An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen

• URL: http://arxiv.org/abs/2604.18467v2
• Date: Sat, 25 Apr 2026 03:15:23 GMT
• Title: An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen
• Abstract summary: We present an integrated framework for early-stage peptide screening.<n>The framework combines a partner-aware prediction and localization model (ConGA-PepPI) with a target-conditioned generative model (TC-PepGen)<n>In five-fold cross-validation, ConGA-PepPI achieved 0.839 accuracy and 0.921 AUROC, with binding-site AUPR values of 0.601 on the protein side and 0.950 on the peptide side.
• Score: 2.9196005639424647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivation: Peptide-protein interactions (PepPIs) are central to cellular regulation and peptide therapeutics, but experimental characterization remains too slow for large-scale screening. Existing methods usually emphasize either interaction prediction or peptide generation, leaving candidate prioritization, residue-level interpretation, and target-conditioned expansion insufficiently integrated. Results: We present an integrated framework for early-stage peptide screening that combines a partner-aware prediction and localization model (ConGA-PepPI) with a target-conditioned generative model (TC-PepGen). ConGA-PepPI uses asymmetric encoding, bidirectional cross-attention, and progressive transfer from pair prediction to binding-site localization, while TC-PepGen preserves target information throughout autoregressive decoding via layerwise conditioning. In five-fold cross-validation, ConGA-PepPI achieved 0.839 accuracy and 0.921 AUROC, with binding-site AUPR values of 0.601 on the protein side and 0.950 on the peptide side, and remained competitive on external benchmarks. Under a controlled length-conditioned benchmark, 40.39% of TC-PepGen peptides exceeded native templates in AlphaFold 3 ipTM, and unconstrained generation retained evidence of target-conditioned signal.

Related papers

• Benchmarking Peptide-Protein Affinity Prediction Across Peptide and Target Shifts [3.2000742569384237]
  We integrated three sources of quantitative peptide-protein binding data to obtain 11,349 deduplicated pairs.<n>We benchmarked ten peptide representations, ESM-2 protein embeddings, and six regressors under peptide-similarity, within-target, and leave-target-out partitions.
  arXiv  Detail & Related papers  (2026-08-31T02:56:26Z)
• PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities [86.63247982275396]
  PRIME is a missing-aware multimodal self-supervised pretraining framework.<n>It learns robust and transferable representations from partially observed cohorts.<n>We evaluate PRIME on The Cancer Genome Atlas with label-free pretraining on 32 cancer types.
  arXiv  Detail & Related papers  (2026-04-05T21:14:27Z)
• KEMP-PIP: A Feature-Fusion Based Approach for Pro-inflammatory Peptide Prediction [1.0131895986034314]
  KEMP-PIP is a hybrid machine learning framework that integrates deep protein embeddings with handcrafted descriptors for robust PIP prediction.<n>Our approach combines contextual embeddings from pretrained ESM protein language models with multi-scale k-mer frequencies, physicochemical descriptors, and modlAMP sequence features.<n>On the standard benchmark dataset, KEMP-PIP achieves an MCC of 0.505, accuracy of 0.752, and AUC of 0.762, outperforming ProIn-fuse, MultiFeatVotPIP, and StackPIP.
  arXiv  Detail & Related papers  (2026-02-22T08:09:01Z)
• PepEDiff: Zero-Shot Peptide Binder Design via Protein Embedding Diffusion [3.9876702935151225]
  We present PepEDiff, a novel peptide binder generator that designs binding sequences given a target receptor protein sequence and its pocket residues.<n>Our approach departs from existing methods by generating binder sequences directly in a continuous latent space derived from a pretrained protein embedding model.<n>Despite its simplicity, our method outperforms state-of-the-art approaches across benchmark tests and in the TIGIT case study.
  arXiv  Detail & Related papers  (2026-01-19T19:07:32Z)
• CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design [46.12506067241116]
  We present CODE (Chain of Diffusion Embeddings), a self evaluating metric to quantify topological frustration.<n>We propose CONFIDE, a unified evaluation framework that combines energetic and topological perspectives.<n>By combining data driven embeddings with theoretical insight, CODE and CONFIDE outperform existing metrics across a wide range of biomolecular systems.
  arXiv  Detail & Related papers  (2025-11-20T03:38:46Z)
• A Novel Framework for Multi-Modal Protein Representation Learning [13.33566214386641]
  We propose Diffused and Aligned Multi-modal Protein Embedding (DAMPE), a unified framework that addresses two core mechanisms.<n>First, we propose Optimal Transport (OT)-based representation alignment that establishes correspondence between intrinsic embedding spaces of different modalities.<n>Second, we develop a Conditional Graph Generation (CGG)-based information fusion method, where a condition encoder fuses the aligned intrinsic embeddings to provide informative cues for graph reconstruction.
  arXiv  Detail & Related papers  (2025-10-27T12:33:01Z)
• PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to Graphs [88.98041407783502]
  PRING is the first benchmark that evaluates protein-protein interaction prediction from a graph-level perspective.<n> PRING curates a high-quality, multi-species PPI network dataset comprising 21,484 proteins and 186,818 interactions.
  arXiv  Detail & Related papers  (2025-07-07T15:21:05Z)
• SCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions [0.0]
  We propose SCMPPI, a novel supervised contrastive multimodal framework for protein-protein interaction prediction.<n> experiments on eight benchmark datasets demonstrate its state-of-the-art accuracy(98.13%) and AUC(99.69%), along with excellent cross-species generalization.<n>Successful applications in CD9 networks, Wnt pathway analysis, and cancer-specific networks further highlight its potential for disease target discovery.
  arXiv  Detail & Related papers  (2025-04-03T15:34:02Z)
• A general language model for peptide identification [3.856457290796735]
  PDeepPP is a unified deep learning framework that integrates pretrained protein language models with a hybrid transformer-convolutional architecture.<n>By enabling large-scale, accurate peptide analysis, PDeepPP supports biomedical research and the discovery of novel therapeutic targets for disease treatment.
  arXiv  Detail & Related papers  (2025-02-21T17:31:22Z)
• Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding [57.89530563948755]
  This work provides a benchmark analysis of peptide encoding with advanced deep learning models. It serves as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
  arXiv  Detail & Related papers  (2023-07-17T00:43:33Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)

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.