Small Natural Cyclic Peptides from DBAASP Database.

Antimicrobial peptides (AMPs) are promising tools for combating microbial resistance. However, their therapeutic potential is hindered by two intrinsic drawbacks-low target affinity and poor in vivo stability. Macrocyclization, a process that improves the pharmacological properties and bioactivity of peptides, can address these limitations.

Evaluation of the synergistic potential and mechanisms of action for designed cationic antimicrobial peptides.

Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an approach to design cationic AMPs , followed by their experimental testing.

Georgian Medicinal Plants as Rich Natural Sources of Antioxidant Derivatives: A Review on the Current Knowledge and Future Perspectives.

The study of antioxidants is of pivotal importance in biomedicine as these molecules could be involved in biological pathways associated with disease. The identification of new antioxidants together with the acquisition of a deeper knowledge on their biology, could lead to the use of these compounds as drugs for innovative treatments. Plants are an important reservoir of phytodrugs that in many cases can be isolated with good extraction yields directly from the vegetal source and are often endowed with a low toxicity profile.

Analysis, Modeling, and Target-Specific Predictions of Linear Peptides Inhibiting Virus Entry.

Antiviral peptides (AVPs) are bioactive peptides that exhibit the inhibitory activity against viruses through a range of mechanisms. Virus entry inhibitory peptides (VEIPs) make up a specific class of AVPs that can prevent envelope viruses from entering cells. With the growing number of experimentally verified VEIPs, there is an opportunity to use machine learning to predict peptides that inhibit the virus entry.

Comparative analysis of machine learning algorithms on the microbial strain-specific AMP prediction.

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs.