Emerging Computational Approaches for Antimicrobial Peptide Discovery

Agüero-Chapin, Guillermin; Cañizares, Deborah Galpert; Domínguez-Pérez, Dany; Marrero-Ponce, Yovani; Pérez-Machado, Gisselle; Teijeira, Marta; Antunes, Agostinho

doi:10.3390/antibiotics11070936

Cited by 16 publications

(7 citation statements)

References 166 publications

(302 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experiments conducted in vitro and in vivo can imitate the actions of molecules in the human body, but they are nonetheless time‐ and money‐consuming. AI approaches have been successfully used to forecast pharmacological bioactivities such as anticancer, antiviral, and antibacterial activities due to their cost‐effectiveness and time economy (Aguero‐Chapin et al, 2022). To forecast antimicrobial activity, Stokes et al (2020) suggested a directed message‐passing neural network (Huang et al, 2021).…”

Section: Application Of Ai In Pharmaceutical Analysismentioning

confidence: 99%

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu,

Obulose

2024

Drug Development Research

View full text Add to dashboard Cite

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well‐known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI‐based models for drug–target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

show abstract

Section: Application Of Ai In Pharmaceutical Analysismentioning

confidence: 99%

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu,

Obulose

2024

Drug Development Research

View full text Add to dashboard Cite

show abstract

“…Over the past two decades, using artificial intelligence (AI) and computational models have significantly advanced the discovery and development of bioactive peptides, revolutionizing drug development and molecular biology . Furthermore, while the initial goal was to develop antimicrobial peptides (AMP) as drugs, applications in food preservation may soon be investigated, considering their ability to inhibit pathogenic microorganisms’ growth and extend perishable products’ shelf life.…”

Section: Recent Advancements In Artificial Intelligence Technology Fo...mentioning

confidence: 99%

Bioactive Peptides and Protein Hydrolysates Used in Meat and Meat Products’ Preservation─A Review

González-Osuna,

Bernal-Mercado,

Wong-Corral

et al. 2024

ACS Food Sci. Technol.

View full text Add to dashboard Cite

Meat is highly perishable due to its composition and susceptibility to microbial growth and enzymatic degradation. Preservation methods are necessary to extend its shelf life, allowing for longer storage, transportation, and distribution without compromising quality or safety. With increasing consumer demand for natural, minimally processed foods and concerns about synthetic additives, there is a growing need for preservation alternatives, including packaging, which align with these preferences. Natural preservation methods, such as those utilizing peptides and protein hydrolysates, offer a solution that meets consumer demands and industry requirements. This review highlights the significance of isolating peptides and protein hydrolysates with antioxidant and antimicrobial properties for application alone or combined with edible films in meat and meat products. Studies have demonstrated that these bioactive compounds effectively inhibit lipid oxidation and microbial proliferation as viable alternatives to synthetic preservatives without affecting meat's quality and sensory characteristics. The future application of these natural preservation alternatives holds considerable promise in the food industry. However, overcoming hurdles such as scaling production and ensuring stability are crucial. Interdisciplinary collaborations, technological advancements, and continued research efforts are essential. In particular, artificial intelligence may be the solution to problems commonly encountered when obtaining bioactive peptides and protein hydrolysates, enhancing the efficiency of identifying novel bioactive compounds. Nonetheless, adopting peptides and protein hydrolysates as natural preservatives represents a significant step toward improving food quality and safety while meeting consumer preferences for more natural products.

show abstract

“…Moreover, relatively small subsets of hemolytic peptides can be extracted for further studies. These subsets (called "scaffolds") have the advantage of representing the whole chemical space of hemolytic peptides but just using a fraction of the nodes of the complex network of hemolytic peptides [24].…”

Section: Introductionmentioning

confidence: 99%

Peptide Hemolytic Activity Analysis using Visual Data Mining of Similarity-based Complex Networks

Castillo-Mendieta¹,

Agüero-Chapin²,

Vispo³

et al. 2023

Preprint

View full text Add to dashboard Cite

Peptides are promising drug development frameworks thanks to their high target selectivity, tolerability and relatively low production cost. However, despite the fact that several thousand potentially therapeutic peptides reported, only sixty have arrived at the market. This concerning low proportion is partially explained by undesired properties such as peptide-induced hemolytic activity. Hence, we aim to get a better insight into the chemical space of hemolytic peptides using a novel approach based on network science and interactive data mining as an alternative to design more effective peptide drugs with low hemolytic activity. Metadata networks (METNs) were used to characterize and find general patterns associated to hemolytic peptides, whereas Half-Space Proximal Networks (HSPNs), created using five different two-way dissimilarity measures, represented the hemolytic peptide space. Then, using the best candidate HSPNs, we extracted various scaffolds that capture information of almost all the chemical space but avoiding peptide overrepresentation. Such scaffolds can have many applications, such as training accurate ML-based prediction models, constructing one-class multi-query similarity searching models and characterizing the diversity of hemolytic peptides using a manageable set of peptides. Finally, by means of an alignment-free approach, we reported 47 putative hemolytic motifs, which might provide hints about the mechanisms of hemolysis and can also be used as toxic signatures when developing novel peptide-based drugs.

show abstract

Emerging Computational Approaches for Antimicrobial Peptide Discovery

Cited by 16 publications

References 166 publications

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Bioactive Peptides and Protein Hydrolysates Used in Meat and Meat Products’ Preservation─A Review

Peptide Hemolytic Activity Analysis using Visual Data Mining of Similarity-based Complex Networks

Contact Info

Product

Resources

About