Transferrable End-to-End Learning for Protein Interface Prediction
While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question. In this work we demonstrate that representations learned from raw atomic coordinates can outperform hand-engineered structural features while displaying a much higher degree of transferrability. To do so, we focus on a central problem in biology: predicting how proteins interact with one another—that is, which surfaces of one protein bind to which surfaces of another protein. We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction. Despite using only spatial coordinates and identities of atoms as inputs, SASNet outperforms state-of-the-art methods that rely on hand-engineered, high-level features. These results are particularly striking because we train the method entirely on a significantly biased data set that does not account for the fact that proteins deform when binding to one another. Demonstrating the first successful application of transfer learning to atomic-level data, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform.
 Summary: This paper uses siamese networks to define a discriminative function for predicting protein-protein interaction interfaces. They show improvements in predictive performance over some other recent deep learning methods. The work is more suitable for a bioinformatics audience though, as the bigger contribution is on the particular application, rather than the model / method itself. Novelty: The main contribution of this paper is the representation of the protein interaction data in the input layer of the CNN Clarity: - The paper is well written, with ample background into the problem. Significance: - Their method improves over prior deep learning approaches to this problem. However, the results are a bit misleading in their reporting of the std error. They should try different train/test splits and report the performance. - This is an interesting application paper and would be of interest to computational biologists and potentially some other members of the ICLR community - Protein conformation information is not required by their method Comments: - The authors should include citations and motivation for some of their choices (what sequence identity is used, what cut-offs are used etc) - The authors should compare to at least some popular previous approaches that use a feature engineering based methodology such as - IntPred - The authors use a balanced ratio of positive and negative examples. The true distribution of interacting residues is not balanced -- there are several orders of magnitude more non-interacting residues than interacting ones. Can they show performance at various ratios of positive:negative examples? In case there is a consistent improvement over prior methods, then this would be a clear winner
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Transferrable End-to-End Learning for Protein Interface Prediction
While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question. In this work we demonstrate that representations learned from raw atomic coordinates can outperform hand-engineered structural features while displaying a much higher degree of transferrability. To do so, we focus on a central problem in biology: predicting how proteins interact with one another—that is, which surfaces of one protein bind to which surfaces of another protein. We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction. Despite using only spatial coordinates and identities of atoms as inputs, SASNet outperforms state-of-the-art methods that rely on hand-engineered, high-level features. These results are particularly striking because we train the method entirely on a significantly biased data set that does not account for the fact that proteins deform when binding to one another. Demonstrating the first successful application of transfer learning to atomic-level data, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Summary: This paper uses siamese networks to define a discriminative function for predicting protein-protein interaction interfaces. They show improvements in predictive performance over some other recent deep learning methods. The work is more suitable for a bioinformatics audience though, as the bigger contribution is on the particular application, rather than the model / method itself. Novelty: The main contribution of this paper is the representation of the protein interaction data in the input layer of the CNN Clarity: - The paper is well written, with ample background into the problem. Significance: - Their method improves over prior deep learning approaches to this problem. However, the results are a bit misleading in their reporting of the std error. They should try different train/test splits and report the performance. - This is an interesting application paper and would be of interest to computational biologists and potentially some other members of the ICLR community - Protein conformation information is not required by their method Comments: - The authors should include citations and motivation for some of their choices (what sequence identity is used, what cut-offs are used etc) - The authors should compare to at least some popular previous approaches that use a feature engineering based methodology such as - IntPred - The authors use a balanced ratio of positive and negative examples. The true distribution of interacting residues is not balanced -- there are several orders of magnitude more non-interacting residues than interacting ones. Can they show performance at various ratios of positive:negative examples? In case there is a consistent improvement over prior methods, then this would be a clear winner
assistant
 Summary: This paper uses siamese networks to define a discriminative function for predicting protein-protein interaction interfaces. They show improvements in predictive performance over some other recent deep learning methods. The work is more suitable for a bioinformatics audience though, as the bigger contribution is on the particular application, rather than the model / method itself. Novelty: The main contribution of this paper is the representation of the protein interaction data in the input layer of the CNN Clarity: - The paper is well written, with ample background into the problem. Significance: - Their method improves over prior deep learning approaches to this problem. However, the results are a bit misleading in their reporting of the std error. They should try different train/test splits and report the performance. - This is an interesting application paper and would be of interest to computational biologists and potentially some other members of the ICLR community - Protein conformation information is not required by their method Comments: - The authors should include citations and motivation for some of their choices (what sequence identity is used, what cut-offs are used etc) - The authors should compare to at least some popular previous approaches that use a feature engineering based methodology such as - IntPred - The authors use a balanced ratio of positive and negative examples. The true distribution of interacting residues is not balanced -- there are several orders of magnitude more non-interacting residues than interacting ones. Can they show performance at various ratios of positive:negative examples? In case there is a consistent improvement over prior methods, then this would be a clear winner