Code to accompany Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL Townshend, and RO Dror.
This repository serves two purposes. If you would like to use the architecture for protein design, we provide the pipeline for our experiments as well as our final trained model. If you are interested in adapting the architecture for other purposes, we provide instructions for general use of the GVP.
UPDATE: A PyTorch Geometric version of the GVP is now available at https://github.com/drorlab/gvp-pytorch, emphasizing ease of use and modularity. All future changes will be in PyTorch and pushed to this new repository.
- UNIX environment
- python==3.7.6
- numpy==1.18.1
- scipy==1.4.1
- pandas==1.0.3
- tensorflow==2.1.0
- tqdm==4.42.1
Our training pipeline uses the CATH 4.2 dataset curated by Ingraham, et al, NeurIPS 2019. We provide code to train, validate, and test the model on this dataset. We also provide a pretrained model in models/cath_pretrained. If you want to test a trained model on new structures, see the section "Using the CPD model" below.
Run getCATH.sh in data/ to fetch the CATH 4.2 dataset. If you are interested in testing on the TS 50 test set, also run grep -Fv -f ts50remove.txt chain_set.jsonl > chain_set_ts50.jsonl to produce a training set without overlap with the TS 50 test set.
Run python3 train_cpd.py [dataset] in src/ where [dataset] is the complete CATH 4.2 dataset, ../data/chain_set.jsonl, or the CATH 4.2 with overlap with TS50 removed, ../data/chain_set_ts50.jsonl. Model checkpoints are saved to models/ identified by the timestamp of the start of the run and the epoch number.
To evaluate perplexity, run python3 test_cpd_perplexity.py ../models/cath_pretrained in src/.
| Command | Output |
|---|---|
python3 test_cpd_perplexity.py ../models/cath_pretrained |
ALL TEST PERPLEXITY 5.29298734664917 SHORT TEST PERPLEXITY 7.0954108238220215 SINGLE CHAIN TEST PERPLEXITY 7.4412713050842285 |
To evaluate recovery, run python3 test_cpd_recovery.py [model] [dataset] [output] in src/. [dataset] should be one of cath, short, sc, ts50. [model] should be ../models/ts50_pretrained if evaluating on the TS50 test set and ../models/cath_pretrained otherwise. Recoveries for each target will be dumped into the file [output]. To get the median recovery, run python3 analyze.py [output].
Because the recovery can take some time to run, we have supplied outputs in outputs/.
| Command | Output |
|---|---|
python3 analyze.py ../outputs/cath.out |
0.40187938705576753 |
python3 analyze.py ../outputs/short.out |
0.32149746594868545 |
python3 analyze.py ../outputs/sc.out |
0.319731182795699 |
python3 analyze.py ../outputs/ts50.out |
0.44852965747702583 |
To use the CPD model on your own backbone structures, first convert the structures into a json format as follows:
[
{
"seq": "TQDCSFQHSP...",
"coords": [[[74.46, 58.25, -21.65],...],...]
}
...
]
For each structure, coords should be a num_residues x 4 x 3 nested list of the positions of the backbone N, C-alpha, C, and O atoms of each residue (in that order). If only backbone information is available, you can use a placeholder sequence of the same length. Then, run the below instructions (the function sample is defined in test_cpd_recovery.py)
dataset = datasets.load_dataset(PATH_TO_JSON, batch_size=1, shuffle=False)
for structure, seq, mask in dataset:
n = 1 # number of sequences to sample
# model is a pretrained CPD model
design = sample(model, structure, mask, n)
design = tf.cast(design, tf.int32).numpy()
The output design now an n x num_residues array of n designs, with amino acids represented as integers according to the encodings used to train the model. The encodings used by our pretrained model are in /src/datasets.py.
We describe our implementation in several levels of abstraction to make it as easy as possible to adapt the GVP to your uses. If you have any questions, please contact [email protected].
The core GVP modules are implemented in src/GVP.py. It contains code for the GVP itself, the vector/scalar dropout, and the vector/scalar batch norm, each of which is a tf.keras.layers.Module. These modules are initialized as follows:
gvp = GVP(vi, vo, so)
dropout = GVPDropout(drop_rate, nv)
layernorm = GVPLayerNorm(nv)
In the code and comments, vi, vo, si, so refer to number of vector/scalar channels in/out. nv and ns are the number of scalar/vector channels, and nls and nlv are the scalar/vector nonlinearities. The value si doesn't need to be specified because TensorFlow imputes it at the first forward pass.
Because the modules are designed to easily replace dense layers in a GNN, they are designed to take a single tensor x instead of seperate scalar/vector channel tensors. This is accomplished by assigning the first 3*nv channels in the input tensor to be the nv vector channels and the remaining channels to be the ns scalar channels. We provide utility functions merge and split to convert between seperate tensors where the vector tensor has dims [..., 3, nv] and the scalar tensor has dims [..., ns], and a single tensor with dims [..., 3*nv + ns]. For example:
v, s = input_data
x = merge(v, s)
x = gvp(x)
x = dropout(x, training=True)
x = layernorm(x)
v, s = split(x, nv=v.shape[-1])
Use vs_concat(x1, x2, nv1, nv2) to concatenate tensors x1 and x2 with nv1 and nv2 implicit vector channels.
Our protein GNN is defined in src/models.py and is adapted from the protein GNN in Ingraham, et al, NeurIPS 2019. We provide two fully specified networks which take in raw protein representations and output a single global scalar prediction (MQAModel) or a 20-dimensional feature vector at each residue (CPDModel). Note that the CPDModel currently uses sequence information autoregressively. Sample usage:
mqa_model = MQAModel(node_dims, edge_dims, hidden_dims, num_layers)
X, S, mask = input_batch
output = mqa_model(X, S, mask) # dims [batch_size, 1]
The input X is a float tensor with dims [batch_size, num_residues, 4, 3] and has the backbone coordinates of N, C-alpha, C, and O atoms of each residue (in that order). S contains the sequence information as a integer tensor with dims [batch_size, num_residues]. The integer encodings can be arbitrary but the ones used by our pretrained model are defined in src/datasets.py. The mask is a float tensor with dims [batch, num_nodes] that is 1 for residues that exist and 0 for nodes that do not.
The three dims arguments should each be tuples (nv, ns) describing the number of vector and scalar channels to use in each embedding. The protein graph is first built using structural features to produce node embeddings with dims node_dims, then transformed into hidden_dims after adding sequence information. Therefore the two arguments are somewhat redundant. Note that the edge embeddings are static and are generated with only one vector feature, so anything greater than edge_nv = 1 is redundant.
If adapting one of the two provided models is insufficient, next we describe the building blocks of the protein GNN.
The StructuralFeatures module converts the tensor X of raw backbone coordinates into a proximity graph with structure-based node and edge embeddings described in the paper.
feature_builder = StructuralFeatures(node_dims, edge_dims, top_k=30) # k nearest neighbors
h_V, h_E, E_idx = feature_builder(X, mask) # mask is as described above
h_V is the node embedding tensor with dims [batch, num_nodes, 3*node_nv+node_ns], h_E is the edge embedding tensor with dims [batch, num_nodes, top_k, 3*edge_nv+edge_ns], and E_idx is the tensor of neighbor node indices with dims [batch, num_nodes, top_k].
A MPNNLayer is a single message-passing layer that takes in a tensor of incoming messages h_M from edges and neighboring nodes to update node embeddings. The layer is initialized as follows:
mpnn_layer = MPNNLayer(vec_in, hidden_dim)
Here, vec_in is the number of vector channels in the incoming message message (node_nv + edge_nv). The layer is then used as follows:
h_V = mpnn_layer(h_V, h_M, mask=None)
The optional mask is as described above. It is also possible to use an edgewise mask mask_attend with dims [batch, num_nodes, num_nodes] --- in autoregressive sampling, for example.
Note that while we also use the local node embedding as part of the message, the MPNNLayer itself will perform this concatenation, so you should only pass in the edge embeddings concatenated to the neighbor node embeddings. That is, h_M should have dims [batch, num_nodes, top_k, 3*vec_in+node_ns+edge_ns]. This tensor can be formed as:
h_M = cat_neighbors_nodes(h_V, h_E, E_idx, node_nv, edge_nv)
The Encoder module is a stack of MPNNLayers that performs multiple graph propagation steps directly using the node and edge embeddings h_V and h_E:
encoder = Encoder(node_dims, edge_dims, num_layers)
h_V = encoder(h_V, h_E, E_idx, mask=None)
The Decoder module is similar, except it incorporates sequence information autoregressively as described in the paper. If you are doing something other than autoregressive protein design, Decoder will likely be less useful to you.
While we provide a data pipeline in src/datasets.py, it is specific for the training points/labels in protein design, so you will probably need to write your own for a different application. At minimum, you should modify load_dataset and parse_batch to convert your input representation to the model inputs X, S, and any necessary training labels.
The initial implementation of portions of the protein GNN and the input data pipeline were adapted from Ingraham, et al, NeurIPS 2019.
@inproceedings{
jing2021learning,
title={Learning from Protein Structure with Geometric Vector Perceptrons},
author={Bowen Jing and Stephan Eismann and Patricia Suriana and Raphael John Lamarre Townshend and Ron Dror},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=1YLJDvSx6J4}
}