iQDeep: an integrated web server for protein scoring using multiscale deep learning models.

The remarkable recent advances in protein structure prediction have enabled computational modeling of protein structures with considerably higher accuracy than ever before. While state-of-the-art structure prediction methods provide self-assessment confidence scores of their own predictions, an independent and open-access system for protein scoring is still needed that can be applied to a broad range of predictive modeling scenarios. Here, we present iQDeep, an integrated and highly customizable web server for protein scoring, freely available at http://fusion.cs.vt.edu/iQDeep. The underlying method of iQDeep employs multiscale deep residual neural networks (ResNets) to perform residue-level error classifications, and then probabilistically combines the error classifications for protein scoring. By adjusting the error resolutions, our method can reliably estimate the standard- or high-accuracy variants of the Global Distance Test metric for versatile protein scoring. The performance of the method has been extensively tested and compared against the state-of-the-art approaches in multiple rounds of Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments including benchmark assessment in CASP12 and CASP13 as well as blind evaluation in CASP14. The iQDeep web server offers a number of convenient features, including (i) the choice of individual and batch processing modes; (ii) an interactive and privacy-preserving web interface for automated job submission, tracking, and results retrieval; (iii) web-based quantitative and visual analyses of the results including overall estimated score and its residue-wise breakdown along with agreements between various sequence- and structural-level features; (iv) extensive help information on job submission and results interpretation via web-based tutorial and help tooltips.