Although there have been steady improvements in protein structure prediction during the past two decades, current methods are still far from consistently predicting accurate structural models with computing power accessible to common users. Towards achieving more accurate and faster structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank (PDB) for a given target protein. Then, an efficient process was applied for iterative coarse-grain model generation and evaluation at the Cα or backbone level. In this process, we construct models using spatial restraints derived from alignments by Multi-dimensional Scaling (MDS), evaluate models through clustering and static scoring functions, and iteratively improve selected models by integrating spatial restraints and previous models. Finally, the full-atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with a >25% sequence identity to any target protein is used. The average RMSD of the best models from the native structures is 4.28 Å,which shows significant and systematic improvement over our previous methods. The computing time is much shorter than other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community-wide experiment for protein structure prediction CASP8.

Bio:
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 150 papers. He is a recipient of 2001 R&D 100 Award and 2003 Federal Laboratory Consortium’s Award of Excellence in Technology Transfer. He is a member of the Editorial Board for “Current Protein and Peptide Science” and “Applied and Environmental Microbiology”. He is a standing member of the NIH Biodata Management and Analysis Panel.