Proteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein's structure can be time-consuming, prohibitively expensive, and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results. GRAPHQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance and computational efficiency. In this work, we demonstrate significant improvements over the state-ofthe-art for both hand-engineered and representation-learning approaches, as well as carefully evaluating the individual contributions of GRAPHQA components.
The paper proposes use of graph convolutional networks (GCN) for quality assessments (QA) of protein structure predictions. In particular, protein structure prediction is a very active area of research witnessing steady progress during the previous decade or so. To estimate the quality of the prediction, many experimentally resolved structures are needed. However, experimental structure determination is expensive and many protein families are notoriously hard to experiment with. Thus, current estimates of the quality of the protein structure prediction models are incomplete and biased. As a result, there is an interest in guessing quality of protein structure predictions on protein families that are not characterized experimentally. Previous papers already proposed several neural network architectures for the QA task. This paper shows that GCN applied on a graph of a predicted protein structure achieves higher accuracy than the previously proposed neural networks
Strengths:
+ The proposed GCN outperforms other neural network baselines.
+ The protein representation is reasonable.
+ The paper is reasonably well written with a nice overview of the related work.
+ Ablation studies are well done, in clean graphs. This can be useful for other authors who work with GCNs.
Weaknesses:
- Methodological novelty is low -- this is a straightforward application of GCN 
- The objective of QA is a bit suspect for the sole reason that the training and testing is performed using experimentally resolved protein structures. This data set is biased and there are no guarantees that the reported accuracy will hold over a vast range of protein families that are not structurally characterized.   
- Separation encoding is done as a one-hot vector. This could probably be passed as a scalar value. Would be nice to have comparison between 1-hot vs scalar in the experimental results
- Formulas in section 2.3 are cryptic for audience unfamiliar with GCN and it is not specific to this application.
- Figure 3b) shows that there is a cluster of predicting 0 where the ground truth is bigger than 0.6.
Overall, this is a borderline paper. There is little methodological novelty and the QA application is a bit of a niche problem in bioinformatics. However, the results show a decent improvement over the state of the art in this particular application, so this paper might be of importance for a limited audience interested in this problem. Giving this work a benefit of the doubt the entered rating is a weak accept.