Enhancing Multimodal Protein Function Prediction Through Dual-Branch Dynamic Selection with Reconstructive Pre-Training

• URL: http://arxiv.org/abs/2511.04040v1
• Date: Thu, 06 Nov 2025 04:19:42 GMT
• Title: Enhancing Multimodal Protein Function Prediction Through Dual-Branch Dynamic Selection with Reconstructive Pre-Training
• Authors: Xiaoling Luo, Peng Chen, Chengliang Liu, Xiaopeng Jin, Jie Wen, Yumeng Liu, Junsong Wang,
• Abstract summary: We propose a multimodal protein function prediction method (DSRPGO) by utilizing dynamic selection and reconstructive pre-training mechanisms.<n>Our proposed DSRPGO model improves significantly in BPO, MFO, and CCO on human datasets.
• Score: 19.3863460349536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal protein features play a crucial role in protein function prediction. However, these features encompass a wide range of information, ranging from structural data and sequence features to protein attributes and interaction networks, making it challenging to decipher their complex interconnections. In this work, we propose a multimodal protein function prediction method (DSRPGO) by utilizing dynamic selection and reconstructive pre-training mechanisms. To acquire complex protein information, we introduce reconstructive pre-training to mine more fine-grained information with low semantic levels. Moreover, we put forward the Bidirectional Interaction Module (BInM) to facilitate interactive learning among multimodal features. Additionally, to address the difficulty of hierarchical multi-label classification in this task, a Dynamic Selection Module (DSM) is designed to select the feature representation that is most conducive to current protein function prediction. Our proposed DSRPGO model improves significantly in BPO, MFO, and CCO on human datasets, thereby outperforming other benchmark models.

Related papers

• Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing [51.12821379640881]
  Autoregressive (AR) models offer holistic, bidirectional representations but face challenges with generative coherence and scalability.<n>We propose a hybrid framework enhancing AR generation by dynamically integrating rich contextual information from non-autoregressive mechanisms.<n>A novel cross-decoder attention module enables the AR decoder to iteratively query and integrate these bidirectional features.
  arXiv  Detail & Related papers  (2025-10-09T12:52:55Z)
• Foundation Model for Skeleton-Based Human Action Understanding [56.89025287217221]
  This paper presents a Unified Skeleton-based Dense Representation Learning framework.<n>USDRL consists of a Transformer-based Dense Spatio-Temporal (DSTE), Multi-Grained Feature Decorrelation (MG-FD), and Multi-Perspective Consistency Training (MPCT)
  arXiv  Detail & Related papers  (2025-08-18T02:42:16Z)
• 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)
• An All-Atom Generative Model for Designing Protein Complexes [65.06317264153175]
  APM (All-Atom Protein Generative Model) is a model specifically designed for modeling multi-chain proteins.<n>It is capable of precisely modeling inter-chain interactions and designing protein complexes with binding capabilities from scratch.<n>It also performs folding and inverse-folding tasks for multi-chain proteins.
  arXiv  Detail & Related papers  (2025-04-17T16:37:41Z)
• ProtCLIP: Function-Informed Protein Multi-Modal Learning [18.61302416993122]
  We develop ProtCLIP, a multi-modality foundation model that represents function-aware protein embeddings.<n>Our ProtCLIP consistently achieves SOTA performance, with remarkable improvements of 75% on average in five cross-modal transformation benchmarks.<n>The experimental results verify the extraordinary potential of ProtCLIP serving as the protein multi-modality foundation model.
  arXiv  Detail & Related papers  (2024-12-28T04:23:47Z)
• SeqProFT: Applying LoRA Finetuning for Sequence-only Protein Property Predictions [8.112057136324431]
  This study employs the LoRA method to perform end-to-end fine-tuning of the ESM-2 model. A multi-head attention mechanism is integrated into the downstream network to combine sequence features with contact map information.
  arXiv  Detail & Related papers  (2024-11-18T12:40:39Z)
• OneProt: Towards Multi-Modal Protein Foundation Models [6.9092913261646345]
  We introduce OneProt, a multi-modal AI for proteins that integrates structural, sequence, text, and binding site data.<n>Using the ImageBind framework, OneProt aligns the latent spaces of protein modality encoders in a lightweight fine-tuning scheme.<n>This work expands the horizons of multi-modal protein models, paving the way for transformative applications in drug discovery, biocatalytic reaction planning, and protein engineering.
  arXiv  Detail & Related papers  (2024-11-07T16:54:54Z)
• 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)
• Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation [0.0]
  This work presents an omics-driven modeling pipeline that integrates machine-learning tools.<n> Random forests and permutation feature importance are proposed to mine omics datasets.<n> Continuous and differentiable machine-learning functions can be trained to link the reduced omics feature set to key components of the dynamic model.
  arXiv  Detail & Related papers  (2024-10-24T15:50:35Z)
• Progressive Multi-Modality Learning for Inverse Protein Folding [47.095862120116976]
  We propose a novel protein design paradigm called MMDesign, which leverages multi-modality transfer learning. MMDesign is the first framework that combines a pretrained structural module with a pretrained contextual module, using an auto-encoder (AE) based language model to incorporate prior protein semantic knowledge. Experimental results, only training with the small dataset, demonstrate that MMDesign consistently outperforms baselines on various public benchmarks.
  arXiv  Detail & Related papers  (2023-12-11T10:59:23Z)
• Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models [51.57843608615827]
  The ability to precisely predict protein thermostability is pivotal for various subfields and applications in biochemistry. We introduce an ESM-assisted efficient approach that integrates protein sequence and structural features to predict the thermostability changes in protein upon single-point mutations.
  arXiv  Detail & Related papers  (2023-12-07T03:25:49Z)
• Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers [18.498779242323582]
  We propose a novel approach, Prot2Text, which predicts a protein's function in a free text style. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types.
  arXiv  Detail & Related papers  (2023-07-25T09:35:43Z)

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.