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    MUFOLD Algorithms for Protein 3D Structure Prediction
    Update time: 2011-06-15
    Text Size: A A A
    Speaker:Prof. Dong Xu, University of Missouri
    Time:10:00am—11:30am, June 17th, 2011 (Friday)
    Place:Room 446, 4th Floor, ICT, CAS
    Knowledge of the three-dimensional structure of a protein often provides a basis for understanding its function. However, the gap between numbers of known protein sequences and structures has been dramatically increasing. One important approach to bridge this gap is computational prediction of protein structure from sequence. There have been steady improvements in protein structure prediction during the past two decades. However, current methods are still far from consistently predicting structural models accurately, especially with computing power accessible to common users. Towards achieving more accurate and efficient structure prediction, we developed a dramatically different framework from conventional methods for protein structure prediction. The framework is implemented into a software system MUFOLD, which integrates a number of novel methods. First, MUFOLD has a systematic protocol to identify useful templates and fragments from Protein Data Bank (PDB) for a given target protein. Next, an efficient process was applied for iterative coarse-grain model generation and evaluation. In this process, MUFOLD applies Multidimensional Scaling to construct multiple models by sampling inter-residue spatial restraints derived from alignments. It then evaluates models through clustering and a machine learning based scoring function, and iteratively improves selected models by integrating spatial restraints and previous models. Finally, the models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. The computing time of MUFOLD is much shorter than most other tools for protein structure prediction. MUFOLD demonstrated its success in the community-wide experiment for protein structure prediction CASP.
    Dong Xu is James C. Dowell Professor and Chair of Computer Science Department, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri. He obtained his Ph.D. from the University of Illinois, Urbana-Champaign in 1995 and did two-year postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining University of Missouri. His research includes protein structure prediction, high-throughput biological data analyses, in silico studies of plants, microbes, and cancers. He has published more than 180 papers. 


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