Generative Models for Graph-Based Protein Design
Authors: John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the merits of our approach via a detailed empirical study. Specifically, we evaluate our model's performance for structural generalization to sequences of protein 3D folds that are topologically distinct from those in the training set. |
| Researcher Affiliation | Academia | John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola Computer Science and Artificial Intelligence Lab, MIT {ingraham, vgarg, regina, tommi}@csail.mit.edu |
| Pseudocode | No | The paper describes the model architecture and components in text and diagrams, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at github.com/jingraham/neurips19-graph-protein-design. |
| Open Datasets | Yes | To evaluate the ability of our models to generalize across different protein folds, we collected a dataset based on the CATH hierarchical classification of protein structure [40]. |
| Dataset Splits | Yes | For all domains in the CATH 4.2 40% non-redundant set of proteins, we obtained full chains up to length 500 and then randomly assigned their CATH topology classifications (CAT codes) to train, validation and test sets at a targeted 80/10/10 split. This resulted in a dataset of 18024 chains in the training set, 608 chains in the validation set, and 1120 chains in the test set. |
| Hardware Specification | Yes | CPU: single core of Intel Xeon Gold 5115, GPU: NVIDIA RTX 2080 |
| Software Dependencies | Yes | We used the latest version of Rosetta (3.10) to design sequences for our Single chain test set with the fixbb fixed-backbone design protocol and default parameters (Table 4a). |
| Experiment Setup | Yes | In all experiments, we used three layers of self-attention and position-wise feedforward modules for the encoder and decoder with a hidden dimension of 128. Optimization We trained models using the learning rate schedule and initialization of the original Transformer paper [7], a dropout rate of 10% [42], a label smoothing rate of 10%, and early stopping based on validation perplexity. |