MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model.

Journal: BMC biology
PMID: 40016695

Abstract

BACKGROUND: Cyclic peptides, known for their high binding affinity and low toxicity, show potential as innovative drugs for targeting "undruggable" proteins. However, their therapeutic efficacy is often hindered by poor membrane permeability. Over the past decade, the FDA has approved an average of one macrocyclic peptide drug per year, with romidepsin being the only one targeting an intracellular site. Biological experiments to measure permeability are time-consuming and labor-intensive. Rapid assessment of cyclic peptide permeability is crucial for their development.

Authors

  • Zixu Wang
    Laboratory of Veterinary Anatomy, College of Animal Medicine, China Agricultural University, Haidian, Beijing, 100193, People's Republic of China.
  • Yangyang Chen
  • Yifan Shang
    College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, People's Republic of China.
  • Xiulong Yang
    School of Computer Science, Central China Normal University, Wuhan, 430079, People's Republic of China.
  • Wenqiong Pan
    Department of Clinical Pharmacy, Jilin University, Changchun, Jilin, 130025, People's Republic of China.
  • Xiucai Ye
    Department of Computer Science, University of Tsukuba, Tsukuba, Science City, Japan.
  • Tetsuya Sakurai
    Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
  • Xiangxiang Zeng
    Department of Computer Science, Hunan University, Changsha, China.

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