Toward optimal fragment generations for ab initio protein structure assembly

By Dong Xu, Yang Zhang. 2012

193 citations recorded by the source corpus.

Fragment assembly using structural motifs excised from other solved proteins has shown to be an efficient method for ab initio protein‐structure prediction. However, how to construct accurate fragments, how to derive optimal restraints from fragments, and what the best fragment length is are the basic issues yet to be systematically examined. In this work, we developed a gapless‐threading method to generate position‐specific structure fragments. Distance profiles and torsion angle pairs are then derived from the fragments by statistical consistency analysis, which achieved comparable accuracy with the machine‐learning‐based methods although the fragments were taken from unrelated proteins. When measured by both accuracies of the derived distance profiles and torsion angle pairs, we come to a consistent conclusion that the optimal fragment length for structural assembly is around 10, and at least 100 fragments at each location are needed to achieve optimal structure assembly. The distant profiles and torsion angle pairs as derived by the fragments have been successfully used in QUARK for ab initio protein structure assembly and are provided by the QUARK online server at http://zhanglab.ccmb. med.umich.edu/QUARK/. Proteins 2013. © 2012 Wiley Periodicals, Inc.

Subfields: Materials Chemistry, Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Robin Pearce
2. Yang Zhang
3. Jianyi Yang
4. Chengxin Zhang
5. S. Mortuza
6. Baoji He
7. Yanting Wang
8. Zhidong Xue

In Molecular Biology

1. Robin Pearce
2. Yang Zhang
3. Chengxin Zhang
4. Jianyi Yang
5. S. Mortuza
6. Xiaogen Zhou
7. Baoji He
8. Yanting Wang

In Computational Biology & Bioinformatics

1. Robin Pearce
2. Jianyi Yang
3. Chengxin Zhang
4. S. Mortuza
5. Yang Zhang
6. Zhidong Xue
7. Jouko Virtanen
8. Baoji He

Citations in the corpus

• Toward the solution of the protein structure prediction problem (2021, background): “The usefulness of deep learning-based contact map prediction was clearly demonstrated by C-I-TASSER and C-QUARK in CASP13, which were ranked as the fi rst and second best automated servers in CASP13, respectively (27).”
• De novo Protein Structure Prediction by Coupling Contact With Distance Profile (2020, background): “Xu and Zhang propose the concept of distance profile to derive long-range pair-wise distances and contact restraints from multiple fragments [36].”
• Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, methodology): “In CASP10 and CASP11, for instance, the interplay between the template-based I-TASSER 9,10 method and the ab initio folding QUARK 11,12 method has demonstrated enhancements of accuracy of the final models for FM targets.”
• Deep learning techniques have significantly impacted protein structure prediction and protein design. (2021, methodology): “…Rosetta [12] uses simulated annealing Monte Carlo simulations, while QUARK [13] uses REMC simulations with as many as 11 different conformational moves and extracts dis-tance-profile-based contact maps from the generated fragments in order to guide the simulations towards the native structure [15].”
• Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade (2015, result): “14,22 Meanwhile, QUARK-TMB has a stronger weight for the external spatial restraints than I-TASSER, which results in the final models with a closer similarity to the templates.”