Formazione e ricerca
Attività Formative del Corso di Dottorato
This page shows the courses and classes of the PhD programme for the academic year 2023/2024. Additional courses and classes will be added during the year. Please check for updates regularly!
How to measure photosynthetic traits in plants
Crediti: 1
Lingua di erogazione: English
Docente: Matteo Ballottari
Perspectives for crop protection under the European green deal
Crediti: 1
Lingua di erogazione: English
Docente: Annalisa Polverari
Food science
Crediti: 1
Lingua di erogazione: Italiano
Docente: Gianni Zoccatelli
Consideration of insects as a source of dietary protein for human consumption
Crediti: 0.5
Lingua di erogazione: English
Docente: Nicola Mori
Introduction to Mass Spectrometry and Proteomics
Crediti: 1
Lingua di erogazione: English
Docente: Jessica Brandi
Peptide signaling in plant development and stress response
Crediti: 1
Lingua di erogazione: English
Docente: Tiziana Pandolfini
Seminars on proteomics analysis
Crediti: 1
Lingua di erogazione: English
Docente: Alberto Vezzelli & Elena Vicentini, Daniela Cecconi, Chiara Piubelli
Protein Engineering for Enzyme Optimisation
Crediti: 1
Lingua di erogazione: English
Docente: Salvatore Fusco
Synchrotron radiation: basics and applications in environmental samples
Crediti: 1
Lingua di erogazione: Inglese
Docente: Prof.ssa Beatrice Giannetta
Fluorescence and confocal microscopy for plant biologists
Crediti: 1
Lingua di erogazione: English
Docente: Mauro Commisso
Plant Sciences
Crediti: 1
Lingua di erogazione: Inglese
Docente: Sara Zenoni, Anita Zamboni
Protein Engineering for Enzyme Optimisation (2023/2024)
Docente
Referente
Crediti
1
Lingua di erogazione
English
Frequenza alle lezioni
Scelta Libera
Sede
VERONA
Lezioni Programmate
| Quando | Aula | Docente | Argomenti |
|---|---|---|---|
|
giovedì 11 luglio 2024 11:00 - 13:00 Durata: 02:00 |
Ca' Vignal - Piramide - Verde [2 - 0] | Salvatore Fusco | Protein Engineering for Enzyme Optimisation (Guest: Dr Marco Orlando; Host: Prof. Salvatore Fusco) Abstract: Natural organisms have been a source of very important biocatalysts for humans, from lysozyme and antibodies in treating infections, to lipases and pectinases for improving the detergent and food industries. For specific applications, natural enzymes “perfectly” suited for the required process simply does not exist, such as for the degradation of new generations of recalcitrant pesticides or for the hydrolysis of polyesters used in the plastic products. Therefore, several attempts to improve the catalytic activity of natural enzymes have been attempted by changing the protein sequences through various approaches, the main being rational site-directed mutagenesis, rational or irrational site-saturation mutagenesis, DNA shuffling, and directed-evolution. Moreover, some attempts have been made to combine the best single-point variants to create multi-point variants, by combining in silico strategies from molecular evolution and structural biology; anyway, this has resulted in a scarce success when the number of sites mutated is large. This is mainly due to structural interactions of mutated sites, causing their combination to act in a non-additive manner. This affects also directed evolution. Therefore, new methods, capable to overcome such structural constraints, are required for the engineering of better biocatalysts. Artificial Intelligence systems have largely solved the problem of protein structure prediction and now their acquired knowledge has been used for the design of novel structures and of protein sequences that can fold in those structures. This course is divided in two lessons: the first lesson will cover an introduction of classic protein/enzyme engineering approaches, how to install simple protein visualization app and integrate plug-ins for basics structural tasks (homology modeling), and how AI models can be run over the internet browser. The second lesson will be the application of available basic AI models over the web to de novo engineer a putative new biocatalyst. The course will make evident how newly developed browser-in-apps with trained AI models are making protein design a more democratic process and in the end each participant is expected to create a different and unique design. |
|
venerdì 12 luglio 2024 11:00 - 13:00 Durata: 02:00 |
Ca' Vignal - Piramide - Verde [2 - 0] | Salvatore Fusco | Protein Engineering for Enzyme Optimisation (Guest: Dr Marco Orlando; Host: Prof. Salvatore Fusco) Natural organisms have been a source of very important biocatalysts for humans, from lysozyme and antibodies in treating infections, to lipases and pectinases for improving the detergent and food industries. For specific applications, natural enzymes “perfectly” suited for the required process simply does not exist, such as for the degradation of new generations of recalcitrant pesticides or for the hydrolysis of polyesters used in the plastic products. Therefore, several attempts to improve the catalytic activity of natural enzymes have been attempted by changing the protein sequences through various approaches, the main being rational site-directed mutagenesis, rational or irrational site-saturation mutagenesis, DNA shuffling, and directed-evolution. Moreover, some attempts have been made to combine the best single-point variants to create multi-point variants, by combining in silico strategies from molecular evolution and structural biology; anyway, this has resulted in a scarce success when the number of sites mutated is large. This is mainly due to structural interactions of mutated sites, causing their combination to act in a non-additive manner. This affects also directed evolution. Therefore, new methods, capable to overcome such structural constraints, are required for the engineering of better biocatalysts. Artificial Intelligence systems have largely solved the problem of protein structure prediction and now their acquired knowledge has been used for the design of novel structures and of protein sequences that can fold in those structures. This course is divided in two lessons: the first lesson will cover an introduction of classic protein/enzyme engineering approaches, how to install simple protein visualization app and integrate plug-ins for basics structural tasks (homology modeling), and how AI models can be run over the internet browser. The second lesson will be the application of available basic AI models over the web to de novo engineer a putative new biocatalyst. The course will make evident how newly developed browser-in-apps with trained AI models are making protein design a more democratic process and in the end each participant is expected to create a different and unique design. |
