Scoring function for automated assessment of protein structure template quality

By Yang Zhang, Jeffrey Skolnick. 2004

2,056 citations recorded by the source corpus.

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

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Citations in the corpus

Accurate prediction of protein structures and interactions using a 3-track neural network (2021, neutral): “4B) chains, and in many cases, the resulting models were very close to the actual structures (TM-score (33) > 0.”
PotentialNet for Molecular Property Prediction (2018, background): “0 minus the sequence homology or the TMScore 59 .”
AFsample: improving multimer prediction with AlphaFold using massive sampling (2023, background): “In addition, it also calculates a predicted TMscore (pTM; Zhang and Skolnick 2004) from the predicted aligned error (PAE) matrix (Jumper et al. 2021).”
Accurate RNA 3D structure prediction using a language model-based deep learning approach (2022, positive): “…between the test set and our training data was substantially positively correlated with the performance of RhoFold+, as measured by the TM score and the local distance difference test (LDDT), a superposition-free score that evaluates local distance differences for all atoms in a model 32,33 .”
Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022, methodology): “For the targets, each model’s 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…”
Improved prediction of protein-protein interactions using AlphaFold2 (2021, background): “The second model, model_1_ptm, is a ne-tuned version of model_1 that predicts the TMscore 44 and alignment errors 15 .”
Improved protein structure prediction using predicted interresidue orientations (2019, methodology): “The average TM-score (21) of our method is 0.”
RUPEE: A fast and accurate purely geometric protein structure search (2018, background): “On the other hand, the TM-score [32] takes all residues into account.”
TM-align: a protein structure alignment algorithm based on the TM-score (2005, positive): “To have a single scoring function that can reasonably assess the alignment quality and balance the coverage and accuracy, we use the TM-score, which is defined as (13)”
End-to-end differentiable learning of protein structure (2018, methodology): “RGNs using TM scores (Zhang and Skolnick, 2004), which are defined by the following equation:”