变压器
计算机科学
肽
语言模型
溶血
人工智能
自然语言处理
化学
工程类
生物
生物化学
电气工程
电压
免疫学
作者
Chakradhar Guntuboina,Asit Kumar Das,Parisa Mollaei,Seong Won Kim,Amir Barati Farimani
标识
DOI:10.1021/acs.jpclett.3c02398
摘要
Recent advances in language models have enabled the protein modeling community with a powerful tool that uses transformers to represent protein sequences as text. This breakthrough enables a sequence-to-property prediction for peptides without relying on explicit structural data. Inspired by the recent progress in the field of large language models, we present PeptideBERT, a protein language model specifically tailored for predicting essential peptide properties such as hemolysis, solubility, and nonfouling. The PeptideBERT utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. Through fine-tuning the pretrained model for the three downstream tasks, our model is state of the art (SOTA) in predicting hemolysis, which is crucial for determining a peptide's potential to induce red blood cells as well as nonfouling properties. Leveraging primarily shorter sequences and a data set with negative samples predominantly associated with insoluble peptides, our model showcases remarkable performance.
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