MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

• URL: http://arxiv.org/abs/2607.01627v1
• Date: Thu, 02 Jul 2026 02:50:42 GMT
• Title: MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction
• Abstract summary: This paper presents method, a multimodal representation framework for cold-start PPI prediction.<n>method combines region-aware protein sequence encoding with four protein-centered biomedical knowledge graphs.<n>Experiments on two benchmark datasets show that method consistently outperforms competitive sequence, network, and knowledge-graph baselines.
• Score: 1.6711037450548076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development. A difficult setting arises when candidate interactions include proteins that have no observed PPI edges during training, where models relying on network topology alone often lose useful context. This paper presents \method, a multimodal representation framework for cold-start PPI prediction. \method\ combines region-aware protein sequence encoding with four protein-centered biomedical knowledge graphs, including protein-drug, protein-disease, protein-miRNA, and protein-lncRNA associations. The sequence branch extracts contextual representations from structurally informed sequence regions, while graph attention encoders learn modality-specific protein embeddings from sparse biomedical associations. A bridge reconstruction objective regularizes graph learning by recovering shared protein-entity associations, and a pair-level gating module adaptively integrates sequence and graph evidence for each candidate protein pair. Experiments on two benchmark datasets under novel-old and novel-novel cold-start settings show that \method\ consistently outperforms competitive sequence, network, and knowledge-graph baselines across ACC, F1, AUC, AUPR, and MCC.

Related papers

• GraphPI: Efficient Protein Inference with Graph Neural Networks [5.080679217338999]
  We introduce GraphPI, a novel framework that treats protein inference as a node classification problem.<n>We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a Graph Neural Network-based architecture to elucidate their interrelations.<n>Our experiments reveal notable performance on various test datasets and deliver significantly reduced computation times compared to common protein inference algorithms.
  arXiv  Detail & Related papers  (2026-05-06T00:55:10Z)
• S$^2$Drug: Bridging Protein Sequence and 3D Structure in Contrastive Representation Learning for Virtual Screening [72.89086338778098]
  We propose a two-stage framework for protein-ligand contrastive representation learning.<n>In the first stage, we perform protein sequence pretraining on ChemBL using an ESM2-based backbone.<n>In the second stage, we fine-tune on PDBBind by fusing sequence and structure information through a residue-level gating module.<n>This auxiliary task guides the model to accurately localize binding residues within the protein sequence and capture their 3D spatial arrangement.
  arXiv  Detail & Related papers  (2025-11-10T11:57:47Z)
• SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
  We introduce a novel pre-training strategy for protein foundation models. It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features. Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
  arXiv  Detail & Related papers  (2024-10-31T15:22:03Z)
• Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers [76.95505296417866]
  Self-supervised training of language models (LMs) has seen great success for protein sequences in learning meaningful representations and for generative drug design.<n>Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths.<n>In this work, we propose LC-PLM based on an alternative protein LM architecture, BiMamba-S, built upon selective structured state-space models.
  arXiv  Detail & Related papers  (2024-10-29T16:43:28Z)
• ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction [54.132290875513405]
  The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions. We propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time.
  arXiv  Detail & Related papers  (2024-03-30T05:32:42Z)
• PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction [63.50967073653953]
  Compound-Protein Interaction prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures. We propose a novel multi-scale Protein Sequence-structure Contrasting framework for CPI prediction.
  arXiv  Detail & Related papers  (2024-02-13T03:51:10Z)
• Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence [16.06613460477948]
  Prediction of binding sites of proteins is an important task for understanding function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. We propose LaMPSite, which only takes protein sequences and ligand molecular graphs as input.
  arXiv  Detail & Related papers  (2023-12-05T01:47:38Z)
• Neural Embeddings for Protein Graphs [0.8258451067861933]
  We propose a novel framework for embedding protein graphs in geometric vector spaces. We learn an encoder function that preserves the structural distance between protein graphs. Our framework achieves remarkable results in the task of protein structure classification.
  arXiv  Detail & Related papers  (2023-06-07T14:50:34Z)
• Structure-aware Protein Self-supervised Learning [50.04673179816619]
  We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
  arXiv  Detail & Related papers  (2022-04-06T02:18:41Z)
• OntoProtein: Protein Pretraining With Gene Ontology Embedding [36.92674447484136]
  We propose OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models. We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph.
  arXiv  Detail & Related papers  (2022-01-23T14:49:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.