MUFOLD: A new solution for protein 3D structure prediction

By Jingfen Zhang, Qingguo Wang, Bogdan Barz, Zhiquan He, Ioan Kosztin, Yingzi Shang, Dong Xu. 2010

78 citations recorded by the source corpus.

There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient 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 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 interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the 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 >25% sequence identity to any target protein is included. The average root‐mean‐square deviation 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 of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community‐wide experiment for protein structure prediction CASP8. Proteins 2010. © 2009 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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Citations in the corpus

Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement (2015, methodology): “The best-performing group with 12 improvements was TS330 (BAKER_ROSETTASERVER), followed by TS280 (ProQ2clust2) and TS333 (MUFOLD-Server; Zhang et al., 2010), with ten improvements each (Figure 3).”
Atomic-level protein structure refinement using fragment guided molecular dynamics conformation sampling (2011, methodology): “…templates are often identified through metathreading techniques (Fischer, 2003; Ginalski et al., 2003; Wu and Zhang, 2007), and full-length models are built by combining the structural fragments/restraints from multiple templates (Cheng, 2008; Raman et al., 2009; Zhang et al., 2010; Zhang, 2009a).”
Large-scale model quality assessment for improving protein tertiary structure prediction (2015, methodology): “…exposed score, and etc.) (Mishra et al. , 2013), the normalized quality score generated by ModelEvaluator (Wang et al. , 2009), RWplus score (Zhang and Zhang, 2010), dope score (Shen and Sali, 2006) and RF_CB_SRS_OD score (Rykunov and Fiser, 2007); ProQ2 The highlighted methods are built in house.”
UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling (2016, methodology): “Considering the 10 targets with reasonably accurate contact maps (sensitivity>0.5), UniCon3D generates 8 models with TM-score>0.5 indicating correct folds (Xu and Zhang, 2010), while GDFuzz3D produces 6 models with TM-score>0.5 and FT-COMAR returned only 4 models (Supplementary Table S5).”