Inhalt des Dokuments
Sven Jäger
- © privat
Wissenschaftlicher Mitarbeiter
Fakultät II - Mathematik und Naturwissenschaften
Institut für Mathematik, Sekr. MA 5-2
Technische Universität Berlin
Straße des 17. Juni 136
10623 Berlin
Deutschland
E-Mail: jaeger #at# math.tu-berlin.de
Tel.: +49 (0)30 314-21095
Fax: +49 (0)30 314-25191
Raum: MA 511
Sprechzeiten: Dienstag 17:00 Uhr. Bitte schicken Sie mir bis Dienstagmittag eine E-Mail, um den Zoom-Link zu erhalten.
Lehre
| SS 2021 | Computerorientierte Mathematik II (theoretische Hausaufgaben) |
| WS 2020/21 | Introduction to Linear and Combinatorial Optimization (ADM I) |
| SS 2020 | Discrete Optimization (ADM II) |
| WS 2019/20 | Introduction to Linear and Combinatorial Optimization (ADM I) |
| SS 2019 | Discrete Optimization (ADM II) |
| WS 2018/19 | Introduction to Linear and Combinatorial Optimization (ADM I) |
| SS 2018 | Computerorientierte Mathematik II (Programmieraufgaben) |
| WS 2017/18 | Computerorientierte Mathematik I (Programmieraufgaben) |
| SS 2017 | Computerorientierte Mathematik II (theoretische Hausaufgaben) |
| WS 2016/17 | Computerorientierte Mathematik I (theoretische Hausaufgaben) |
Lebenslauf
| Seit Juli 2016 | Wissenschaftlicher Mitarbeiter am Lehrstuhl von Martin Skutella, Technische Universität Berlin |
| 2013 - 2016: | Masterstudium der Mathematik und der Angewandten Informatik an der Georg-August-Universität Göttingen |
| 2010 - 2013: | Bachelor-Studium der Mathematik und der Angewandten Informatik an der Georg-August-Universität Göttingen |
| 2010: | Abitur am Hainberg-Gymnasium Göttingen |
| Zitatschlüssel | GueltasDuezguenHerzog+2014 |
|---|---|
| Autor | Gültas, Mehmet and Düzgün, Güncel and Herzog, Sebastian and Jäger, Sven and Meckbach, Cornelia and Wingender, Edgar and Waack, Stephan |
| Seiten | 1-17 |
| Jahr | 2014 |
| ISSN | 1471-2105 |
| DOI | 10.1186/1471-2105-15-96 |
| Journal | BMC Bioinformatics |
| Jahrgang | 15 |
| Nummer | 96 |
| Monat | April |
| Verlag | BioMed Central Ltd. |
| Zusammenfassung | Background: The identification of functionally or structurally important non-conserved residue sites in protein MSAs is an important challenge for understanding the structural basis and molecular mechanism of protein functions. Despite the rich literature on compensatory mutations as well as sequence conservation analysis for the detection of those important residues, previous methods often rely on classical information-theoretic measures. However, these measures usually do not take into account dis/similarities of amino acids which are likely to be crucial for those residues. In this study, we present a new method, the Quantum Coupled Mutation Finder (QCMF) that incorporates significant dis/similar amino acid pair signals in the prediction of functionally or structurally important sites. Results: The result of this study is twofold. First, using the essential sites of two human proteins, namely epidermal growth factor receptor (EGFR) and glucokinase (GCK), we tested the QCMF-method. The QCMF includes two metrics based on quantum Jensen-Shannon divergence to measure both sequence conservation and compensatory mutations. We found that the QCMF reaches an improved performance in identifying essential sites from MSAs of both proteins with a significantly higher Matthews correlation coefficient (MCC) value in comparison to previous methods. Second, using a data set of 153 proteins, we made a pairwise comparison between QCMF and three conventional methods. This comparison study strongly suggests that QCMF complements the conventional methods for the identification of correlated mutations in MSAs. Conclusions: QCMF utilizes the notion of entanglement, which is a major resource of quantum information, to model significant dissimilar and similar amino acid pair signals in the detection of functionally or structurally important sites. Our results suggest that on the one hand QCMF significantly outperforms the previous method, which mainly focuses on dissimilar amino acid signals, to detect essential sites in proteins. On the other hand, it is complementary to the existing methods for the identification of correlated mutations. The method of QCMF is computationally intensive. To ensure a feasible computation time of the QCMF’s algorithm, we leveraged Compute Unified Device Architecture (CUDA). The QCMF server is freely accessible at http://qcmf.informatik.uni-goettingen.de/. |
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