Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers

• URL: http://arxiv.org/abs/2307.14367v3
• Date: Sat, 20 Apr 2024 09:10:47 GMT
• Title: Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers
• Authors: Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, Michalis Vazirgiannis,
• Abstract summary: 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.
• Score: 18.498779242323582
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e. assigning predefined labels to proteins. In this work, we propose a novel approach, Prot2Text, which predicts a protein's function in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including protein sequence, structure, and textual annotation and description. This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate functional descriptions. To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Prot2Text. These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate function prediction of existing as well as first-to-see proteins.

Related papers

• Prot2Chat: Protein LLM with Early Fusion of Sequence and Structure [7.9473027178525975]
  Prot2Chat is a novel framework that integrates multimodal protein representations with natural language through a unified module. Our model incorporates a modified ProteinMPNN encoder, which encodes protein sequence and structural information in a unified manner, and a protein-text adapter with cross-attention mechanisms.
  arXiv  Detail & Related papers  (2025-02-07T05:23:16Z)
• ProtCLIP: Function-Informed Protein Multi-Modal Learning [18.61302416993122]
  We develop ProtCLIP, a multi-modality foundation model that represents function-aware protein embeddings. Our ProtCLIP consistently achieves SOTA performance, with remarkable improvements of 75% on average in five cross-modal transformation benchmarks. 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)
• Multi-Scale Representation Learning for Protein Fitness Prediction [31.735234482320283]
  Previous methods have primarily relied on self-supervised models trained on vast, unlabeled protein sequence or structure datasets. We introduce the Sequence-Structure-Surface Fitness (S3F) model - a novel multimodal representation learning framework that integrates protein features across several scales. Our approach combines sequence representations from a protein language model with Geometric Vector Perceptron networks encoding protein backbone and detailed surface topology.
  arXiv  Detail & Related papers  (2024-12-02T04:28:10Z)
• OneProt: Towards Multi-Modal Protein Foundation Models [5.440531199006399]
  We introduce OneProt, a multi-modal AI for proteins that integrates structural, sequence, alignment, and binding site data. It surpasses state-of-the-art methods in various downstream tasks, including metal ion binding classification, gene-ontology annotation, and enzyme function prediction. This work expands multi-modal capabilities in protein models, paving the way for applications in drug discovery, biocatalytic reaction planning, and protein engineering.
  arXiv  Detail & Related papers  (2024-11-07T16:54:54Z)
• ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training [82.37346937497136]
  We propose a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mechanism, enabling it to handle complex inputs. By developing a specialized protein vocabulary, we equip the model with the capability to predict not just natural language but also proteins from a vast pool of candidates.
  arXiv  Detail & Related papers  (2024-02-28T01:29:55Z)
• xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein [74.64101864289572]
  We propose a unified protein language model, xTrimoPGLM, to address protein understanding and generation tasks simultaneously. xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories. It can also generate de novo protein sequences following the principles of natural ones, and can perform programmable generation after supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-01-11T15:03:17Z)
• 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)
• Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning [2.01243755755303]
  We introduce TargetVAE, a target-aware auto-encoder that generates with high binding affinities to arbitrary protein targets. This is the first effort to unify different representations of proteins into a single model that we name as Protein Multimodal Network (PMN)
  arXiv  Detail & Related papers  (2023-08-02T12:08:17Z)
• 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)
• Multi-Scale Representation Learning on Proteins [78.31410227443102]
  This paper introduces a multi-scale graph construction of a protein -- HoloProt. The surface captures coarser details of the protein, while sequence as primary component and structure captures finer details. Our graph encoder then learns a multi-scale representation by allowing each level to integrate the encoding from level(s) below with the graph at that level.
  arXiv  Detail & Related papers  (2022-04-04T08:29:17Z)
• BERTology Meets Biology: Interpreting Attention in Protein Language Models [124.8966298974842]
  We demonstrate methods for analyzing protein Transformer models through the lens of attention. We show that attention captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure. We also present a three-dimensional visualization of the interaction between attention and protein structure.
  arXiv  Detail & Related papers  (2020-06-26T21:50:17Z)

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.