Inhalt des Dokuments
Research interests
- Ab initio Protein Structure Prediction
- Hybrid Methods in Protein Structure Prediction
- Protein Classification
- Machine Learning
Curriculum Vitae
- 03/2013-present
Research Assistant at the Robotics & Biology Lab, TU Berlin (Prof. Oliver Brock). - 11/2012
Master of Sc. in Computer Science with focus "Intelligent Systems" at the Technische Universität Berlin. - 02/2011-12/2011
Student Assistant at the Neural Information Processing Group, Technische Universität Berlin (Prof. Klaus Obermayer). - 09/2009-08/2010
Exchange student at the Shanghai Jiaotong University (TU-Berlin Scholarship) - 03/2010
Bachelor of Sc. in Computer Science at the Technische Universität Berlin. - 04/2009-08/2009
Student Assistant at the Daimler Center for Automotive Information Technology Innovations, Technische Universität Berlin
Publications
| Citation key | Mabrouk-et-al2016 |
|---|---|
| Author | Mahmoud Mabrouk and Tim Werner and Michael Schneider and Ines Putz and Oliver Brock |
| Pages | 87–104 |
| Year | 2016 |
| DOI | 10.1002/prot.24950 |
| Journal | Proteins: Structure, Function, and Bioinformatics |
| Volume | 84(Suppl 1) |
| Note | http://dx.doi.org/10.1002/prot.24950 |
| Abstract | The CASP experiment is a biannual benchmark for assessing protein structure prediction methods. In CASP11, RBO Aleph ranked as one of the top-performing automated servers in the free modeling category. This category consists of targets for which structural templates are not easily retrievable. We analyze the performance of RBO Aleph and show that its success in CASP was a result of its ab initio structure prediction protocol. A detailed analysis of this protocol demonstrates that two components unique to our method greatly contributed to prediction quality: residue–residue contact prediction by EPC-map and contact-guided conformational space search by model-based search (MBS). Interestingly, our analysis also points to a possible fundamental problem in evaluating the performance of protein structure prediction methods: Improvements in components of the method do not necessarily lead to improvements of the entire method. This points to the fact that these components interact in ways that are poorly understood. This problem, if indeed true, represents a significant obstacle to community-wide progress. |
