Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure

• URL: http://arxiv.org/abs/2502.06846v2
• Date: Thu, 22 May 2025 09:43:36 GMT
• Title: Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure
• Authors: Zhicong Wang, Zicheng Ma, Ziqiang Cao, Changlong Zhou, Jun Zhang, Yiqin Gao,
• Abstract summary: We modified ProteinMPNN to encode protein sequence and structural information in a unified way.<n>We used a large language model (LLM) to encode questions into vectors and developed a protein-text adapter to compress protein information into virtual tokens.
• Score: 7.9473027178525975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivation: Proteins are of great significance in living organisms. However, understanding their functions encounters numerous challenges, such as insufficient integration of multimodal information, a large number of training parameters, limited flexibility of classification-based methods, and the lack of systematic evaluation metrics for protein Q&A systems. To tackle these issues, we propose the Prot2Chat framework. Results: We modified ProteinMPNN to encode protein sequence and structural information in a unified way. We used a large language model (LLM) to encode questions into vectors and developed a protein-text adapter to compress protein information into virtual tokens based on these vectors, achieving the early fusion of text and protein information. Finally, the same LLM reads the virtual tokens and the questions to generate answers. To optimize training efficiency, we froze the encoder and employed Low-Rank Adaptation (LoRA) techniques for the LLM. Experiments on two datasets show that both automated metrics and expert evaluations demonstrate the superior performance of our model, and zero-shot prediction results highlight its generalization ability. The models and codes are available at https://github.com/ wangzc1233/Prot2Chat. Contact: zqcao@suda.edu.cn or wangzc025@163.com Key words: Protein Q&A, Early-Fusion, LLM

Related papers

• Rethinking Text-based Protein Understanding: Retrieval or LLM? [26.278517638774005]
  protein-text models have gained significant attention for their potential in protein generation and understanding.<n>Current approaches focus on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment.<n>We propose a retrieval-enhanced method, which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.
  arXiv  Detail & Related papers  (2025-05-26T06:25:43Z)
• ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models [8.520384176663423]
  Large language models have made remarkable progress in the field of molecular science. In protein science, the amino acid sequence serves as the sole tokenizer for LLMs. We introduce a novel framework, Prot, which tokenizes the protein sequences, structures, and textual information into a unified discrete space.
  arXiv  Detail & Related papers  (2025-03-11T08:43:05Z)
• Protein Large Language Models: A Comprehensive Survey [71.65899614084853]
  Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. This work provides the first comprehensive overview of Protein LLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications.
  arXiv  Detail & Related papers  (2025-02-21T19:22:10Z)
• Computational Protein Science in the Era of Large Language Models (LLMs) [54.35488233989787]
  Computational protein science is dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm.<n>Recently, Language Models (pLMs) have emerged as a milestone in AI due to their unprecedented language processing & generalization capability.
  arXiv  Detail & Related papers  (2025-01-17T16:21:18Z)
• EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations [28.298740080002077]
  Current Large Language Models (LLMs) for understanding proteins primarily treats amino acid sequences as a text modality.<n>EvoLlama is a framework that connects a structure-based encoder, a sequence-based protein encoder and an LLM for protein understanding.<n>Our experiments show that EvoLlama's protein understanding capabilities have been significantly enhanced.
  arXiv  Detail & Related papers  (2024-12-16T10:01:33Z)
• 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. Most protein LMs are based on the Transformer architecture trained on individual proteins with short context lengths. 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)
• Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding [43.811432723460534]
  We introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach integrates a noval structure-aware module into pLMs to inform them with structural knowledge, and then connects these enhanced pLMs to large language models (LLMs) to generate understanding of proteins. We construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model.
  arXiv  Detail & Related papers  (2024-10-04T16:02:50Z)
• A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding [10.652670673334486]
  ProteinLMBench is the first benchmark dataset consisting of 944 manually verified multiple-choice questions for assessing the protein understanding capabilities of LLMs. ProteinLMDataset is a dataset specifically designed for further self-supervised pretraining and supervised fine-tuning. InternLM2-7B, pretrained and fine-tuned on the ProteinLMDataset, outperforms GPT-4 on ProteinLMBench, achieving the highest accuracy score.
  arXiv  Detail & Related papers  (2024-06-08T18:11:30Z)
• ProtT3: Protein-to-Text Generation for Text-based Protein Understanding [88.43323947543996]
  Language Models (LMs) excel in understanding textual descriptions of proteins. Protein Language Models (PLMs) can understand and convert protein data into high-quality representations, but struggle to process texts. We introduce ProtT3, a framework for Protein-to-Text Generation for Text-based Protein Understanding.
  arXiv  Detail & Related papers  (2024-05-21T08:06:13Z)
• 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)
• 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)
• ProtChatGPT: Towards Understanding Proteins with Large Language Models [33.71286652570309]
  We introduce ProtChatGPT, which aims at learning and understanding protein structures via natural languages.<n>ProtChatGPT enables users to upload proteins, ask questions, and engage in interactive conversations to produce comprehensive answers.
  arXiv  Detail & Related papers  (2024-02-15T01:22:30Z)
• Endowing Protein Language Models with Structural Knowledge [5.587293092389789]
  We introduce a novel framework that enhances protein language models by integrating protein structural data. The refined model, termed Protein Structure Transformer (PST), is further pretrained on a small protein structure database. PST consistently outperforms the state-of-the-art foundation model for protein sequences, ESM-2, setting a new benchmark in protein function prediction.
  arXiv  Detail & Related papers  (2024-01-26T12:47:54Z)
• 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.<n>xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories.<n>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)
• 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)
• 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)
• A Systematic Study of Joint Representation Learning on Protein Sequences and Structures [38.94729758958265]
  Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequence representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. Our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM with distinct structure encoders.
  arXiv  Detail & Related papers  (2023-03-11T01:24:10Z)
• Structure-informed Language Models Are Protein Designers [69.70134899296912]
  We present LM-Design, a generic approach to reprogramming sequence-based protein language models (pLMs) We conduct a structural surgery on pLMs, where a lightweight structural adapter is implanted into pLMs and endows it with structural awareness. Experiments show that our approach outperforms the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-03T10:49:52Z)
• ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts [22.870765825298268]
  We build a ProtST dataset to augment protein sequences with text descriptions of their functions and other important properties. During pre-training, we design three types of tasks, i.e., unimodal mask prediction, multimodal representation alignment and multimodal mask prediction. On downstream tasks, ProtST enables both supervised learning and zero-shot prediction.
  arXiv  Detail & Related papers  (2023-01-28T00:58:48Z)
• Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model [93.9943278892735]
  Key problem in protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences. We propose a novel method to capture this information directly by pre-training via a dedicated language model, i.e., Pairwise Masked Language Model (PMLM) Our result shows that the proposed method can effectively capture the interresidue correlations and improves the performance of contact prediction by up to 9% compared to the baseline.
  arXiv  Detail & Related papers  (2021-10-29T04:01:32Z)

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.