pLM4ACE: A protein language model based predictor for antihypertensive peptide screening

Zhenjiao Du, Xingjian Ding, William Hsu, Arslan Munir, Yixiang Xu, Yonghui Li

Research output: Contribution to journalArticlepeer-review


Angiotensin-I converting enzyme (ACE) regulates the renin-angiotensin system and is a drug target in clinical treatment for hypertension. This study aims to develop a protein language model (pLM) with evolutionary scale modeling (ESM-2) embeddings that is trained on experimental data to screen peptides with strong ACE inhibitory activity. Twelve conventional peptide embedding approaches and five machine learning (ML) modeling methods were also tested for performance comparison. Among the 65 classifiers tested, logistic regression with ESM-2 embeddings showed the best performance, with balanced accuracy (BACC), Matthews correlation coefficient (MCC), and area under the curve of 0.883 ± 0.017, 0.77 ± 0.032, and 0.96 ± 0.009, respectively. Multilayer perceptron and support vector machine also exhibited great compatibility with ESM-2 embeddings. The ESM-2 embeddings showed superior performance in enhancing the prediction model compared to the 12 traditional embedding methods. A user-friendly webserver ( with the top three models is now freely available.

Original languageEnglish (US)
Article number137162
JournalFood Chemistry
StatePublished - Jan 15 2024


  • ACE inhibitory peptide
  • Antihypertension
  • Bioactive peptide
  • Machine learning
  • Protein language model

ASJC Scopus subject areas

  • Analytical Chemistry
  • Food Science


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