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Institute
- Theodor-Boveri-Institut für Biowissenschaften (123) (remove)
Sonstige beteiligte Institutionen
- DNA Analytics Core Facility, Biocenter, University of Würzburg, Würzburg, Germany (1)
- Department of Animal Ecology and Tropical Biology, University of Würzburg, Würzburg, Germany (1)
- Forschungsstation Fabrikschleichach (1)
- Institut für Tierökologie und Tropenbiologie (1)
- Interdisziplinäres Zentrum für Klinische Forschung (ZIKF), Würzburg (1)
- Klinische Mikrobiologie am Universitätsklinikum Erlangen (1)
- Technische Hochschule Wildau (1)
Die Identifikation der Bindungsspezifitäten von Proteininteraktionsdomänen und damit letztlich auch die Fähigkeit potentielle Bindungspartner dieser in vivo vorherzusagen bildet ein grundlegendes Element für das Verständnis der biologischen Funktionen dieser Domänen. In dieser Arbeit wurde untersucht, inwieweit solche Vorhersagen bezüglich der SH3-Domäne – als Beispiel für eine Proteininteraktionsdomäne – mithilfe von Support-Vector-Machines (SVMs) möglich sind, wenn diesen als Informationsquelle ausschließlich die innerhalb der Aminosäuresequenz der Domäne konservierten Informationen zur Verfügung stehen. Um den SVM-basierten Klassifikator zu trainieren und zu validieren, wurde ein Satz aus 51 SH3-Domänen verwendet, die zuvor entsprechend ihrer Ligandenpräferenz in ein System aus acht verschiedenen Klassen eingeteilt worden waren. Da die innerhalb der Aminosäuresequenzen konservierten Informationen in abstrakte Zahlenwerte konvertiert werden mussten (Voraussetzung für mathematisch basierte Klassifikatoren wie SVMs), wurde jede Aminosäuresequenz durch ihren jeweiligen Fisher-Score-Vektor ausgedrückt. Die Ergebnisse erbrachten einen Klassifikationserror, welcher weit unterhalb des Zufallsniveaus lag, was darauf hindeutet, dass sich die Bindungsspezifität (Klasse) einer SH3-Domäne in der Tat von seiner Aminosäuresequenz ableiten lassen dürfte. Mithilfe klassenspezifisch emittierter, artifizieller Sequenzen, implementiert in den Trainingsprozess des Klassifikators, um etwaigen nachteiligen Auswirkungen von Overfitting zu entgegenzuwirken, sowie durch Berücksichtigung taxonomischer Informationen des Klassensystems während Training und Validierung, ließ sich der Klassifikationserror sogar noch weiter senken und lag schließlich bei lediglich 35,29% (vergleiche Zufall: 7/8 = 87.50%). Auch die Nutzung von Feature Selections zur Abmilderung Overfitting-bedingter, negativer Effekte lieferte recht vielversprechende Ergebnisse, wenngleich ihr volles Potential aufgrund von Software-Beschränkungen nicht ausgenutzt werden konnte.
Die Analyse der Positionen im Sequence-Alignment, welche für den SVM- basierten Klassifikator am relevantesten waren, zeigte, dass diese häufig mit Positionen korrelierten, von denen angenommen wird auch in vivo eine Schlüsselrolle bei der Determination der Bindungsspezifität (Klasse) zu spielen. Dies unterstreicht nicht nur die Reliabilität des präsentierten Klassifikators, es gibt auch Grund zur Annahme, dass das Verfahren möglicherweise auch als Supplement anderer Ansätze genutzt werden könnte, welche zum Ziel haben die Positionen zu identifizieren, die die Ligandenpräferenz in vivo determinieren. Informationen, die nicht nur für ein besseres Verständnis der SH3-Domäne (und möglicherweise auch anderer Proteininteraktionsdomänen) von grundlegender Bedeutung sind, sondern auch aus pharmakologischer Sicht von großem Interesse sein dürften.