Work

Multi-Objective Optimization Accelerates the De Novo Design of Antimicrobial Peptide for Staphylococcus aureus

Year: 2024

Type: article

Abstract: Humans have long used antibiotics to fight bacteria, but increasing drug resistance has reduced their effectiveness. Antimicrobial peptides (AMPs) are a promising alternative with natural broad-spectrum activity against bacteria and viruses. However, their instability and hemolysis limit their medical use, making the design and improvement of AMPs a key research focus. Designing antimicrobial peptides with multiple desired properties using machine learning is still challenging, especially with limited data. This study utilized a multi-objective optimization method, the non-dominated sorting genetic algorithm II (NSGA-II), to enhance the physicochemical properties of peptide sequences and identify those with improved antimicrobial activity. Combining NSGA-II with neural networks, the approach efficiently identified promising AMP candidates and accurately predicted their antibacterial effectiveness. This method significantly advances by optimizing factors like hydrophobicity, instability index, and aliphatic index to improve peptide stability. It offers a more efficient way to address the limitations of AMPs, paving the way for the development of safer and more effective antimicrobial treatments.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms252413688

Authors: Cheng-Hong Yang, Yi-Ling Chen, Tin-Ho Cheung, Li-Yeh Chuang

Institutions: Tainan University of Technology, Kaohsiung Medical University, National Kaohsiung University of Science and Technology, I-Shou University

Language: English

Topic: Antimicrobial Peptides and Activities

Subfield: Microbiology

Field: Immunology and Microbiology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,296