Core Insights - The article discusses the development of ProTrek, a novel trimodal protein language model that integrates amino acid sequences, three-dimensional structures, and natural language descriptions for advanced protein searches [3][9][20]. Group 1: Challenges and Opportunities in Protein Research - Proteins are essential for life, and understanding the complex relationship between their sequences, structures, and functions is crucial for molecular science and pharmacology [6]. - Traditional tools like BLAST and Foldseek are limited to single-modal comparisons, hindering the discovery of cross-modal relationships between sequences, structures, and functions [6][9]. - Approximately 30% of proteins in the UniProt database remain functionally unannotated due to their distant phylogenetic relationships with known homologs, likened to "dark matter" in the protein universe [6][9]. Group 2: ProTrek's Innovative Framework - ProTrek employs a unique trimodal framework that unifies three core protein information types: amino acid sequences (1D), three-dimensional structures (spatial), and natural language function descriptions (semantic) [9][20]. - The model utilizes a bidirectional alignment framework to establish strong correlations across sequence-structure, structure-function, and function-sequence dimensions [9][21]. Group 3: Performance and Experimental Validation - ProTrek demonstrates superior performance, outperforming existing top methods like ProteinDT and ProtST by over 30-60 times in standard protein function retrieval benchmarks [11][21]. - The model's global representation learning capability allows it to identify convergent evolution proteins that have significant sequence and structure differences but perform similar functions [11][21]. - Experimental validation showed ProTrek's ability to discover new proteins with similar functions to human UDG, achieving higher editing efficiency and lower off-target effects compared to existing tools [15][23]. Group 4: Implications and Future Prospects - ProTrek enhances the efficiency and depth of protein research, facilitating large-scale annotation of unknown protein functions and accelerating enzyme discovery and drug design [18][23]. - The model's integration of complex molecular data with intuitive natural language promotes a better understanding of the protein world [18][23]. - ProTrek's capabilities are expected to lead to new scientific discoveries across various fields of protein science [18][23].

Core Viewpoint - The article discusses the launch of PFMBench, a comprehensive benchmarking tool for evaluating protein foundation models (PFMs) across various tasks, addressing the need for standardized assessments in the rapidly evolving field of protein science driven by AI advancements [2][3][24]. Summary by Sections Introduction to Protein Science and AI - Proteins are essential for life activities, and understanding them is crucial for disease treatment and new drug development. The AI wave is revolutionizing protein science, with models like ESM-2 and ProtT5 emerging to predict protein structures and functions [2]. The Need for Benchmarking - The protein model field faces challenges similar to comparing students taking different exams, leading to difficulties in assessing model performance. Existing benchmarks are either too limited in tasks or overlook multimodal models, resulting in fragmented evaluation results [7][8]. PFMBench Development - PFMBench was developed by West Lake University and BioMap, encompassing 38 tasks and 17 models across eight protein science domains, serving as a "final exam" for protein models [10]. Core Design of PFMBench - PFMBench is designed with modularity and efficiency in mind, integrating tasks, models, and tuning methods into a unified framework. It includes: 1. **Task Library**: 38 tasks categorized into eight types, covering the entire protein lifecycle [12]. 2. **Model Library**: 17 top models categorized into four types, with 12 core models selected based on performance benchmarks [14]. 3. **Tuning Protocols**: Supports parameter-efficient fine-tuning methods, allowing for quick adaptation to new tasks [15]. Key Findings from PFMBench - The analysis revealed four critical conclusions: 1. Task relevance allows for focusing on 11 representative tasks instead of testing all 38 [18]. 2. Multimodal models outperform pure sequence models, with ProTrek achieving a 75% win rate compared to ESM-2's 50% [19]. 3. Zero-shot evaluations may mislead developers, emphasizing the need for supervised tasks [20]. 4. The cost-effectiveness of model scaling is low, with DoRA fine-tuning emerging as a superior method [21]. Significance of PFMBench - PFMBench is a milestone in the industry, providing a standardized evaluation framework that promotes innovation and guides future research directions. It aims to accelerate biopharmaceutical applications by offering reliable assessments that shorten development cycles [24][25].