Multi-Objective Optimization Accelerates the De Novo Design of Antimicrobial Peptide for Staphylococcus aureus DOI Open Access
Cheng‐Hong Yang,

Yi-Ling Chen,

Terence K.M. Cheung

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(24), С. 13688 - 13688

Опубликована: Дек. 21, 2024

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, instability hemolysis limit medical use, making the design improvement of AMPs key research focus. Designing antimicrobial multiple desired properties using machine learning is still challenging, especially limited data. This study utilized multi-objective optimization method, non-dominated sorting genetic algorithm II (NSGA-II), enhance physicochemical peptide sequences identify those improved activity. Combining NSGA-II neural networks, approach efficiently identified AMP candidates accurately predicted antibacterial method significantly advances by optimizing factors like hydrophobicity, index, aliphatic index improve stability. It offers more efficient way address limitations AMPs, paving for development safer effective treatments.

Язык: Английский

Antimicrobial peptides: Opportunities and challenges in overcoming resistance DOI Creative Commons

Cezara Bucataru,

Corina Ciobănaşu

Microbiological Research, Год журнала: 2024, Номер 286, С. 127822 - 127822

Опубликована: Июнь 26, 2024

Antibiotic resistance represents a global health threat, challenging the efficacy of traditional antimicrobial agents and necessitating innovative approaches to combat infectious diseases. Among these alternatives, peptides have emerged as promising candidates against resistant pathogens. Unlike antibiotics with only one target, can use different mechanisms destroy bacteria, low toxicity mammalian cells compared many conventional antibiotics. Antimicrobial (AMPs) encouraging antibacterial properties are currently employed in clinical treatment pathogen infection, cancer, wound healing, cosmetics, or biotechnology. This review summarizes discusses drug resistance, limitations challenges AMPs peptide applications for combating drug-resistant bacterial infections, strategies enhance their capabilities.

Язык: Английский

Процитировано

41
Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides DOI Creative Commons
Kedong Yin, Ruifang Li, Shaojie Zhang

и другие.

Advanced Science, Год журнала: 2025, Номер unknown

Опубликована: Фев. 8, 2025

Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) potential candidates due to their specific mechanism of action, which makes them less prone developing drug resistance. An AFP de novo design method, Deep Learning-Quantitative Structure‒Activity Relationship Empirical Screening (DL-QSARES), is developed by integrating deep learning and quantitative structure‒activity relationship empirical screening. After generating candidate AFPs (c_AFPs) through the recombination dominant amino acids dipeptide compositions, natural language processing models utilized (QSAR) approaches based on physicochemical properties screen promising c_AFPs. Forty-nine c_AFPs screened, minimum inhibitory concentrations (MICs) against C. albicans determined be 3.9-125 µg mL-1, four leading (AFP-8, -10, -11, -13) has MICs <10 mL-1 tested pathogenic fungi, AFP-13 excellent therapeutic efficacy in animal model.

Язык: Английский

Процитировано

1
PADG‐Pred: Exploring Ensemble Approaches for Identifying Parkinson's Disease Associated Biomarkers Using Genomic Sequences Analysis DOI Creative Commons
Ayesha Karim, Tamim Alkhalifah, Fahad Alturise

и другие.

IET Systems Biology, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 1, 2025

ABSTRACT Parkinson's disease (PD), a degenerative disorder affecting the nervous system, manifests as unbalanced movements, stiffness, tremors, and coordination difficulties. Its cause, believed to involve genetic environmental factors, underscores critical need for prompt diagnosis intervention enhance treatment effectiveness. Despite array of available diagnostics, their reliability remains challenge. In this study, an innovative predictor PADG‐Pred is proposed identification associated biomarkers, utilising genomic profile. novel predictor, PADG‐Pred, which not only identifies biomarkers through profiling but also uniquely integrates multiple statistical feature extraction techniques with ensemble‐based classification frameworks, thereby providing more robust interpretable decision‐making process than existing tools. The processed dataset was utilised moments it further involved in extensive training model using diverse techniques, encompassing Ensemble methods; XGBoost, Random Forest, Light Gradient Boosting Machine, Bagging, ExtraTrees, Stacking. State‐of‐the‐art validation procedures are applied, assessing key metrics such specificity, accuracy, sensitivity/recall, Mathew's correlation coefficient. outcomes demonstrate outstanding performance PADG‐RF, showcasing accuracy consistently achieving ∼91% independent set, ∼94% 5‐fold, ∼96% 10‐fold cross‐validation.

Язык: Английский

Процитировано

0
Discovery of AMPs from Random Peptides via Deep Learning-Based Model and Biological Activity Validation DOI

Jun Du,

Changyan Yang,

Yabo Deng

и другие.

European Journal of Medicinal Chemistry, Год журнала: 2024, Номер 277, С. 116797 - 116797

Опубликована: Авг. 26, 2024

Язык: Английский

Процитировано

3
Umami-gcForest: Construction of a predictive model for umami peptides based on deep forest DOI

Shuaiqi Ji,

Junrui Wu,

Feiyu An

и другие.

Food Chemistry, Год журнала: 2024, Номер 464, С. 141826 - 141826

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

2
Integrated computational approaches for advancing antimicrobial peptide development DOI

Yanpeng Fang,

Yeshuo Ma, Kunqian Yu

и другие.

Trends in Pharmacological Sciences, Год журнала: 2024, Номер 45(11), С. 1046 - 1060

Опубликована: Окт. 25, 2024

Язык: Английский

Процитировано

1
Understanding Antimicrobial Peptide Synergy: Differential Binding Interactions and Their Impact on Membrane Integrity DOI
Jeseong Yoon,

Youngbeom Jo,

Seokmin Shin

и другие.

The Journal of Physical Chemistry B, Год журнала: 2024, Номер unknown

Опубликована: Сен. 30, 2024

Research on antimicrobial peptides (AMPs) has been conducted as a solution to overcome antibiotic resistance. In particular, the synergistic effect that appears when two or more AMPs are used in combination observed. To find an effective combination, it is necessary understand underlying mechanism. However, consistent explanation for this phenomenon not yet provided due limitations experimentally determining predicting structure of heteroaggregates formed by interactions between different and interaction aggregate surface with lipid membrane surface. study, we molecular dynamics simulations heterogeneous aggregates melittin-indolicidin pexiganan-indolicidin observe their structures phase membrane. We aimed determine how surfaces these interact Due amino acid residue sequence characteristics melittin pexiganan, found bind indolicidin, they form completely structural characteristics. Accordingly, which exhibits relatively unstable compared aqueous membranes, allow stable forming effectively inhibiting integrity membranes. also residues AMP show differential binding strengths species membrane, thereby disrupting way weakens its integrity. Through this, insight into basic principle occurs.

Язык: Английский

Процитировано

0
Progress in the Identification and Design of Novel Antimicrobial Peptides Against Pathogenic Microorganisms DOI Creative Commons
Shengwei Sun

Probiotics and Antimicrobial Proteins, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 18, 2024

Язык: Английский

Процитировано

0
Multi-Objective Optimization Accelerates the De Novo Design of Antimicrobial Peptide for Staphylococcus aureus DOI Open Access
Cheng‐Hong Yang,

Yi-Ling Chen,

Terence K.M. Cheung

и другие.

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(24), С. 13688 - 13688

Опубликована: Дек. 21, 2024

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, instability hemolysis limit medical use, making the design improvement of AMPs key research focus. Designing antimicrobial multiple desired properties using machine learning is still challenging, especially limited data. This study utilized multi-objective optimization method, non-dominated sorting genetic algorithm II (NSGA-II), enhance physicochemical peptide sequences identify those improved activity. Combining NSGA-II neural networks, approach efficiently identified AMP candidates accurately predicted antibacterial method significantly advances by optimizing factors like hydrophobicity, index, aliphatic index improve stability. It offers more efficient way address limitations AMPs, paving for development safer effective treatments.

Язык: Английский

Процитировано

0