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Luo Z, Geng A, Wei L, Zou Q, Cui F, Zhang Z. CPL-Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2412926. [PMID: 40231709 PMCID: PMC12120732 DOI: 10.1002/advs.202412926] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2024] [Revised: 03/13/2025] [Indexed: 04/16/2025]
Abstract
Peptides are recognized as next-generation therapeutic drugs due to their unique properties and are essential for treating human diseases. In recent years, a number of deep generation models for generating peptides have been proposed and have shown great potential. However, these models cannot well control the length of the generated sequence, while the sequence length has a very important impact on the physical and chemical properties and therapeutic effects of peptides. Here, a diffusion model is introduced, capable of controlling the length of generated functional peptide sequences, named CPL-Diff. CPL-Diff can control the length of generated polypeptide sequences using only attention masking. Additionally, CPL-Diff can generate single-functional polypeptide sequences based on given conditional information. Experiments demonstrate that the peptides generated by CPL-Diff exhibit lower perplexity and similarity compared to those produced by the current state-of-the-art models, and further exhibit relevant physicochemical properties similar to real sequences. The interpretability analysis is also performed on CPL-Diff to understand how it controls the length of generated sequences and the decision-making process involved in generating polypeptide sequences, with the aim of providing important theoretical guidance for polypeptide design. The code for CPL-Diff is available at https://github.com/luozhenjie1997/CPL-Diff.
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Affiliation(s)
- Zhenjie Luo
- College of Computer Science and TechnologyHainan UniversityNo. 58, Renmin AvenueHaikou570228China
| | - Aoyun Geng
- College of Computer Science and TechnologyHainan UniversityNo. 58, Renmin AvenueHaikou570228China
| | - Leyi Wei
- Centre for Artificial Intelligence driven Drug DiscoveryFaculty of Applied ScienceMacao Polytechnic UniversityMacao SAR999078China
- School of InformaticsXiamen UniversityXiamen361005China
| | - Quan Zou
- Institute of Fundamental and Frontier SciencesUniversity of Electronic Science and Technology of ChinaChengdu610054China
- Yangtze Delta Region Institute (Quzhou)University of Electronic Science and Technology of ChinaQuzhou324000China
| | - Feifei Cui
- College of Computer Science and TechnologyHainan UniversityNo. 58, Renmin AvenueHaikou570228China
| | - Zilong Zhang
- College of Computer Science and TechnologyHainan UniversityNo. 58, Renmin AvenueHaikou570228China
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Yin K, Li R, Zhang S, Sun Y, Huang L, Jiang M, Xu D, Xu W. Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2025; 12:e2412488. [PMID: 39921483 PMCID: PMC11967820 DOI: 10.1002/advs.202412488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Revised: 01/20/2025] [Indexed: 02/10/2025]
Abstract
Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to 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 of dominant amino acids and dipeptide compositions, natural language processing models are utilized and quantitative structure‒activity relationship (QSAR) approaches based on physicochemical properties to screen for promising c_AFPs. Forty-nine promising c_AFPs are screened, and their minimum inhibitory concentrations (MICs) against C. albicans are determined to be 3.9-125 µg mL-1, of which four leading c_AFPs (AFP-8, -10, -11, and -13) has MICs of <10 µg mL-1 against the four tested pathogenic fungi, and AFP-13 has excellent therapeutic efficacy in the animal model.
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Affiliation(s)
- Kedong Yin
- Zhengzhou Key Laboratory of Functional Molecules for Biomedical ResearchHenan University of TechnologyZhengzhouHenan450001P. R. China
- College of Information Science and EngineeringHenan University of TechnologyZhengzhouHenan450001P. R. China
| | - Ruifang Li
- Zhengzhou Key Laboratory of Functional Molecules for Biomedical ResearchHenan University of TechnologyZhengzhouHenan450001P. R. China
- School of Biological EngineeringHenan University of TechnologyZhengzhouHenan450001P. R. China
| | - Shaojie Zhang
- Zhengzhou Key Laboratory of Functional Molecules for Biomedical ResearchHenan University of TechnologyZhengzhouHenan450001P. R. China
- School of Biological EngineeringHenan University of TechnologyZhengzhouHenan450001P. R. China
| | - Yiqing Sun
- Zhengzhou Key Laboratory of Functional Molecules for Biomedical ResearchHenan University of TechnologyZhengzhouHenan450001P. R. China
- School of Biological EngineeringHenan University of TechnologyZhengzhouHenan450001P. R. China
| | - Liang Huang
- Zhengzhou Key Laboratory of Functional Molecules for Biomedical ResearchHenan University of TechnologyZhengzhouHenan450001P. R. China
- School of Biological EngineeringHenan University of TechnologyZhengzhouHenan450001P. R. China
| | - Mengwan Jiang
- School of Artificial Intelligence and Big DataHenan University of TechnologyZhengzhouHenan450001P. R. China
| | - Degang Xu
- College of Information Science and EngineeringHenan University of TechnologyZhengzhouHenan450001P. R. China
| | - Wen Xu
- Zhengzhou Key Laboratory of Functional Molecules for Biomedical ResearchHenan University of TechnologyZhengzhouHenan450001P. R. China
- Law CollegeHenan University of TechnologyZhengzhouHenan450001P. R. China
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Zhou X, Liu G, Cao S, Lv J. Deep Learning for Antimicrobial Peptides: Computational Models and Databases. J Chem Inf Model 2025; 65:1708-1717. [PMID: 39927895 DOI: 10.1021/acs.jcim.5c00006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2025]
Abstract
Antimicrobial peptides are a promising strategy to combat antimicrobial resistance. However, the experimental discovery of antimicrobial peptides is both time-consuming and laborious. In recent years, the development of computational technologies (especially deep learning) has provided new opportunities for antimicrobial peptide prediction. Various computational models have been proposed to predict antimicrobial peptide. In this review, we focus on deep learning models for antimicrobial peptide prediction. We first collected and summarized available data resources for antimicrobial peptides. Subsequently, we summarized existing deep learning models for antimicrobial peptides and discussed their limitations and challenges. This study aims to help computational biologists design better deep learning models for antimicrobial peptide prediction.
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Affiliation(s)
- Xiangrun Zhou
- College of Computer Science and Technology, Jilin University, Changchun, 130000, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130000, China
| | - Guixia Liu
- College of Computer Science and Technology, Jilin University, Changchun, 130000, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130000, China
| | - Shuyuan Cao
- College of Computer Science and Technology, Jilin University, Changchun, 130000, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130000, China
| | - Ji Lv
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China
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Guan C, Fernandes FC, Franco OL, de la Fuente-Nunez C. Leveraging large language models for peptide antibiotic design. CELL REPORTS. PHYSICAL SCIENCE 2025; 6:102359. [PMID: 39949833 PMCID: PMC11823563 DOI: 10.1016/j.xcrp.2024.102359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/16/2025]
Abstract
Large language models (LLMs) have significantly impacted various domains of our society, including recent applications in complex fields such as biology and chemistry. These models, built on sophisticated neural network architectures and trained on extensive datasets, are powerful tools for designing, optimizing, and generating molecules. This review explores the role of LLMs in discovering and designing antibiotics, focusing on peptide molecules. We highlight advancements in drug design and outline the challenges of applying LLMs in these areas.
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Affiliation(s)
- Changge Guan
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
- These authors contributed equally
| | - Fabiano C. Fernandes
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- Departamento de Ciência da Computação, Instituto Federal de Brasília, Campus Taguatinga, Brasília, Brazil
- These authors contributed equally
| | - Octavio L. Franco
- Centro de Análises Proteômicas e Bioquímicas, Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, Brazil
- S-Inova Biotech, Programa de Pós-Graduação em Biotecnologia, Universidade Católica Dom Bosco, Campo Grande, Brazil
| | - Cesar de la Fuente-Nunez
- Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA
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