Ene-reductases (ERs) are enzymes that catalyze the reduction of activated C=C double bonds in substrates bearing electron-withdrawing groups. Their catalytic efficiency and stereoselectivity make them attractive tools for synthetic applications in industrial biocatalysis, particularly for the synthesis of enantiopure compounds, such as chiral molecules. This project combines experimental and computational approaches to develop and validate a protocol for predicting the enantiomeric excess (e.e.) of ERs, using citral as model substrate to obtain (R)- or (S)-citronellal. The validated protocol was then applied to identify new stereoselective enzymes using an enzyme mining strategy. After employing molecular docking and molecular dynamic (MD) simulations, a protocol was designed integrating near attack conformations (NACs), a geometric criteria method, with an energy-weighting function to generate the e.e. prediction. These predictions were then compared with benchmark experimental values obtained from activity screenings. The protocol was able to reproduce the experimental outcomes with good accuracy (R² = 0.89), demonstrating its potential as a tool to support enzyme discovery and engineering.
Le ene-reduttasi (ERs) sono enzimi in grado di catalizzare la riduzione di doppi legami C=C attivati in substrati contenenti gruppi elettron-attrattori. La loro efficienza catalitica e stereoselettività le rendono strumenti molto promettenti per applicazioni di sintesi organica nella biocatalisi industriale, in particolare per la sintesi di composti enantiopuri, come le molecole chirali. Questo progetto combina approcci sperimentali e computazionali per sviluppare e validare un protocollo in grado di predire l’eccesso enantiomerico (e.e.) delle reazioni catalizzate da ERs, utilizzando il citrale come substrato modello per ottenere (R)- o (S)-citronellale. Il protocollo validato è stato successivamente applicato per identificare nuovi enzimi stereoselettivi attraverso una strategia di enzyme mining. Dopo aver utilizzato tecniche quali molecular docking e simulazioni di dinamica molecolare (MD), è stato progettato un protocollo che integra l’analisi delle near attack conformations (NACs), un metodo basato su criteri geometrici, con una funzione di ponderazione energetica per generare la predizione dell’e.e. Queste predizioni sono state poi confrontate con i valori sperimentali di riferimento ottenuti dagli activity screenings. Il protocollo è stato in grado di riprodurre i risultati sperimentali con buona accuratezza (R² = 0.89), dimostrando il suo potenziale come strumento di supporto alla scoperta e all’ingegnerizzazione di enzimi.
Computational screening and design of highly stereoselective enzymes for enantiopure citronellal synthesis
GUERINI ROCCO, SABRINA
2024/2025
Abstract
Ene-reductases (ERs) are enzymes that catalyze the reduction of activated C=C double bonds in substrates bearing electron-withdrawing groups. Their catalytic efficiency and stereoselectivity make them attractive tools for synthetic applications in industrial biocatalysis, particularly for the synthesis of enantiopure compounds, such as chiral molecules. This project combines experimental and computational approaches to develop and validate a protocol for predicting the enantiomeric excess (e.e.) of ERs, using citral as model substrate to obtain (R)- or (S)-citronellal. The validated protocol was then applied to identify new stereoselective enzymes using an enzyme mining strategy. After employing molecular docking and molecular dynamic (MD) simulations, a protocol was designed integrating near attack conformations (NACs), a geometric criteria method, with an energy-weighting function to generate the e.e. prediction. These predictions were then compared with benchmark experimental values obtained from activity screenings. The protocol was able to reproduce the experimental outcomes with good accuracy (R² = 0.89), demonstrating its potential as a tool to support enzyme discovery and engineering.| File | Dimensione | Formato | |
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2025_10_Guerini Rocco_01.pdf
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2025_10_Guerini Rocco_Executive Summary_02.pdf
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Descrizione: executive summary
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https://hdl.handle.net/10589/243658