InstructPro：自然语言引导的配体结合蛋白设计
InstructPro: Natural Language Guided Ligand-Binding Protein Design
AI总结：
InstructPro通过自然语言指导生成模型，实现了高亲和力配体结合蛋白的设计，显著提升了从头蛋白设计的性能和泛化能力。
摘要：
从头设计具有定制功能的配体结合蛋白对于推进生物技术和分子医学至关重要，但现有AI方法受限于稀缺的蛋白质-配体复合物数据。为克服这一数据瓶颈，我们利用大量描述蛋白质-配体相互作用的自然语言描述。在这里，我们介绍了InstructPro，一组生成模型，能够根据自然语言指令和配体公式设计蛋白质。InstructPro生成的蛋白质序列与指定的功能描述和配体靶点一致。为了实现训练和评估，我们开发了InstructProBench，一个包含960万（功能描述、配体、蛋白质）三元组的大规模数据集。我们训练了两个模型变体——InstructPro-1B和InstructPro-3B，它们在强基线模型上表现显著优于现有方法。InstructPro-1B在可见配体上实现了AlphaFold3 ipTM为0.918和结合亲和力为-8.764，同时在零样本设置中保持稳健性能，得分为0.869和-6.713，分别。这些结果伴随着新颖性得分为70.1%和68.8%，突显了模型在训练集之外的泛化能力。此外，该模型产生了优于的结合自由能为-20.9 kcal/mol和平均5.82个分子间氢键，验证了其在设计高亲和力配体结合蛋白方面的熟练程度。值得注意的是，扩大到InstructPro-3B进一步提高了零样本ipTM到0.882，结合亲和力到-6.797，结合自由能到-25.8 kcal/mol，显示出随着模型容量增加而明显提升的性能。这些发现突显了自然语言引导生成模型在缓解传统基于结构方法的数据瓶颈方面的强大能力，显著扩展了从头蛋白设计的范围。
英文摘要：
The de novo design of ligand-binding proteins with tailored functions is essential for advancing biotechnology and molecular medicine, yet existing AI approaches are limited by scarce protein-ligand complex data. To circumvent this data bottleneck, we leverage the abundant natural language descriptions characterizing protein-ligand interactions. Here, we introduce InstructPro, a family of generative models that design proteins following the guidance of natural language instructions and ligand formulas. InstructPro produces protein sequences consistent with specified function descriptions and ligand targets. To enable training and evaluation, we develop InstructProBench, a large-scale dataset of 9.6 million (function description, ligand, protein) triples. We train two model variants -- InstructPro-1B and InstructPro-3B -- that substantially outperform strong baselines. InstructPro-1B achieves an AlphaFold3 ipTM of 0.918 and a binding affinity of -8.764 on seen ligands, while maintaining robust performance in a zero-shot setting with scores of 0.869 and -6.713, respectively. These results are accompanied by novelty scores of 70.1% and 68.8%, underscoring the model's ability to generalize beyond the training set. Furthermore, the model yields a superior binding free energy of -20.9 kcal/mol and an average of 5.82 intermolecular hydrogen bonds, validating its proficiency in designing high-affinity ligand-binding proteins. Notably, scaling to InstructPro-3B further improves the zero-shot ipTM to 0.882, binding affinity to -6.797, and binding free energy to -25.8 kcal/mol, demonstrating clear performance gains associated with increased model capacity. These findings highlight the power of natural language-guided generative models to mitigate the data bottlenecks in traditional structure-based methods, significantly broadening the scope of de novo protein design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
