Artificial intelligence-driven antimicrobial peptide discovery

Szymczak, Paulina; Szczurek, Ewa

doi:10.1016/j.sbi.2023.102733

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…Recently, humanizing computational models have been proposed to shed light on these differences [ 52 , 53 ]. This strategy can also result in the accelerated discovery of AMPs as novel antibiotics by artificial intelligence [ 54 , 55 , 56 ]. Although they are of great interest, the immunomodulatory properties of AMPs have been less considered [ 57 ].…”

Section: Discussionmentioning

confidence: 99%

Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents

Hemmati,

Saeidikia,

Seradj

et al. 2024

Pharmaceuticals

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The underdevelopment of adjuvant discovery and diversity, compared to core vaccine technology, is evident. On the other hand, antibiotic resistance is on the list of the top ten threats to global health. Immunomodulatory peptides that target a pathogen and modulate the immune system simultaneously are promising for the development of preventive and therapeutic molecules. Since investigating innate immunity in insects has led to prominent achievements in human immunology, such as toll-like receptor (TLR) discovery, we used the capacity of the immunomodulatory peptides of arthropods with concomitant antimicrobial or antitumor activity. An SVM-based machine learning classifier identified short immunomodulatory sequences encrypted in 643 antimicrobial peptides from 55 foe-to-friend arthropods. The critical features involved in efficacy and safety were calculated. Finally, 76 safe immunomodulators were identified. Then, molecular docking and simulation studies defined the target of the most optimal peptide ligands among all human cell-surface TLRs. SPalf2-453 from a crab is a cell-penetrating immunoadjuvant with antiviral properties. The peptide interacts with the TLR1/2 heterodimer. SBsib-711 from a blackfly is a TLR4/MD2 ligand used as a cancer vaccine immunoadjuvant. In addition, SBsib-711 binds CD47 and PD-L1 on tumor cells, which is applicable in cancer immunotherapy as a checkpoint inhibitor. MRh4-679 from a shrimp is a broad-spectrum or universal immunoadjuvant with a putative Th1/Th2-balanced response. We also implemented a pathway enrichment analysis to define fingerprints or immunological signatures for further in vitro and in vivo immunogenicity and reactogenicity measurements. Conclusively, combinatorial machine learning, molecular docking, and simulation studies, as well as systems biology, open a new opportunity for the discovery and development of multifunctional prophylactic and therapeutic lead peptides.

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Section: Discussionmentioning

confidence: 99%

Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents

Hemmati,

Saeidikia,

Seradj

et al. 2024

Pharmaceuticals

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“…Various AI tools and platforms can be involved in different stages of this process. Szymczak et al present an interesting and exhaustive analysis of the AI methods that could support AMP discovery and design, discussing different categories of AI methodologies and focusing on the recent achievements in AI-driven AMP discovery [35]. Since the de novo design of antimicrobial peptides is a complex task, a multidisciplinary approach involving expertise in biology, chemistry and bioinformatics is essential for success [36].…”

Section: Antimicrobial Peptides (Amps): An Overviewmentioning

confidence: 99%

Antimicrobial Peptides towards Clinical Application—A Long History to Be Concluded

Cresti,

Cappello,

Pini

2024

IJMS

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Antimicrobial peptides (AMPs) are molecules with an amphipathic structure that enables them to interact with bacterial membranes. This interaction can lead to membrane crossing and disruption with pore formation, culminating in cell death. They are produced naturally in various organisms, including humans, animals, plants and microorganisms. In higher animals, they are part of the innate immune system, where they counteract infection by bacteria, fungi, viruses and parasites. AMPs can also be designed de novo by bioinformatic approaches or selected from combinatorial libraries, and then produced by chemical or recombinant procedures. Since their discovery, AMPs have aroused interest as potential antibiotics, although few have reached the market due to stability limits or toxicity. Here, we describe the development phase and a number of clinical trials of antimicrobial peptides. We also provide an update on AMPs in the pharmaceutical industry and an overall view of their therapeutic market. Modifications to peptide structures to improve stability in vivo and bioavailability are also described.

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“…Machine learning models have emerged as a time-saving and cost-effective tool for screening large data sets to identify potential AMPs. To date, machine learning models − based on amino acid sequences have mainly been built using traditional − and deep learning (DL) − techniques, as well as using similarity networks. , However, the outstanding results of deep neural network-based approaches, such as trRosetta, AlphaFold, RoseTTAFold, ESMFold, and HelixFold-Single, in the prediction of tertiary (3D) structures of proteins from their amino acid sequences have unlocked new opportunities to build better predictive models. In this regard, non-DL based models using 3D protein descriptors − as well as Graph Neural Network-based models , (e.g., equivariant network) are promissory strategies to be developed.…”

Section: Introductionmentioning

confidence: 99%

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

Cordoves-Delgado,

García-Jacas

2024

J. Chem. Inf. Model.

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Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.

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Artificial intelligence-driven antimicrobial peptide discovery

Cited by 11 publications

References 38 publications

Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents

Immunomodulatory Peptides as Vaccine Adjuvants and Antimicrobial Agents

Antimicrobial Peptides towards Clinical Application—A Long History to Be Concluded

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

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