Antimicrobial peptides can mitigate the threat of antimicrobial resistance to global health. This demand has driven us to seek new computational methods to generate more effective antimicrobial peptides. CD ComputaBio offers specialized computational services for antimicrobial peptide design, breaking through the limitations of traditional experiments to support the development of novel and efficient antimicrobial peptides.
With the increasing demand for novel antibiotics, antimicrobial peptides (AMPs) as alternative antibacterial therapies have gained increasing attention in recent years. Computational methods, as a complement to experimental high-throughput screening, have become crucial in the discovery of hit and lead compounds in drug research. Technologies such as machine learning and deep learning hold great potential in predicting antimicrobial peptide activity, sequence optimization, and design, and are expected to accelerate the development of novel antimicrobial peptides, providing new strategies to address the global issue of antibiotic resistance.
Fig. 1 Design of cationic antimicrobial peptides. (Aliyu A, et al., 2022)
| Tools | Description | References |
| APD3 | Developed for the classification, prediction, and design of AMPs, based on the parameter space defined by all available natural peptides in the database. | Wang et al. (2016) |
| CAMPR3 | A database of sequences, structures, and family-specific signatures of prokaryotic and eukaryotic AMPs, where users can avail the sequence optimization algorithm for rational design of AMPs. | Waghu et al. (2016) |
| Deep-AmPEP30 | A promising prediction tool to identify short-length AMPs from genomic sequences for drug discovery. | Yan et al. (2020) |
Computational strategies can accelerate drug design by interpreting and guiding experiments. CD ComputaBio provides comprehensive computational services for antimicrobial peptide design, utilizing molecular dynamics simulations and artificial intelligence technologies to predict peptide efficacy, optimize sequences, and identify promising candidates.
Identifying known antimicrobial peptide ligands for targets, exploring the ligands' physicochemical properties, and establishing structure-activity relationships (SAR) with antimicrobial activity provide key guidance for antimicrobial peptide design.
Analyze the three-dimensional structural information of macromolecular targets, such as proteins or RNA, to identify key sites and interactions essential for their biological activity. Then, design antimicrobial peptide drugs that specifically interfere with the basic functions of the targets.
Instead of relying on natural peptide sequences, novel peptide sequences with desired properties are designed de novo using computational methods. For example, antimicrobial peptides with amphipathic α-helical structures can be designed by rationally arranging hydrophobic and hydrophilic amino acid residues.
If you have requirements in antimicrobial peptide design, CD ComputaBio is your ideal partner. With advanced computational technologies and a professional scientific team, we provide cutting-edge antimicrobial peptide design services. For more details or professional advice, please feel free to contact us.
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CD ComputaBio offers computation-driven peptide design services to research institutions, pharmaceutical, and biotechnology companies.
