The Plant Science Student Conference (PSSC) has been organised by students from the two Leibniz institutes, IPK and IPB, every year for the last 20 years. In this interview, Christina Wäsch (IPK) and Carolin Apel (IPB)…
Over 600 guests came to the IPB on July 4 for the Long Night of Sciences to learn lots of new things and put their knowledge to the test at our science quiz course. This year, our program was aimed equally at children and…
Our 10th Leibniz Plant Biochemistry Symposium on May 7 and 8 was a great success. This year's theme was new methods and research approaches in natural product chemistry. The excellent presentations on active substances and…
Struwe, H.; Droste, J.; Dhar, D.; Davari, M. D.; Kirschning, A.;Chemoenzymatic synthesis of a new germacrene derivative named germacrene FChemBioChem25e202300599(2024)DOI: 10.1002/cbic.202300599
The new farnesyl pyrophosphate (FPP) derivative with a shifted olefinic double bond from C6‐C7 to C7‐C8 is accepted and converted by the sesquiterpene cyclases protoilludene synthase (Omp7) as well as viridiflorene synthase (Tps32). In both cases, a so far unknown germacrene derivative was found to be formed, which we name “germacrene F”. Both cases are examples in which a modification around the central olefinic double bond in FPP leads to a change in the mode of initial cyclization (from 1→11 to 1→10). For Omp7 a rationale for this behaviour was found by carrying out molecular docking studies. Temperature‐dependent NMR experiments, accompanied by NOE studies, show that germacrene F adopts a preferred mirror‐symmetric conformation with both methyl groups oriented in the same directions in the cyclodecane ring.
Publications
Goles, M.; Daza, A.; Cabas-Mora, G.; Sarmiento-Varón, L.; Sepúlveda-Yañez, J.; Anvari-Kazemabad, H.; Davari, M. D.; Uribe-Paredes, R.; Olivera-Nappa, A.; Navarrete, M. A.; Medina-Ortiz, D.;Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptidesBriefings in Bioinformatics25bbae275(2024)DOI: 10.1093/bib/bbae275
With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.