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Computational Predictions of Amphiphile Aggregation for Early Compartmentalization

Cleaves, H. James und Gillams, Richard J. und Meringer, Markus (2018) Computational Predictions of Amphiphile Aggregation for Early Compartmentalization. 4D Workshop: Deep-time Data Driven Discovery and the Evolution of Earth, 2018-06-04 - 2018-06-06, Washington, DC.

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Offizielle URL: https://docs.wixstatic.com/ugd/0de8cd_7c7baa755c654af8b86de0f37e17ebd3.pdf

Kurzfassung

Extant biology uses a vast array of lipids to perform a range of tasks, and compartmentalization is critical for Life's existence by providing, a separation of chemical environments, enhanced local concentration of molecules, interfaces with reduced dimensionality, and individuality, leading to competition and evolution. We wished to explore and predict which kind of molecules are able to aggregate to form compartments that can host and/or encourage complex and perhaps even simple life-like chemistry that can be assayed easily in vitro. There may be a very large numberof such molecule types, and the use of high- resolution models is computationally prohibitive. We thus set out to develop an efficient way to predict aggregation and screen large in silico-generated compound libraries. There are a range of methods available for producing or accessing libraries of molecules. Through the recent explosion of lipidomics, there are a number of tools developed for mass spectrometry that include large compound libraries (e.g. LipidBlast, LipidHome, etc.). These give access to biologically relevant lipids, but do not facilitate the identification of novel molecules. We have identified computationally cheap methods for the generation of exhaustive lipid libraries and the evaluation of their propensity to self-assemble into either micelles or vesicles. Depending on user-defined parameters such libraries can easily contain well past trillions of molecules. We used MolGen (http://www.molgen.de/) for exhaustive generation of sub-libraries of lipid tails and heads. MolGen allows for disallowed molecular motifs and ranges of molecule parameters to be defined for the output. Once generated, solubility properties are assessed using QSPR models, and geometric properties computed. These are then combinatorially reacted using ChemAxon's Reactor software (https://chemaxon.com/) to give a final library. We finally evaluate them using chemoinformatics approaches to identify molecules that possess properties commensurate with an ability to form micelles, and more discriminatively, vesicles.

elib-URL des Eintrags:https://elib.dlr.de/129049/
Dokumentart:Konferenzbeitrag (Poster)
Titel:Computational Predictions of Amphiphile Aggregation for Early Compartmentalization
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Cleaves, H. JamesEarth-Life Science Institute, Tokyo Institute of TechnologyNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Gillams, Richard J.Earth-Life Science Institute, Tokyo Institute of TechnologyNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Meringer, MarkusMarkus.Meringer (at) dlr.dehttps://orcid.org/0000-0001-8526-2429NICHT SPEZIFIZIERT
Datum:Juni 2018
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:lipids, compound libraries, chemoinformatics, micelles, vesicles, astrobiology
Veranstaltungstitel:4D Workshop: Deep-time Data Driven Discovery and the Evolution of Earth
Veranstaltungsort:Washington, DC
Veranstaltungsart:Workshop
Veranstaltungsbeginn:4 Juni 2018
Veranstaltungsende:6 Juni 2018
Veranstalter :Carnegie Institution for Science
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erforschung des Weltraums
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EW - Erforschung des Weltraums
DLR - Teilgebiet (Projekt, Vorhaben):R - Explorationsstudien (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Atmosphärenprozessoren
Hinterlegt von: Meringer, Dr.rer.nat. Markus
Hinterlegt am:26 Nov 2019 09:37
Letzte Änderung:24 Apr 2024 20:32

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