Scholarlink

Abstract <p>Protein-protein interactions (PPIs) regulate essential biological processes through complex interfaces, with their dysfunction is associated with various diseases. Consequently, the identification of PPIs and their interface-targeting modulators has emerged as a critical therapeutic approach. However, discovering modulators that target PPIs and PPI interfaces remains challenging as traditional structure-similarity-based methods fail to effectively characterize PPI targets, particularly those for which no active compounds are known. Here, we present AlphaPPIMI, a comprehensive deep learning framework that combines large-scale pretrained language models with domain adaptation for predicting PPI-modulator interactions, specifically targeting PPI interface. To enable robust model development and evaluation, we constructed comprehensive benchmark datasets of PPI-modulator interactions (PPIMI). Our framework integrates comprehensive molecular features from Uni-Mol2, protein representations derived from state-of-the-art language models (ESM2 and ProTrans), and PPI structural characteristics encoded by PFeature. Through a specialized cross-attention architecture and conditional domain adversarial networks (CDAN), AlphaPPIMI effectively learns potential associations between PPI targets and modulators while ensuring robust cross-domain generalization. Extensive evaluations indicate that AlphaPPIMI achieves consistently improved performance over existing methods in PPIMI prediction, offering a promising approach for prioritizing candidate PPI modulators, particularly those targeting protein–protein interfaces.</p> Scientific contribution <p>This work presents AlphaPPIMI, a novel deep learning framework for accurately predicting modulators targeting protein-protein interactions (PPIs) and their interfaces. Its core contributions include a specialized cross-attention module for the synergistic fusion of multimodal pretrained representations, and the novel application of a Conditional Domain Adversarial Network (CDAN) to significantly improve generalization across diverse protein families. AlphaPPIMI demonstrates superior performance on curated benchmarks, providing a powerful computational tool for the discovery of targeted PPI therapeutics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Alphappimi: a comprehensive deep learning framework for predicting PPI-modulator interactions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

查看全文
  • Dayan Liu,
  • Tao Song,
  • Shuang Wang,
  • Xue Li,
  • Peifu Han,
  • Jianmin Wang,
  • Shudong Wang

摘要

Abstract

Protein-protein interactions (PPIs) regulate essential biological processes through complex interfaces, with their dysfunction is associated with various diseases. Consequently, the identification of PPIs and their interface-targeting modulators has emerged as a critical therapeutic approach. However, discovering modulators that target PPIs and PPI interfaces remains challenging as traditional structure-similarity-based methods fail to effectively characterize PPI targets, particularly those for which no active compounds are known. Here, we present AlphaPPIMI, a comprehensive deep learning framework that combines large-scale pretrained language models with domain adaptation for predicting PPI-modulator interactions, specifically targeting PPI interface. To enable robust model development and evaluation, we constructed comprehensive benchmark datasets of PPI-modulator interactions (PPIMI). Our framework integrates comprehensive molecular features from Uni-Mol2, protein representations derived from state-of-the-art language models (ESM2 and ProTrans), and PPI structural characteristics encoded by PFeature. Through a specialized cross-attention architecture and conditional domain adversarial networks (CDAN), AlphaPPIMI effectively learns potential associations between PPI targets and modulators while ensuring robust cross-domain generalization. Extensive evaluations indicate that AlphaPPIMI achieves consistently improved performance over existing methods in PPIMI prediction, offering a promising approach for prioritizing candidate PPI modulators, particularly those targeting protein–protein interfaces.

Scientific contribution

This work presents AlphaPPIMI, a novel deep learning framework for accurately predicting modulators targeting protein-protein interactions (PPIs) and their interfaces. Its core contributions include a specialized cross-attention module for the synergistic fusion of multimodal pretrained representations, and the novel application of a Conditional Domain Adversarial Network (CDAN) to significantly improve generalization across diverse protein families. AlphaPPIMI demonstrates superior performance on curated benchmarks, providing a powerful computational tool for the discovery of targeted PPI therapeutics.