We have recently developed a quality assessment method to detect protein structural model outliers using machine learning techniques. Our method, called DAQ (Deep-learning-based Amino acid-wise model Quality) score, uses deep neural network to capture local density features of amino acids and atoms in proteins and assesses the likelihood that modeled residues in a structural model are correct. DAQ is also able to detect not only errors in conformations but also shifts in sequence assignment to otherwise correct main-chain conformations, which is often not easy to detect by checking density fitting.<br/>Here, we performed a PDB-scale model analysis by DAQ. We applied DAQ to around 10,000 protein structure models in PDB that were derived from cryo-EM maps deposited in Electron Microscopy Data Bank (EMDB). We report the tendency of common errors made in the models through the large-scale analysis. When authors deposited updated structure models to PDB over an initial model, we see clear improvement of DAQ score in the updated version of the model. A common type of errors observed include sequence shifts along alpha helices. Model assessment results with DAQ are made available in a database (https://daqdb.bio.purdue.edu/), where models can be searched by PDB IDs, EMDB IDs, and keywords.

DAQ-score database: Deep-learning based quality estimation of cryo-EM derived protein models

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