Generative Modeling for Protein Structures
Analyzing the structure and function of proteins is a key part of understanding biology at the molecular and cellular level. In addition, a major engineering challenge is to design new proteins in a principled and methodical way. Current computational modeling methods for protein design are slow and often require human oversight and intervention. Here, we apply Generative Adversarial Networks (GANs) to the task of generating protein structures, toward application in fast de novo protein design. We encode protein structures in terms of pairwise distances between ↵-carbons on the protein backbone, which eliminates the need for the generative model to learn translational and rotational symmetries. We then introduce a convex formulation of corruption-robust 3D structure recovery to fold the protein structures from generated pairwise distance maps, and solve these problems using the Alternating Direction Method of Multipliers. We test the effectiveness of our models by predicting completions of corrupted protein structures and show that the method is capable of quickly producing structurally plausible solutions.
Overview In silico protein folding is an essential part of many bioengineering pipelines; however, all existing methods are highly computationally expensive, which makes the generation of large numbers of candidate protein structures infeasible. The authors propose an alternative to the incumbent biophysical models (the most common of which is Rosetta) which uses instead a generative adversarial network (GAN) to generate a pairwise distance matrix for alpha-carbons within a variable-length amino acid chain. The authors then use an existing algorithm called Alternating Direction Method of Multipliers (ADMM) to reconstruct the 3D position of the alpha-carbons, and finally use a partial solution from Rosetta to obtain the full amino acid backbone structure.  Technical quality  The authors’ method is clearly and adequately described. The experiments are well thought out and results are presented fairly. In the de novo protein folding experiment, the authors compare their method to five other protein folding methods. Aside from 3DGAN (Wu et al, NIPS 2016), it is not clear which (or any) of these are published methods, or whether they were partial methods invented by the authors. Regardless of the origin of these methods, the authors show that potential alternative formulations of their method using torsion angles, full-atom GANs, or Hidden Markov Models are insufficient for de novo protein generation.  However, between the GAN folded by fragment sampling and the GAN folded by ADMM, it is worth noting that none of the proteins generated by the authors’ chosen method (ADMM) contain any beta sheets. This pattern is clearly repeated in the inpainting experiment, where not one of the proteins in which the original inpainted section contained a beta sheet (1mva, 3h3i, 4ang, 1ahk, 3ame) has a beta sheet when inpainted by ADMM GAN. As one of the most common tertiary amino acid structures, the failure to recapitulate a beta sheet is a severe technical flaw in the proposed method.  Additionally, as noted by the authors, the metrics used for numerical comparison of the tested methods are significantly flawed. The authors note that “in the case where the inpainted solution is plausible but deviates from the native structure, the rigid-body alignment error will be high”. However, the authors could at the very least verify that the GAN is capable of producing *a* plausible solution by examining the distribution of rigid-body alignment errors for all possible GAN inpainting solutions. If the minimum (or e.g. 10th percentile) rigid-body alignment error across hundreds of inpainting solutions approaches the scores achieved by Rosetta, this would be a convincing argument that the GAN is capable of generating biologically plausible solutions, and that other solutions with higher rigid-body alignment error are simply alternative solutions less similar to the original protein that may still be valid folded proteins. Novelty While the authors’ proposed algorithm is indeed a novel pipeline, and to date the first attempt to apply GANs to protein modeling, it should be noted that none of the component parts of the algorithm are novel. ADMM was invented in 2011 and has been previously applied to protein folding, and the semantic inpainting method used in section 4 was previously described in 2016. Thus the authors present a novel application of existing technologies.  Potential impact  The authors’ work has the potential to be highly impactful, contingent on the resolution of the technical failure to produce beta sheet structures. De novo protein design is a highly computationally expensive task, and additionally requires human intervention. A fast, automated and performant method to design proteins in a high-throughput manner could revolutionize drug design; however, the authors have not yet demonstrated that their algorithm is genuinely competitive with slower alternatives.  Clarity The paper is well written and easy to read.   Minor comments: - “Our model for the scope of this paper is not necessarily learning protein structures which will fold, but rather is learning building block features that define secondary and tertiary structural elements.” – this sentence is confusing.  - The authors claim that the semidefinite program to infer 3D coordinates from pairwise distances is quadratic, and that ADMM by contrast converges “quickly”. What is quickly? Could this be shown, perhaps in a supplementary figure? - It would also be good to see the difference in performance of the ADMM folding method compared to the GAN + fragment sampling shown in Figure 3. - Equation 3 fails to define \lambda and Z. - Equation 4: should G_{k+1} be dependent on G_k, rather than G? - If is not clear how the alpha-carbon trace algorithm works, and whether it is a custom script written by the authors, or included in the Rosetta package.  The authors present a novel application of GANs in a field where the impact of such a technology could be very high.  (post response) The authors have addressed my major concern with the manuscript, the inability to produce beta sheets, as this inability is caused not by the ADMM GAN but by the protein rendering software which creates the cartoons. Discussion of how to overcome this issue, in addition to added reporting on runtimes and the distribution of rigid-body alignment error will make more convincing the capability of GANs to revolutionize the field of in silico protein modeling. Recommendation: weak accept.