Ab initio protein structure prediction using chunk-TASSER.

By Hongyi Zhou, Jeffrey Skolnick. 2007

93 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

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. Yang Zhang
2. Sitao Wu
3. Dong Xu
4. Jianyi Yang
5. Robin Pearce
6. Lukasz Kurgan
7. Ke Chen
8. Jeffrey Skolnick

In Molecular Biology

1. Yang Zhang
2. Dong Xu
3. Sitao Wu
4. Jianyi Yang
5. Robin Pearce
6. Lukasz Kurgan
7. Hongyi Zhou
8. Jeffrey Skolnick

In Spectroscopy

1. Yang Zhang
2. Sitao Wu
3. Lukasz Kurgan
4. Ke Chen
5. Jeffrey Skolnick
6. Mu Gao
7. Hongyi Zhou
8. Dong Xu

Citations in the corpus

• Toward optimal fragment generations for ab initio protein structure assembly (2012, background): “For long-range contacts, fragments in the range of [9, 20] have the best accuracy.”
• Computational prediction of secondary and supersecondary structures. (2013, methodology): “First, we predict how the sequence folds into the secondary structure, then how these secondary structure elements come together to form SSSs, and finally the information about the secondary and supersecondary structures is used to help in computational determination of the full three-dimensional mo”
• Recognizing protein substructure similarity using segmental threading. (2010, methodology): “In chunk-TASSER (Zhou and Skolnick, 2007), since the ‘‘chunk-structure’’ is built by ab initio simulation, it does not involve the procedure of threading sequence segments through structure databases.”
• DESTINI: A deep-learning approach to contact-driven protein structure prediction (2019, methodology): “When there is no suitable template model available, the structural modeling essentially makes de novo predictions 4 ; if there is a significant structural template hit, modeling based on template(s) is conducted.”
• MUSTER: Improving protein sequence profile–profile alignments by using multiple sources of structure information (2008, background): “The combination of both close and distant sequence profiles follows Skolnick’s idea25,26,43 which helps increase the MUS-TER alignment sensitivity in different homology area.”