Dem IPB wird erneut ein beispielhaftes Handeln im Sinne einer chancengleichheitsorientierten Personal- und Organisationspolitik bescheinigt. Das Institut erhält zum 6. Mal in Folge das TOTAL E-QUALITY…
Die Plant Science Student Conference (PSSC) wird seit 20 Jahren im jährlichen Wechsel von Studierenden der beiden Leibniz-Institute IPK und IPB organisiert. Im Interview erläutern Christina Wäsch…
Müllers, Y.; Sadr, A. S.; Schenderlein, M.; Pallab, N.; D. Davari, M.; Glebe, U.; Reifarth, M.;Acrylate‐derived RAFT polymers for enzyme hyperactivation – boosting the α‐chymotrypsin enzyme activity using tailor‐made poly(2‐carboxyethyl)acrylate (PCEA)ChemCatChem16e202301685(2024)DOI: 10.1002/cctc.202301685
We study the hyperactivation of α‐chymotrypsin (α‐ChT) using the acrylate polymer poly(2‐carboxyethyl) acrylate (PCEA) in comparison to the commonly used poly(acrylic acid) (PAA). The polymers are added during the enzymatic cleavage reaction of the substrate N‐glutaryl‐L‐phenylalanine p‐nitroanilide (GPNA). Enzyme activity assays reveal a pronounced enzyme hyperactivation capacity of PCEA, which reaches up to 950% activity enhancement, and is significantly superior to PAA (revealing an activity enhancement of approx. 450%). In a combined experimental and computational study, we investigate α‐ChT/polymer interactions to elucidate the hyperactivation mechanism of the enzyme. Isothermal titration calorimetry reveals a pronounced complexation between the polymer and the enzyme. Docking simulations reveal that binding of polymers significantly improves the binding affinity of GPNA to α‐ChT. Notably, a higher binding affinity is found for the α‐ChT/PCEA compared to the α‐ChT/PAA complex. Further molecular dynamics (MD) simulations reveal changes in the size of the active site in the enzyme/polymer complexes, with PCEA inducing a more pronounced alteration compared to PAA, facilitating an easier access for the substrate to the active site of α‐ChT.
Publikation
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.