ArticleBMC bioinformatics2024
xCAPT5: protein-protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Predicting Protein-Protein Interactions by Convolutional Neural Network Model.Biotech (Basel (Switzerland)) · 2026Article
- ProtAttn-QuadNet: An attention-based deep learning framework for protein-protein interaction prediction using ProtBERT embeddings.PloS one · 2026Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Two-Component Systems inVeterinary sciences · 2025Review
- PPISHES-an enhanced physicochemical approach for predicting protein interaction sites using graph neural networks.Protein science : a publication of the Protein Society · 2025Article
- Protein Language Models are Accidental Taxonomists.bioRxiv : the preprint server for biology · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models.Biosafety and health · 2025Article
- ESM2_AMP: an interpretable framework for protein-protein interactions prediction and biological mechanism discovery.Briefings in bioinformatics · 2025Article
- Feature fusion with attributed deepwalk for protein-protein interaction prediction.Scientific reports · 2025Article
- PPILS: Protein-protein interaction prediction with language of biological coding.Computers in biology and medicine · 2025Article
- NFEmbed: modeling nitrogenase activity via classification and regression with pretrained protein embeddings.Bioinformatics advances · 2025Article
- Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein-Protein Interaction Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- StackGlyEmbed: prediction of N-linked glycosylation sites using protein language models.Bioinformatics advances · 2025Article
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2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPredicting protein-protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, which contain diverse information, including structural, evolutionary, and functional aspects, has not been fully exploited. Additionally, there is a significant need for a comprehensive neural network capable of efficiently extracting these multifaceted representations.
resultsAddressing this gap, we propose xCAPT5, a novel hybrid classifier that uniquely leverages the T5-XL-UniRef50 protein large language model for generating rich amino acid embeddings from protein sequences. The core of xCAPT5 is a multi-kernel deep convolutional siamese neural network, which effectively captures intricate interaction features at both micro and macro levels, integrated with the XGBoost algorithm, enhancing PPIs classification performance. By concatenating max and average pooling features in a depth-wise manner, xCAPT5 effectively learns crucial features with low computational cost.
conclusionThis study represents one of the initial efforts to extract informative amino acid embeddings from a large protein language model using a deep and wide convolutional network. Experimental results show that xCAPT5 outperforms recent state-of-the-art methods in binary PPI prediction, excelling in cross-validation on several benchmark datasets and demonstrating robust generalization across intra-species, cross-species, inter-species, and stringent similarity contexts.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.