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Mahmoud Mabrouk

Lupe [1]

E-mail query [2]

Raum: MAR 5.065
Telefon: +49.30.314-73 118

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 [3], TU Berlin (Prof. Oliver Brock).
  • 11/2012
    Master of Sc. in Computer Science with focus "Intelligent Systems" at the Technische Universität Berlin [4].
  • 02/2011-12/2011
    Student Assistant at the Neural Information Processing Group [5], Technische Universität Berlin (Prof. Klaus Obermayer).
  • 09/2009-08/2010
    Exchange student at the Shanghai Jiaotong University [6] (TU-Berlin Scholarship)
  • 03/2010
    Bachelor of Sc. in Computer Science at the Technische Universität Berlin [7].
  • 04/2009-08/2009
    Student Assistant at the
    Daimler Center for Automotive Information Technology Innovations [8], Technische Universität Berlin


Analysis of free modeling predictions by RBO aleph in CASP11
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.
Link to original publication [9] Download Bibtex entry [10]


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