PeptideBERT：一种基于Transformer语言模型的肽性质预测方法
PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction
AI总结：
本文提出了基于ProtBERT的蛋白质语言模型PeptideBERT，通过微调实现了对肽的溶血性、溶解性和非污染性的高效预测，并在溶血性预测任务上达到了SOTA水平。
摘要：
语言模型的最新进展为蛋白质建模社区提供了强大的工具，因为蛋白质序列可以表示为文本。具体而言，利用Transformer的优势，无需显式结构数据即可实现序列到性质的预测。在本研究中，受大型语言模型（LLMs）最新进展的启发，我们引入了PeptideBERT，这是一种用于预测肽的三个关键性质（溶血性、溶解性和非污染性）的蛋白质语言模型。PeptideBERT利用了具有12个注意力头和12个隐藏层的ProtBERT预训练Transformer模型。随后，我们针对这三个下游任务对预训练模型进行了微调。我们的模型在预测溶血性方面达到了最先进（SOTA）水平，该任务旨在确定肽诱导红细胞裂解的潜力。我们的PeptideBERT非污染模型在预测肽抵抗非特异性相互作用的能力方面也取得了显著的准确率。该模型主要在较短的序列上训练，得益于负例主要与不溶性肽相关的数据集。本研究所用的代码、模型和数据可在以下网址免费获取：https://github.com/ChakradharG/PeptideBERT
英文摘要：
Recent advances in Language Models have enabled the protein modeling community with a powerful tool since protein sequences can be represented as text. Specifically, by taking advantage of Transformers, sequence-to-property prediction will be amenable without the need for explicit structural data. In this work, inspired by recent progress in Large Language Models (LLMs), we introduce PeptideBERT, a protein language model for predicting three key properties of peptides (hemolysis, solubility, and non-fouling). The PeptideBert utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. We then finetuned the pretrained model for the three downstream tasks. Our model has achieved state of the art (SOTA) for predicting Hemolysis, which is a task for determining peptide's potential to induce red blood cell lysis. Our PeptideBert non-fouling model also achieved remarkable accuracy in predicting peptide's capacity to resist non-specific interactions. This model, trained predominantly on shorter sequences, benefits from the dataset where negative examples are largely associated with insoluble peptides. Codes, models, and data used in this study are freely available at: https://github.com/ChakradharG/PeptideBERT
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。
