How significant is a protein structure similarity with TM-score = 0.5?

By Jinrui Xu, Yang Zhang. 2010

1,081 citations recorded by the source corpus.

Subfields: 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 Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Jinbo Xu
4. Jianlin Cheng
5. David T. Jones
6. Sheng Wang
7. David Baker
8. Sergey Ovchinnikov

In Computational Biology & Bioinformatics

1. Yang Zhang
2. Jinbo Xu
3. Robin Pearce
4. David T. Jones
5. Sergey Ovchinnikov
6. Sheng Wang
7. David Baker
8. Arne Elofsson

Citations in the corpus

• Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022, methodology): “…similarity to the experimental structures was quantified using template modeling (TM) score ( Zhang and Skolnick, 2004 ; Zhang and Skolnick, 2005 ; Xu and Zhang, 2010 ), a metric ranging from 0 to 1, which indicates how well the two backbone (C α atoms) structures superimpose over one another…”
• SWISS-MODEL: homology modelling of protein structures and complexes (2018, neutral): “The same also holds for other commonly used model quality metrics, i.e. GDT-HA (Global Distance Test High Accuracy score) (58) and TM-score (Template Modelling score) (59) (Supplementary Figure S1).”
• DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, background): “57, suggesting they have the same fold, the fold of the SCOP domain is transferred to the CASP target (Xu and Zhang, 2010).”
• End-to-end differentiable learning of protein structure (2018, background): “TM scores range from 0 to 1, with a score of < 0.17 corresponding to a random unrelated protein, and > 0.5 generally corresponding to the same protein fold (Xu and Zhang, 2010).”
• Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “5, respectively, using DeepContact’s predicted contacts representing a significant improvement (Figure 2D) (Xu and Zhang, 2010).”
• A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments (2019, methodology): “9,10 GDTTS ranges between 0 and 100 with scores below Finally, the TM 9 score (defined from 0 to 1, higher is better) combines the distances between all residues of the aligned region in the target and model, weighting them by a reference distance calibrated from the target's length instead of consi”
• Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, neutral): “We compare the backbone of the predicted structure with the original backbone structure using TM-score (Xu & Zhang, 2010) and root-mean-square-distance (RMSD) for the motif. et al., 2022).”
• Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4* (2015, methodology): “The structure comparison method was then used to compare the hZIP4 structural model with non-redundant Protein Data Bank (PDB) membrane protein structures (30, 31).”
• Universal Transforming Geometric Network (2019, background): “A TM score < 0.17 corresponds to random alignment whereas a TM score of > 0.5 correspond to the same protein fold (Xu & Zhang (2010)).”