Antimicrobial peptides: An alternative to traditional antibiotics

European Journal of Medicinal Chemistry, Год журнала: 2023, Номер 265, С. 116072 - 116072

Опубликована: Дек. 21, 2023

Язык: Английский

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AI Methods for Antimicrobial Peptides: Progress and Challenges

Microbial Biotechnology, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

ABSTRACT Antimicrobial peptides (AMPs) are promising candidates to combat multidrug‐resistant pathogens. However, the high cost of extensive wet‐lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. approaches have recently revolutionised this field by accelerating discovery new anti‐infective activity, particularly in preclinical mouse models. Initially, classical ML dominated field, but there been shift towards deep (DL) Despite significant contributions, existing reviews not thoroughly explored potential large language models (LLMs), graph neural networks (GNNs) structure‐guided AMP design. This review aims fill that gap providing comprehensive overview latest advancements, challenges opportunities using methods, particular emphasis on LLMs, GNNs We discuss limitations current highlight most relevant topics address coming years

Язык: Английский

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Deep learning tools to accelerate antibiotic discovery

Expert Opinion on Drug Discovery, Год журнала: 2023, Номер 18(11), С. 1245 - 1257

Опубликована: Окт. 4, 2023

ABSTRACTIntroduction As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep models offer an efficient way explore high-dimensional data design compounds with desired properties, including those antibacterial activity.Areas covered This review covers key frameworks in antibiotic discovery, highlighting physicochemical features addressing dataset limitations. The approaches here described include discriminative such as convolutional neural networks, recurrent graph generative like language models, variational autoencoders, adversarial normalizing flow, diffusion models. the integration these discovery continues evolve, this aims provide insights into promising prospects challenges that lie ahead harnessing technologies development antibiotics.Expert opinion Accurate antimicrobial prediction using faces imbalanced data, limited datasets, experimental validation, target strains, structure. bioinformatics, molecular dynamics, augmentation holds potential overcome challenges, enhance model performance, utlimately accelerate discovery.KEYWORDS: Drug discoverydrug designantimicrobialsDeep-learning modelsinfectious diseases Article highlights AI ML innovative ways expedite by optimizing design.The successful use is directly impacted algorithm influences model´s ability represent diverse structures.Discriminative computational predicting activity, leveraging their specific architectures approaches.Generative applied utilizing composition molecules generate potent candidates.Deep yet face due quality availability limitations.AcknowledgmentsThe authors thank Dr. Karen Pepper editing manuscript de la Fuente Lab members insightful discussions. All figures were prepared BioRender.com. Molecules shown paper rendered PyMOL Molecular Graphics System, Version 2.5.2 Schrödinger, LLC.Declaration interestC Fuente-Nunez provides consulting services Invaio Sciences a member Scientific Advisory Boards Nowture S.L. Phare Bio. have no other relevant affiliations or financial involvement any organization entity interest conflict subject matter materials discussed apart from disclosed.Reviewer disclosuresPeer reviewers on relationships disclose.Additional informationFundingCesar Presidential Professorship at University Pennsylvania, recipient Langer Prize AIChE Foundation, acknowledges funding IADR Innovation Oral Care Award, Procter & Gamble Company, United Therapeutics, BBRF Young Investigator Grant, Nemirovsky Prize, Penn Health-Tech Accelerator Dean's Fund Perelman School Medicine National Institute General Medical Institutes Health under award number R35GM138201, Defense Threat Reduction Agency (DTRA; HDTRA11810041, HDTRA1-21-1-0014, HDTRA1-23-1-0001).

Язык: Английский

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Therapeutic Peptide Development Revolutionized: Harnessing the Power of Artificial Intelligence for Drug Discovery

Heliyon, Год журнала: 2024, Номер 10(22), С. e40265 - e40265

Опубликована: Ноя. 1, 2024

Due to the spread of antibiotic resistance, global attention is focused on its inhibition and expansion effective medicinal compounds. The novel functional properties peptides have opened up new horizons in personalized medicine. With artificial intelligence methods combined with therapeutic peptide products, pharmaceuticals biotechnology advance drug development rapidly reduce costs. Short-chain inhibit a wide range pathogens great potential for targeting diseases. To address challenges synthesis sustainability, methods, namely machine learning, must be integrated into their production. Learning can use complicated computations select active toxic compounds metabolic activity. Through this comprehensive review, we investigated method as tool finding peptide-based drugs providing more accurate analysis through introduction predictable databases selection development.

Язык: Английский

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Artificial intelligence-driven antimicrobial peptide discovery

Current Opinion in Structural Biology, Год журнала: 2023, Номер 83, С. 102733 - 102733

Опубликована: Ноя. 21, 2023

Язык: Английский

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Structure-aware machine learning strategies for antimicrobial peptide discovery

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Май 25, 2024

Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These often need protein structure awareness, as they heavily rely on sequential data. The excel at identifying sequences of a particular biological nature or activity, but frequently fail comprehend their intricate mechanism(s) action. To solve two problems once, we studied the mechanisms action structural landscape antimicrobial peptides (i) membrane-disrupting peptides, (ii) membrane-penetrating (iii) protein-binding By analyzing critical features such dipeptides physicochemical descriptors, developed with high accuracy (86-88%) in predicting these categories. However, initial (1.0 2.0) exhibited bias towards α-helical coiled structures, influencing predictions. address this bias, implemented subset selection data reduction strategies. former gave three structure-specific for likely fold into α-helices (models 1.1 2.1), coils (1.3 2.3), mixed structures (1.4 2.4). latter depleted over-represented leading structure-agnostic predictors 1.5 2.5. Additionally, research highlights sensitivity important different classes across models.

Язык: Английский

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Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies

Acta Pharmaceutica Sinica B, Год журнала: 2024, Номер 14(8), С. 3476 - 3492

Опубликована: Май 10, 2024

Owing to their limited accuracy and narrow applicability, current antimicrobial peptide (AMP) prediction models face obstacles in industrial application. To address these limitations, we developed improved an AMP model using Comparing Optimizing Multiple DEep Learning (COMDEL) algorithms, coupled with high-throughput screening method, finally reaching of 94.8% test 88% experiment verification, surpassing other state-of-the-art models. In conjunction COMDEL, employed the phage-assisted evolution method screen Sortase vivo a cell-free synthesis system vitro, ultimately increasing AMPs yields range 0.5‒2.1 g/L within hours. Moreover, by multi-omics analysis identified Lactobacillus plantarum as most promising candidate for generation among 35 edible probiotics. Following this, microdroplet sorting approach successfully screened three L. mutants, each showing twofold increase ability, underscoring substantial application values.

Язык: Английский

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Explainable artificial intelligence and machine learning: novel approaches to face infectious diseases challenges

Annals of Medicine, Год журнала: 2023, Номер 55(2)

Опубликована: Ноя. 27, 2023

Artificial intelligence (AI) and machine learning (ML) are revolutionizing human activities in various fields, with medicine infectious diseases being not exempt from their rapid exponential growth. Furthermore, the field of explainable AI ML has gained particular relevance is attracting increasing interest. Infectious have already started to benefit AI/ML models. For example, they been employed or proposed better understand complex models aimed at improving diagnosis management coronavirus disease 2019, antimicrobial resistance prediction quantum vaccine algorithms. Although some issues concerning dichotomy between explainability interpretability still require careful attention, an in-depth understanding how arrive predictions recommendations becoming increasingly essential properly face growing challenges present century.

Язык: Английский

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deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model

PeerJ, Год журнала: 2024, Номер 12, С. e17729 - e17729

Опубликована: Июль 19, 2024

Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components innate immune system, have emerged as a potent solution to AMR due their therapeutic potential. Employing computational methodologies for prompt recognition these indeed unlocks fresh perspectives, thereby potentially revolutionizing drug development. Methods In this study, we developed model named deepAMPNet. This model, which leverages graph neural networks, excels at swift identification AMPs. It employs structures predicted AlphaFold2, encodes residue-level features through bi-directional long short-term memory (Bi-LSTM) protein language and constructs adjacency matrices anchored on amino acids’ contact maps. Results comparative study with other state-of-the-art AMP predictors two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, terms commonly accepted evaluation such AUC, Mcc, sensitivity, specificity, achieved highest or highly comparable performances against predictors. Conclusion interweaves both structural sequence information AMPs, stands high-performance that propels evolution design peptide pharmaceuticals. The data code utilized can be accessed https://github.com/Iseeu233/deepAMPNet .

Язык: Английский

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AI-driven antimicrobial peptides for drug development

Methods in microbiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

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