Stereoselective Tripeptide Organocatalysis – Highly Reactive Electrophiles and Machine Learning
EMBARGOED UNTIL 2026-02-21
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2022
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Doctoral Thesis
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EMBARGOED UNTIL 2026-02-21
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Abstract
Tripeptides of the Pro-Pro-Xaa type are highly reactive and stereoselective organocatalysts for conjugate addition reactions. This thesis investigated the use of tripeptides to overcome catalyst deactivation, caused by highly reactive nitroolefins (Chapters 3 and 4), and challenged chemoinformatic workflows, to predict and optimize stereoselective tripeptide catalysts (Chapters 5 and 6).
In the first part of the thesis, we investigated H-DPro-Pro-Glu-NH2 and related peptides in the conjugate addition reactions of aldehydes with fluorinated nitroolefins and nitroacrylates. While these electrophiles typically deactivate secondary amine catalysts by N-alkylation, mechanistic studies revealed that this deactivation is reversible for Pro-Pro-Glu type peptides, due to the presence of a well-positioned proton donor in the catalyst. Detailed kinetic analysis showed that the N-alkylation proceeds slow compared to the enamine-formation, and that the formation of the enamine-intermediate is the rate-limiting step. We identified H-DPro-αMePro-Glu-NH2 as highly reactive and stereoselective catalyst for the conjugate addition reaction of aldehydes with fluorinated nitroolefins and obtained a broad substrate scope and chiral downstream derivatives. These reactions were carried out with only 0.5 mol% catalyst loading and provided the products in generally high yields and stereoselectivities. Furthermore, we identified H-(4S)Mep-DPro-DGlu-NH2 as even more stereoselective catalyst than H-DPro-Pro-Glu-NH2 for the reaction of aldehydes with nitroacrylates and provided the first comprehensive substrate scope for this reaction, with catalyst loadings of as little as 0.05 mol%. These low catalyst loadings for reactions with highly electron-deficient nitroolefins are remarkable and highlight the excellent reactivity of Pro-Pro-Glu type peptides, and the value of thorough mechanistic and kinetic investigations.
In the second part of this thesis, we challenged state-of-the-art machine learning methods to predict and optimize conformationally flexible tripeptide organocatalysts. First, we showed that machine learning algorithms can be trained to predict the same highly selective tripeptides of the Pro-Pro-Glu type that were previously developed by rational design. Next, we constructed a in silico library with >30000 tripeptide catalysts and synthesized a representative subset of 161 peptides. We tested these peptides on a dienamine-mediated model reactions and obtained a broad experimental data set. This first round of experiments also revealed a stereoselective Pro-Pro-Glu type catalyst that, however, could only be marginally optimized over four consecutive rounds of supervised machine learning. Thus, this work highlights the current limitations of machine learning methods and emphasizes the need for more sophisticated chemoinformatic workflows.
We also addressed the question whether and how seven electronegative Cγ-substituents at the N-terminal proline of H-Pro-DPro-DGlu-NH2 influence the catalyst reactivity and stereoselectivity. First, proline methyl ester model compounds were computationally modelled by the two independent programs MacroModel and CREST. Comparison of the predicted conformational and electronic properties showed a high linear correlation between both programs and experimental validation with dihedral angles, determined by NMR spectroscopy for hydroxyproline methyl esters in solution, were in good agreement with the computational data. In a second step, we synthesized the Cγ-substituted tripeptides and tested them as catalysts in two model reactions. While we observed a strong influence of the Cγ-substituents on the catalyst performance, we could not see a clear relationship between the computationally predicted properties and the reaction outcome. Thus, this research emphasizes that isolated properties predicted by molecular modeling do not suffice to explain the complex influence of Cγ-substituents on tripeptide catalysis.
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Examiner : Wennemers, Helma
Examiner : Morandi, Bill
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ETH Zurich
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Organocatalysis; Peptide catalysis; Enantioselective Catalysis; ORGANIC CHEMISTRY; MACHINE LEARNING (ARTIFICIAL INTELLIGENCE)
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03940 - Wennemers, Helma / Wennemers, Helma