领域：学术论文 · 应用学科

Protein Language Models have become important tools in computational biology for tasks such as protein structure prediction, functional annotation, and sequence design. These models draw on recent advances in Natural Language Processing , which uses Deep Learning techniques to capture complex relationships within protein sequences. However, optimizing Protein Language Models for specific biological tasks is still a huge challenge due to the large volume of protein sequence space and the complexity of folding and interaction. To transform general yet unfocused protein Large Language Models into expert tools capable of solving specific biological challenges, an optimized strategy that combines Fine-Tuning and Prompt Engineering has emerged. The essence of this approach lies in a two-pronged process: on one hand, Parameter-Efficient Fine-Tuning uses specialized datasets related to specific tasks to further train the model, much like providing specialized training to equip the model with deep, domain-specific knowledge and fundamentally enhance its performance in targeted applications. On the other hand, advanced Prompt Engineering serves as a “precise remote control,” enabling researchers to deliver complex design objectives and constraints to the model in real time and at a fine-grained level through well-structured and information-rich instructions, thereby precisely steering its generation process. These two techniques complement each other—Fine-Tuning endows the model with robust internal expertise, while Prompt Engineering provides the means to harness and direct this expertise. The synergy between them ultimately gives rise to the next generation of protein design tools, which are both deeply specialized and highly controllable, dramatically accelerating innovation in fields such as drug discovery and synthetic biology. Protein science, the study of protein structure, function, and interactions in living organisms, has entered a new era with the emergence of Large Language Models. This convergence leverages various Deep Learning models, particularly the Transformer, to analyze and predict protein sequences, structures, and functions, accelerating biomedical research and drug development. For example,  ProteinBERT demonstrated the effectiveness for protein sequence analysis, while xTrimoPGLM, with its 100 billion parameters, established new capabilities in both protein understanding and generation. ProteinLMBench is a comprehensive, manually annotated benchmark for a comprehensive assessment of under- standing and representation of protein knowledge.TourSynbio-7B is a multi-modal large model designed for protein engineering and it does not require an external protein encoder.
AutoPE mainly introduces a proxy framework called AutoProteinEngine, which utilizes Large Language Models to implement multimodal automated machine learning in protein engineering. However, the reason why general Large Language Models perform poorly on scientific tasks fundamentally lies in the inherent flaws of the datasets used to train them. Simply expanding the size of general web-text corpora is far from sufficient, as these datasets cannot meet the rigorous requirements of scientific research in terms of quality, structure, and content. The specific limitations are as follows:
       Contamination and low signal-to-noise ratio in general corpora: Models trained on such "polluted" data tend to develop "world knowledge" that is biased toward common sense rather than rigorous scientific facts. This leads to a propensity for hallucinations, namely, generating statements that sound plausible but are entirely incorrect, and makes it difficult to distinguish between peer-reviewed knowledge and arbitrary claims found on the internet. Neglect of the multimodality of scientific knowledge: During the processing of traditional text datasets, structured information is often flattened or discarded, causing the model to lose the essence of the "language" of science. A model exposed only to plain text cannot truly understand or manipulate a chemical reaction equation, nor can it correctly generate or interpret a complex mathematical derivation. Insufficient coverage of "long-tail" scientific knowledge: As a result, the model exhibits breadth but lacks depth in knowledge. It may be able to discuss basic physics fluently but remains completely ignorant of a particular branch of condensed matter physics or the latest algorithms in bioinformatics. This imbalance in knowledge coverage severely limits its usefulness as a professional research assistant. Lack of knowledge provenance and citation information: The model cannot provide citations or sources for its generated content, which is fatal in scientific applications. It cannot distinguish between widely accepted theories and controversial hypotheses, nor can it help researchers trace the original source of information, greatly undermining its reliability. Static and outdated information: The knowledge acquired by the model will inevitably become outdated. For rapidly fields such as Artificial Intelligence or gene editing, a model trained on data from several years ago may provide information that is incorrect or no longer applicable. Protein structure prediction and functional analysis are of great significance in the field of bioinformatics. In recent years, the application of protein large language model has greatly improved the accuracy of protein sequence analysis. However, how to efficiently Fine-Tuning the parameters of large language models for optimal performance remains a challenge Overwhelming hardware barriers, especially the “VRAM wall.” This is the most immediate and fundamental limitation. Full parameter Fine-Tuning requires loading all model parameters, gradients, and optimizer states into GPU VRAM during training. Overall, fully Fine-Tuning a 7B-parameter model may require close to 100GB of VRAM. This far exceeds the capacity of most consumer GPUs (such as the RTX 4090, with 24GB) and even many professional-grade GPUs, creating a “VRAM wall” that ordinary
users cannot overcome. Inefficiency in deployment and storage. Full parameter Fine-Tuning generates a complete and independent model copy for each specific downstream task. This leads to the problem of “model copy proliferation.” For organizations with limited resources, simply storing these massive model files is already overwhelming, let alone configuring a separate and expensive inference service for each model in production environments. This results in extremely high costs for model scaling and maintenance. Instability during training and “catastrophic forgetting.” On limited hardware, researchers may be forced to use extremely small batch sizes just to make training feasible. Too small a batch size causes gradients to be noisy, making the training process highly unstable and difficult to converge. Improvements in task-specific performance may come at the cost of degrading general capabilities, which is unacceptable in many application scenarios. At the same time, unstable training also increases the difficulty and time cost of hyperparameter tuning. Extremely low iteration efficiency and high trial-and-error costs. Each training cycle of full parameter Fine-Tuning is extremely time-consuming. With limited computational resources, completing a single Fine-Tuning experiment may take days or even weeks. This greatly slows the pace of research and innovation. Researchers are unable to quickly conduct experiments, test ideas, or adjust hyperparameters. The high time and energy costs make exploratory research a luxury, hindering the popularization and democratization of the technology. At present, the commonly used evaluation indicators for protein language models are limited, mainly focusing on sequence prediction, structure prediction, etc., lacking rich downstream functional or interactive evaluations. The evaluation system for Large Language Models in biomedicine is still not perfect, especially in downstream tasks such as interactive question answering and reasoning, where the problem of single evaluation indicators is more prominent. The evaluation of Protein Language Models mainly relies on a limited number of benchmarks, with insufficient consideration of diverse tasks and actual dialogue task. The dataset of Protein Language Models has problems such as insufficient coverage of professional domain knowledge, untimely data updates, severe data pollution, and difficulty in ensuring scientificity . The Fine-Tuning of Protein Language Models faces significant limitations: Firstly, full-parameter Fine-Tuning has extremely high requirements for video memory and hardware, which are unaffordable for ordinary researchers; Secondly, different tasks require the separate saving and deployment of complete models, which
leads to a significant increase in storage and operation and maintenance costs. Secondly, due to the limitations of hardware resources, unstable gradients and catastrophic forgetting are prone to occur during the training process, making it difficult to guarantee the model’s performance. Finally, the long training time and the low efficiency of experiments and parameter adjustment have greatly affected the promotion of research and application. At present, the evaluation methods for protein language models are relatively limited, mainly focusing on sequence and structure prediction, and lacking comprehensive assessment of downstream functionality and interactive capabilities. The evaluation system for large biomedical models is also not fully developed; in practical scenarios such as question answering and reasoning, the evaluation metrics are rather single-dimensional, making it difficult to fully demonstrate the overall application level of the models. 
     Based on the above content, the research questions are as follows. Firstly, this study optimized the application of Protein Language Models in the field of bioinformatics. By combining Fine-Tuning techniques and Prompt Engineering methods, it effectively enhanced the model’s capabilities in tasks such as protein sequence understanding, functional prediction, and structural inference. Secondly, this method can achieve efficient adaptation to specific downstream tasks without significantly increasing the consumption of computing resources, thereby promoting the automation and intelligence of protein science research. Finally, this research provides new ideas for the customized application of cross-domain large models, opens up broader prospects for the integration of life sciences and Artificial Intelligence, and helps accelerate the progress of practical applications such as drug design and disease mechanism analysis.


翻译考点：
考点1： "Large Language Models" 应译为"大语言模型"
考点2:   "multi-modal large model"应译为"多模态大模型"