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# What is DAQ

DAQ is a deep-learning-based score that quantifies residue-wise local quality for protein models from cryo-electron microscopy (cryo-EM) maps.

# 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 by a window of 19 residues along the sequence.

# Key Insights

1. A residue in a model has a positive score if the secondary structure/amino acid/Cα position assignment is correct. A negative score indicates that the assignment may be incorrect and worth close check.
2. A negative $$DAQ(AA)$$ score indicates the possibility of misalignment of the amino acid sequence to the local structure. A negative $$DAQ(C\alpha)$$ score indicates the possibility that the local region has incorrect conformation.
3. If a position in the map does not have distinct density pattern for the assigned amino acid (or secondary structure, Cα atom), $$DAQ$$ will be close to 0.