TPepPro: a deep learning model for predicting peptide-protein interactions.

Journal: Bioinformatics (Oxford, England)
Published Date: Dec 26, 2024

Abstract

MOTIVATION: Peptides and their derivatives hold potential as therapeutic agents. The rising interest in developing peptide drugs is evidenced by increasing approval rates by the FDA of USA. To identify the most potential peptides, study on peptide-protein interactions (PepPIs) presents a very important approach but poses considerable technical challenges. In experimental aspects, the transient nature of PepPIs and the high flexibility of peptides contribute to elevated costs and inefficiency. Traditional docking and molecular dynamics simulation methods require substantial computational resources, and the predictive accuracy of their results remain unsatisfactory.

Authors

  • Xiaohong Jin
    School of Electronic Information, Guangxi University for Nationalities, Nanning 530000, China.
  • Zimeng Chen
    Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
  • Dan Yu
    Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, People's Republic of China.
  • Qianhui Jiang
    Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China.
  • Zhuobin Chen
    School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, 66 Gongchang Road, Shenzhen, Guangdong, 518107, China.
  • Bin Yan
    National Digital Switching System Engineering and Technological Research Centre, Zhengzhou, China.
  • Jing Qin
    School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
  • Yong Liu
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.
  • Junwen Wang
    Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, Scottsdale, Arizona, USAWang.Junwen@mayo.edu.

Keywords

External Resources

Popular Topics
Recent Journals