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Updated entries on 2024/10/31 based on the PDB data as of 2024/06/26.

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Key Features

The model is colored by DAQ score scaled from red (low) to blue (high). PDB 7JSN chain B Version 1, EMD-22458.

This database provides pre-computed DAQ score for structure models in PDB that were derived from cryo-EM maps. Entries can be searched by PDB ID, EMDB ID, or key words. DAQ score along a protein structure model is visualized in an interactive structure viewer as well as a graph and a score table, which are connected with the model structure in the viewer. Three scores are provided, which evaluate correctness of amino acid assignments, Cα positions, and secondary structure assignments.

Overview of DAQ

DAQ uses deep-learning and computes the likelihood that each local position in a cryo-EM map corresponds to different secondary structures, amino acids, and Cα atoms from its local density features. Then, a plausibility of each residue in a structure model from the cryo-EM map is quantified with the following equations.

The amino acid type of residue \(i\) in a model is evaluated as: \[ DAQ(AA)(i)=log\left(\frac{P_{aa(i)}(i)}{\sum_{j}P_{aa(i)}(j)/N}\right), \] where \(aa(i)\) is the amino acid type of residue \(i\), \(P_{aa(i)}(i)\) is the computed probability for the amino acid type of residue \(i\) by deep learning, which is normalized by the average probability of the amino acid type across over all atom positions in the protein model.

The Cα position of residue \(i\) in a model is evaluated as: \[ DAQ(C\alpha)(i)=log\left(\frac{P_{C\alpha}(i)}{\sum_{j}P_{C\alpha}(j)/N}\right), \] where \(C\alpha(i)\) is the Cα atom of residue \(i\), \(P_{C\alpha}(i)\) is the computed probability that the position correspond to a Cα atom by deep learning, which is normalized by the average probability of Cα over all atom positions in the protein model.

Lastly, the secondary structure of residue \(i\) in a model is evaluated as: \[ DAQ(SS)\left(i\right)=\sum_{ss\in H,E,C}{{Pseq}_{ss}\left(i\right)log\left(\frac{P_{ss}\left(i\right)}{\sum_{j}P_{ss}(j)/N}\right)}, \] where \(SS(i)\) is the secondary structure type of residue \(i\) to be evaluated, \({Pseq}_{ss}(i)\) is the probability of the secondary structure \(ss\) for the amino acid residue \(i\) predicted from the protein sequence using a secondary structure prediction method, SPOT1D. \(P_{ss}(i)\) is the computed probability of the secondary structure of residue \(i\) by the deep learning, which is normalized by the average probability of the secondary structure type across over all atom positions in the protein model.

Computed scores are averaged over a window of 19 residues along the sequence, because this averaging better distinguished potential incorrect and correct residue placements in the benchmark.

Key Insights: What type of modeling issues does DAQ detect and when does DAQ tend not to flag?

In general, a residue in a model has a positive score if the secondary structure/amino acid/Cα position assignment is likely to be correct. A negative score indicates that the assignment may be incorrect and worth close check.

A negative DAQ(AA) score indicates the possibility of misalignment of the amino acid sequence to the local structure.

A negative DAQ(Cα) score indicates the possibility that the local region has incorrect conformation.

Residues with a DAQ(AA) score less than -0.5 are highly likely to be incorrect and therefore require attention. From the analysis in the original DAQ paper (Terashi et al., Nature Methods, 2022), it was extremely rare (0.066%) that correct residues have DAQ(AA) < -0.5.

On the other hand, incorrect residues can have DAQ(AA) > -0.5.

If a position in the map does not have distinct map features, which makes it difficult for the neural network to make a decisive call, DAQ tend to be close to 0.

Incorrect residues may have DAQ larger than -0.5, i.e. above the cutoff, 1) if the map resolution is globally or locally low at that position, e.g. at the borderline of volume overlap; 2) if incorrect residue assignments occur at a really short region in the protein because the presented DAQ is a window average of 19 residues.

DAQ-Score Database

Precomputed Residue-Wise Local Quality Scores of Protein Models from Cryo-EM Maps

Developed by Kihara Lab

Updated entries on 2024/10/31 based on the PDB data as of 2024/06/26.

What is DAQ

Key Features

Overview of DAQ

Key Insights: What type of modeling issues does DAQ detect and when does DAQ tend not to flag?