Affinity Screening.ACS omega · 2024Article
- Graphormer supervised de novo protein design method and function validation.Briefings in bioinformatics · 2024Article
- SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.Briefings in bioinformatics · 2024Article
- Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.Bioinformatics (Oxford, England) · 2024Article
- SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.PLoS computational biology · 2023Article
- Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.Genomics, proteomics & bioinformatics · 2023Review
- From sequence to function through structure: Deep learning for protein design.Computational and structural biotechnology journal · 2023Review
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationComputational protein sequence design has been widely applied in rational protein engineering and increasing the design accuracy and efficiency is highly desired.
resultsHere, we present ProDESIGN-LE, an accurate and efficient approach to protein sequence design. ProDESIGN-LE adopts a concise but informative representation of the residue's local environment and trains a transformer to learn the correlation between local environment of residues and their amino acid types. For a target backbone structure, ProDESIGN-LE uses the transformer to assign an appropriate residue type for each position based on its local environment within this structure, eventually acquiring a designed sequence with all residues fitting well with their local environments. We applied ProDESIGN-LE to design sequences for 68 naturally occurring and 129 hallucinated proteins within 20 s per protein on average. The designed proteins have their predicted structures perfectly resembling the target structures with a state-of-the-art average TM-score exceeding 0.80. We further experimentally validated ProDESIGN-LE by designing five sequences for an enzyme, chloramphenicol O-acetyltransferase type III (CAT III), and recombinantly expressing the proteins in Escherichia coli. Of these proteins, three exhibited excellent solubility, and one yielded monomeric species with circular dichroism spectra consistent with the natural CAT III protein. AVAILABILITY AND IMPLEMENTATION: The source code of ProDESIGN-LE is available at https://github.com/bigict/ProDESIGN-LE.
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