• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to previous page

Design of immunoreceptor protein through the integration of machine learning and Bayesian inference

Research Project

Project/Area Number 22K18003
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 62010:Life, health and medical informatics-related
Research InstitutionAichi Cancer Center Research Institute

Principal Investigator

Guo Zhongliang  愛知県がんセンター(研究所), システム解析学分野, 研究員 (20875819)

Project Period (FY) 2022-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2023: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2022: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Keywordsタンパク質間相互作用 / 機械学習 / タンパク質大規模言語モデル / トポロジカルデータ解析 / タンパク質設計 / TCR / タンパク質間相互作用予測 / 結合能予測 / マルチモーダル学習 / サンプリング / ベイズ推論
Outline of Research at the Start

T細胞が受容体タンパク質(TCR)を通じてがん細胞やウイルス感染細胞を認識し,攻撃する.近年,この機構を利用してがん細胞認識能の高い受容体をT細胞に導入することでがんを治療するT細胞受容体遺伝子改変T細胞輸注療法が注目されている.しかしながら,がん抗原の個別性および多様性より,抗原に合わせた受容体分子の高精度かつ高効率な設計手法の開発は治療法の実現に不可欠である.本研究は機械学習とベイズ推論を組み合わせ,高精度かつTCR分子の設計手法の確立を目指す.

Outline of Final Research Achievements

Accurate protein-protein binding affinity prediction is essential for understanding protein function, designing new proteins for treating diseases. However, experimental measurement of protein binding affinity is time-consuming and expensive. In this study, we addressed the overlooked issues in current binding affinity prediction models in protein design. We developed a high-accuracy, fast prediction method that integrates information from both protein 3D structures and amino acid sequences using multimodal learning. The performance of our model on benchmark datasets showed a significant improvement over existing methods, with the Pearson correlation coefficient increasing from 0.684 to 0.904. Additionally, through model analysis, we confirmed the efficacy of multimodal learning in predicting protein binding affinity.

Academic Significance and Societal Importance of the Research Achievements

近年,ウイルスやがん細胞に結合する抗体またT細胞受容体の設計が注目され,臨床を含め,多くの研究が行われてきた.しかし,設計されたタンパク質とターゲット分子の結合能を実験で測定するには時間と費用がかかる.また,既存の結合能予測モデルの予測精度が実用化に至っていないことも十分に認識されていない.本研究課題で提案した高精度かつ高速な結合能予測モデルは,深層学習を用いたタンパク質設計手法と組み合わせることで,効率的にターゲット分子と結合するタンパク質を発見できることが期待される.タンパク質間相互作用はタンパク質機能の基礎であり,結合能を正確に予測することは生命現象の理解につながる重要なステップとなる.

Report

(3 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • Research Products

    (4 results)

All 2024 2023 2022

All Journal Article (1 results) (of which Peer Reviewed: 1 results,  Open Access: 1 results) Presentation (3 results) (of which Int'l Joint Research: 2 results)

  • [Journal Article] Machine learning methods for protein-protein binding affinity prediction in protein design2022

    • Author(s)
      Zhongliang Guo and Rui Yamaguchi
    • Journal Title

      Frontiers in Bioinformatics

      Volume: 2 Pages: 1065703-1065703

    • DOI

      10.3389/fbinf.2022.1065703

    • Related Report
      2022 Research-status Report
    • Peer Reviewed / Open Access
  • [Presentation] An integrated approach using sequential and structural features for precise prediction of protein-protein binding affinity2024

    • Author(s)
      Zhongliang Guo, Osamu Muto, Rui Yamaguchi
    • Organizer
      IUPAB Congress 2024
    • Related Report
      2023 Annual Research Report
    • Int'l Joint Research
  • [Presentation] A Sequence and topological feature integration for accurate protein-protein binding affinity estimation2024

    • Author(s)
      郭 中梁、武藤 理、山口 類
    • Organizer
      第6回日本メディカルAI学会学術集会
    • Related Report
      2023 Annual Research Report
  • [Presentation] A multimodal framework combining sequence and topological features for accurate protein-protein binding affinity prediction2023

    • Author(s)
      Zhongliang Guo, Osamu Muto, Yasunori Fukushima, Ayako Demachi-Okamura, Motonori Ota, Ryo Yoshida, Hirokazu Matsushita, Rui Yamaguchi
    • Organizer
      GIW ISCB ASIA 2023
    • Related Report
      2023 Annual Research Report
    • Int'l Joint Research

URL: 

Published: 2022-04-19   Modified: 2025-01-30  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi