Research activity information

• Apr. 2024, 北海道大学, Program for Supporting Challenging and Interdisciplinary Research Field
• Apr. 2023, 北海道大学, Program for Supporting Challenging and Interdisciplinary Research Field
• Apr. 2022, 北海道大学, Program for Supporting Challenging and Interdisciplinary Research Field
• Apr. 2022, 北海道大学, DX 博士人材フェローシップ

• Probe-based identification of metal-binding sites using deep learning representations
  Shijie Xu; Akira Onoda
  Nature Communications, Springer Science and Business Media LLC, 30 Jun. 2026, [Lead author]
  Scientific journal
• Accurate prediction of enzymatic degradation of plastics by language models
  Shijie Xu; Ke Xu; Akira Onoda
  Chemistry Letters, 55, 2, Oxford University Press (OUP), 03 Feb. 2026, [Lead author]
  Scientific journal, Abstract
  
  Plastic pollution poses a significant environmental threat, with PET comprising 12% of global solid waste. Enzymatic degradation provides a sustainable recycling method, yet optimizing enzymes for industrial use remains difficult. This study utilizes protein and polymer language models to predict plastic degradation from protein sequences and polymer SMILES. The approach demonstrates high prediction accuracy with key structural features revealed by language models, enabling efficient in silico screening for protein design.
• Accurate and Rapid Prediction of Protein pKa: Protein Language Models Reveal the Sequence–pKa Relationship
  Shijie Xu; Akira Onoda
  Journal of Chemical Theory and Computation, 08 Apr. 2025, [Lead author]
  Scientific journal
• PsiPartition: Improved Site Partitioning for Genomic Data by Parameterized Sorting Indices and Bayesian Optimization
  Shijie Xu; Akira Onoda
  Journal of Molecular Evolution, 92, 6, 874, 890, Springer Science and Business Media LLC, 05 Dec. 2024, [Lead author]
  Scientific journal
• Accurate and Fast Prediction of Intrinsically Disordered Protein by Multiple Protein Language Models and Ensemble Learning
  Shijie Xu; Akira Onoda
  Journal of Chemical Information and Modeling, 64, 7, 2901, 2911, American Chemical Society (ACS), 26 Oct. 2023, [Lead author]
  Scientific journal

• Accurate and Fast Prediction of Metal-binding Sites in Proteins Using Deep Learning Methods
  Shijie Xu, Akira Onoda
  52nd Biomolecule Science Symposium, 14 May 2026, English, Oral presentation
  14 May 2026 - 16 May 2026, Himeji, Japan, Japan
• Accurate and Fast Prediction of Protein pKa by A Protein Language Model
  Shijie Xu, Akira Onoda
  the 19th Symposium on Biorelevant Chemistry, 02 Sep. 2025, English, Poster presentation
  02 Sep. 2025 - 04 Sep. 2025, Kyoto, Japan, Japan
• Accurate and Fast Prediction of Protein pKa by Protein Language Model
  Shijie Xu, Akira Onoda
  51st Biomolecule Science Symposium, 26 Jun. 2025, English, Oral presentation
  26 Jun. 2025 - 27 Jun. 2025, Sapporo, Japan, Japan
• Accurate and Fast Prediction of Protein pKa by Protein Language Model
  Shijie Xu, Akira Onoda
  8th ICReDD International Symposium, 22 Oct. 2024, English, Poster presentation
  22 Oct. 2024 - 24 Oct. 2024, Sapporo, Japan, Japan
• Accurate Prediction of Intrinsically Disordered Protein by Protein Language Models
  Shijie Xu, Akira Onoda
  the 24th Annual Meeting of the Protein Science Society of Japan, 11 Jul. 2024, English, Poster presentation
  11 Jul. 2024 - 13 Jul. 2024, Sapporo, Japan, Japan
• Accurate Prediction of Protein pKa by Geometric Deep Learning
  Shijie Xu, Akira Onoda
  the 104th CSJ annual meeting, 18 Mar. 2024, English, Oral presentation
  18 Mar. 2024 - 21 Mar. 2024, Funabashi, Japan, Japan
• Accurate Prediction of Intrinsically Disordered Protein by Protein Language Models
  Shijie Xu, Akira Onoda
  the 17th Biorelevant Chemistry Symposium, 08 Sep. 2023, English, Poster presentation
  08 Sep. 2023 - 10 Sep. 2023, Chiba, Japan, Japan
• Fast and Accurate Prediction of Intrinsically Disordered Protein by Protein Language Model
  Shijie Xu, Akira Onoda
  the 103rd CSJ annual meeting, 22 Mar. 2023, English, Oral presentation
  22 Mar. 2023 - 25 Mar. 2023, Chiba, Japan, Japan

• Mar. 2023 - Present
  日本化学会