Accurate self-assessment in 2D: protein contact map quality estimation by deep evolutionary reconciliation

Motivation: Protein contact maps have proven to be a valuable tool in the deep learning revolution of protein structure prediction, ushering in the recent breakthrough by AlphaFold2. However, self-assessment of the quality of predicted structures is typically performed at the granularity of 3D coordinates as opposed to directly exploiting the rotation- and translation-invariant 2D contact maps. Results: Here we present rrQNet, a deep learning method for self-assessment in 2D by contact map quality estimation. Our approach is based on the intuition that for a contact map to be of high quality, the residue pairs predicted to be in contact should be mutually consistent with the evolution-ary context of the protein. The deep neural network architecture of rrQNet implements this intuition by cascading two deep modules--one encoding the evolutionary context and the other performing evolutionary reconciliation. The penultimate stage of rrQNet estimates the quality scores at the inter-acting residue-pair level, which are then aggregated for estimating the quality of a contact map. This design choice offers versatility at varied resolutions from individual residue pairs to full-fledged contact maps. Trained on multiple complementary sources of contact predictors, rrQNet facilitates generalizability across various contact maps. By rigorously testing using publicly available datasets and comparing against several in-house baseline approaches, we show that rrQNet accurately reproduces the true quality score of a predicted contact map and successfully distinguishes between accurate and inaccurate contact maps predicted by a wide variety of contact predictors. Availability: The open-source rrQNet software package is freely available at https://github.com/Bhattacharya-Lab/rrQNet. ### Competing Interest Statement The authors have declared no competing interest.