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𝐖𝐡𝐚𝐭 𝐚𝐫𝐞 𝐫𝐞𝐭𝐫𝐨-𝐢𝐧𝐯𝐞𝐫𝐬𝐨 𝐩𝐞𝐩𝐭𝐢𝐝𝐞𝐬 𝐚𝐧𝐝 𝐭𝐡𝐞𝐢𝐫 𝐫𝐨𝐥𝐞 𝐢𝐧 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲? Peptide drugs, though often potent in their biological activities, often face hurdles such as protease degradation that reduces their half life and bioavailability. However, retro-inverso #peptides, a kind of non canonical peptides recently explored in a new article on #drugdiscovery, are a promising solution for drug developers. Retro-inverso peptides are a type of peptidomimetic using D-amino acids (retro isomers) and inverting the primary sequence of the original peptide (inverted). This dual modification creates a stable version of the peptide that is not easily recognized and broken down by biological systems. As retro-inverso peptides are more resistant to proteolytic degradation they can remain active in the body for longer periods increasing their bioavailabiity, while their unique structure can enhance binding affinity and specificity to their targets. This makes them highly interesting in drug discovery. Yet their implementation can be intricate and time-consuming. Fortunately, artificial intelligence can facilitate and expedite the process, revolutionizing peptide development. AI algorithms can analyze vast datasets of peptide sequences and structure-function relationships to design the most effective peptide sequences for specific targets, with enhanced peptide durability and absorption in the body. For example, by leveraging machine learning techniques, researchers can design shorter versions of retro-inverso peptides with optimized biological properties. AI-enabled virtual screening platforms can also expedite the selection of retro-inverso peptidomimetics for experimental validation. By predicting binding affinities and interactions with target receptors, AI algorithms narrow down the pool of candidates, streamlining the selection process and reducing the need for extensive experimental testing. Last but not least, AI algorithms can optimize experimental design and data analysis for phage binding assays, accelerating the identification of retro-inverso peptidomimetics that effectively compete with native peptides for receptor binding. Machine learning models can analyze assay results in real-time, guiding researchers towards promising candidates for further characterization. Integrating AI into the retroinversion pipeline, it is clear, will revolutionize peptidomimetic development and provide unprecedented efficiency and precision, and scalability. Read the full article on peptidomimetic development here >> https://lnkd.in/d9ykxYj8 #AI

Understanding the Structural Requirements of Peptide–Protein Interaction and Applications for Peptidomimetic Development

Understanding the Structural Requirements of Peptide–Protein Interaction and Applications for Peptidomimetic Development

link.springer.com

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