Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

Szymczak, Paulina; Możejko, Marcin; Grzegorzek, Tomasz; Jurczak, Radosław; Bauer, Marta; Neubauer, Damian; Sikora, Karol; Michalski, Michal; Sroka, Jacek; Setny, Piotr; Kamysz, Wojciech; Szczurek, Ewa

doi:10.1038/s41467-023-36994-z

Cited by 26 publications

(21 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, traditional AMP discovery using wet experiments is time-consuming and costly 15, 16 . Consequently, artificial intelligence (AI)-driven approaches such as identification 17–21 , optimization 22–24 , and generation 16, 25–28 have been proposed to accelerate AMP discovery. Among these, generative models overcome the limitations of identification and optimisation models.…”

Section: Mainmentioning

confidence: 99%

De novo multi-mechanism antimicrobial peptide design via multimodal deep learning

Wang,

Gong,

et al. 2024

Preprint

View full text Add to dashboard Cite

Artificial intelligence (AI)-driven discovery of antimicrobial peptides (AMPs) is yet to fully utilise their three-dimensional (3D) structural characteristics, microbial specie-specific antimicrobial activities and mechanisms. Here, we constructed a QLAPD database comprising the sequence, structures and antimicrobial properties of 12,914 AMPs. QLAPD underlies a multimodal, multitask, multilabel, and conditionally controlled AMP discovery (M3-CAD) pipeline, which is proposed for thede novodesign of multi-mechanism AMPs to combat multidrug-resistant organisms (MDROs). This pipeline integrates the generation, regression, and classification modules, using a innovative 3D voxel coloring method to capture the nuanced physicochemical context of amino acids, significantly enhancing structural characterizations. QL-AMP-1, discovered by M3-CAD, which possesses four antimicrobial mechanisms, exhibited low toxicity and significant activity against MDROs. The skin wound infection model demonstrates its considerable antimicrobial effects and negligible toxicity. Altogether, integrating 3D features, specie-specific antimicrobial activities and mechanisms enhanced AI-driven AMP discovery, making the M3-CAD pipeline a viable tool forde novoAMP design.

show abstract

Section: Mainmentioning

confidence: 99%

De novo multi-mechanism antimicrobial peptide design via multimodal deep learning

Wang,

Gong,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…We then compared antimicrobial and anticancer activity of generated by Prefix-Prot and several state-of-the-art methods. For the AMP generation, we employed PepLSTM [37], PepCVAE [12], and Hydramp [51]. PepLSTM designs AMPs by leveraging Long Short-Term Memory (LSTM) to recognize the grammar of amphipathicity in peptides.…”

Section: Controllable Functional Protein Generationmentioning

confidence: 99%

Controllable Protein Design by Prefix-Tuning Protein Language Models

Luo,

Liu,

et al. 2023

Preprint

View full text Add to dashboard Cite

Designing novel proteins tailored for specific purposes presents a promising approach to address various biomedical challenges, including drug discovery, vaccine design, etc.. The protein language models (ProtLMs) with control tags as a prefix or fine-tuning in a specific domain have achieved unprecedented breakthroughs in the controllable protein design. However, the vocabulary of protein sequences only contains 20 amino acid residues, which is not like natural language vocabulary to make up flexible control tags. Moreover, due to their large number of parameters, fine-tuning ProtLMs with limited data is challenging. In this study, we propose a flexible and controllable protein design method, named PrefixProt, which employs prefix-tuning to learn a virtual token for each protein property on corresponding datasets. Subsequently, the learned virtual tokens can be used to prompt pre-trained ProtLMs for generating proteins with tailored structures and functions. We trained two prefix virtual tokens on alpha-helix structure dataset and antimicrobial peptide (AMP) dataset, respectively. Our results demonstrate that prefix virtual tokens are efficient to prompt the pre-trained ProtLM by optimizing fewer trainable parameters to achieve superior results compared with fine tuning, even under low-data settings. Furthermore, these two prefix virtual tokens are combined to precisely control protein generation with both AMP function and alpha-helix structure. These results demonstrate prefix virtual tokens are flexible to be learned and integrated to control the generation of proteins. Therefore PrefixProt has advantages of both control tags and fine-tuning. In summary, PrefixProt offers a flexible and controllable protein design solution. We anticipate that PrefixProt will contribute to protein discovery and biomedical advancement.Availability and implementationThe models and associated code are available at:https://github.com/chen-bioinfo/PrefixProt

show abstract

“…These models recently enabled comprehensive mining of the human gut microbiome, 17 modern and ancient human proteomes, 18, 19 or even the entire sequence space of hexapeptides. 8 Generative models like PepVAE, 20 CLaSS, 21 and HydrAMP 22 reconstructed the sequences from latent space according to the encoder-decoder architecture of variational autoencoders (VAE), while PepGAN 23 and AMPGAN v2 24 utilized the generative adversarial network (GAN) to approximate the antimicrobial sequences by deceiving the discriminator.…”

Section: Introductionmentioning

confidence: 99%

Exploring the repository ofde novodesigned bifunctional antimicrobial peptides through deep learning

Dong,

Liu,

Liu

et al. 2024

Preprint

View full text Add to dashboard Cite

Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target bio-membranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here we establish ade novoAMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden ‘grammars’ of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discover three bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogensin vitroand in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistantA. baumannii, while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections.

show abstract

Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

Cited by 26 publications

References 83 publications

De novo multi-mechanism antimicrobial peptide design via multimodal deep learning

De novo multi-mechanism antimicrobial peptide design via multimodal deep learning

Controllable Protein Design by Prefix-Tuning Protein Language Models

Exploring the repository ofde novodesigned bifunctional antimicrobial peptides through deep learning

Contact Info

Product

Resources

About