Implementation of Alpha Fold 3 from the paper: "Accurate structure prediction of biomolecular interactions with AlphaFold3" in PyTorch
$ pip install alphafold3
import torch
# Define the batch size, number of nodes, and number of features
batch_size = 1
num_nodes = 5
num_features = 64
# Generate random pair representations using torch.randn
# Shape: (batch_size, num_nodes, num_nodes, num_features)
pair_representations = torch.randn(
batch_size, num_nodes, num_nodes, num_features
)
# Generate random single representations using torch.randn
# Shape: (batch_size, num_nodes, num_features)
single_representations = torch.randn(
batch_size, num_nodes, num_features
)Need review but basically it operates on atomic coordinates.
import torch
from alphafold3.diffusion import GeneticDiffusion
# Create an instance of the GeneticDiffusionModuleBlock
model = GeneticDiffusion(channels=3, training=True)
# Generate random input coordinates
input_coords = torch.randn(10, 100, 100, 3)
# Generate random ground truth coordinates
ground_truth = torch.randn(10, 100, 100, 3)
# Pass the input coordinates and ground truth coordinates through the model
output_coords, loss = model(input_coords, ground_truth)
# Print the output coordinates
print(output_coords)
# Print the loss value
print(loss)import torch
from alphafold3 import AlphaFold3
# Create random tensors
x = torch.randn(1, 5, 5, 64) # Shape: (batch_size, seq_len, seq_len, dim)
y = torch.randn(1, 5, 64) # Shape: (batch_size, seq_len, dim)
# Initialize AlphaFold3 model
model = AlphaFold3(
dim=64,
seq_len=5,
heads=8,
dim_head=64,
attn_dropout=0.0,
ff_dropout=0.0,
global_column_attn=False,
pair_former_depth=48,
num_diffusion_steps=1000,
diffusion_depth=30,
)
# Forward pass through the model
output = model(x, y)
# Print the shape of the output tensor
print(output.shape)A basic PyTorch image is provided that includes the dependencies to run this code.
## Build the image
docker build -t af3 .
## Run the image (with GPUs)
docker run --gpus all -it af3@article{Abramson2024-fj,
title = "Accurate structure prediction of biomolecular interactions with
{AlphaFold} 3",
author = "Abramson, Josh and Adler, Jonas and Dunger, Jack and Evans,
Richard and Green, Tim and Pritzel, Alexander and Ronneberger,
Olaf and Willmore, Lindsay and Ballard, Andrew J and Bambrick,
Joshua and Bodenstein, Sebastian W and Evans, David A and Hung,
Chia-Chun and O'Neill, Michael and Reiman, David and
Tunyasuvunakool, Kathryn and Wu, Zachary and {\v Z}emgulyt{\.e},
Akvil{\.e} and Arvaniti, Eirini and Beattie, Charles and
Bertolli, Ottavia and Bridgland, Alex and Cherepanov, Alexey and
Congreve, Miles and Cowen-Rivers, Alexander I and Cowie, Andrew
and Figurnov, Michael and Fuchs, Fabian B and Gladman, Hannah and
Jain, Rishub and Khan, Yousuf A and Low, Caroline M R and Perlin,
Kuba and Potapenko, Anna and Savy, Pascal and Singh, Sukhdeep and
Stecula, Adrian and Thillaisundaram, Ashok and Tong, Catherine
and Yakneen, Sergei and Zhong, Ellen D and Zielinski, Michal and
{\v Z}{\'\i}dek, Augustin and Bapst, Victor and Kohli, Pushmeet
and Jaderberg, Max and Hassabis, Demis and Jumper, John M",
journal = "Nature",
month = "May",
year = 2024
}-> pairwise representation -> explicit atomic positions
-> within the trunk, msa processing is de emphasized with a simpler MSA block, 4 blocks
-> msa processing -> pair weighted averaging
-> pairformer: replaces evoformer, operates on pair representation and single representation
-> pairformer 48 blocks
-> pair and single representation together with the input representation are passed to the diffusion module
-> diffusion takes in 3 tensors [pair, single representation, with new pairformer representation]
-> diffusion module operates directory on raw atom coordinates
-> standard diffusion approach, model is trained to receiev noised atomic coordinates then predict the true coordinates
-> the network learns protein structure at a variety of length scales where the denoising task at small noise emphasizes large scale structure of the system.
-> at inference time, random noise is sampled and then recurrently denoised to produce a final structure
-> diffusion module produces a distribution of answers
-> for each answer the local structure will be sharply defined
-> diffusion models are prone to hallucination where the model may hallucinate plausible looking structures
-> to counteract hallucination, they use a novel cross distillation method where they enrich the training data with alphafold multimer v2.3 predicted strutctures.
-> confidence measures predicts the atom level and pairwise errors in final structures, this is done by regressing the error in the outut of the structure mdule in training,
-> Utilizes diffusion rollout procedure for the full structure generation during training ( using a larger step suze than normal)
-> diffused predicted structure is used to permute the ground truth and ligands to compute metrics to train the confidence head.
-> confidence head uses the pairwise representation to predict the lddt (pddt) and a predicted aligned error matrix as used in alphafold 2 as well as distance error matrix which is the error in the distance matrix of the predicted structure as compared to the true structure
-> confidence measures also preduct atom level and pairwise errors
-> early stopping using a weighted average of all above metic
-> af3 can predict srtructures from input polymer sequences, rediue modifications, ligand smiles
-> uses structures below 1000 residues
-> alphafold3 is able to predict protein nuclear structures with thousnads of residues
-> Covalent modifications (bonded ligands, glycosylation, and modified protein residues and 202 nucleic acid bases) are also accurately predicted by AF
-> distills alphafold2 preductions
-> key problem in protein structure prediction is they predict static structures and not the dynamical behavior
-> multiple random seeds for either the diffusion head or network does not product an approximation of the solution ensenble
-> in future: generate large number of predictions and rank them
-> inference: top confidence sample from 5 seed runs and 5 diffusion samples per model seed for a total of 25 samples
-> interface accuracy via interface lddt which is calculated from distances netween atoms across different chains in the interface
-> uses a lddt to polymer metric which considers differences from each atom of a entity to any c or c1 polymer atom within aradius
-
Implement input Embedder from Alphafold2 openfold implementation LINK
-
Implement the template module from openfold LINK
-
Implement the MSA embedding from openfold LINK
-
Fix residuals and make sure pair representation and generated output goes into the diffusion model
-
Implement reclying to fix residuals
- Get all datasets pushed to huggingface
Smaller, start here
- Protein data bank
- Working with pdb data
- PDB ligands
- AlphaFold Protein Structure Database
- Colab notebook for AlphaFold search
- RoseTTAFold(https://www.ipd.uw.edu/2021/07/rosettafold-accurate-protein-structure-prediction-accessible-to-all/0)
@article{Abramson2024-fj,
title = "Accurate structure prediction of biomolecular interactions with
{AlphaFold} 3",
author = "Abramson, Josh and Adler, Jonas and Dunger, Jack and Evans,
Richard and Green, Tim and Pritzel, Alexander and Ronneberger,
Olaf and Willmore, Lindsay and Ballard, Andrew J and Bambrick,
Joshua and Bodenstein, Sebastian W and Evans, David A and Hung,
Chia-Chun and O'Neill, Michael and Reiman, David and
Tunyasuvunakool, Kathryn and Wu, Zachary and {\v Z}emgulyt{\.e},
Akvil{\.e} and Arvaniti, Eirini and Beattie, Charles and
Bertolli, Ottavia and Bridgland, Alex and Cherepanov, Alexey and
Congreve, Miles and Cowen-Rivers, Alexander I and Cowie, Andrew
and Figurnov, Michael and Fuchs, Fabian B and Gladman, Hannah and
Jain, Rishub and Khan, Yousuf A and Low, Caroline M R and Perlin,
Kuba and Potapenko, Anna and Savy, Pascal and Singh, Sukhdeep and
Stecula, Adrian and Thillaisundaram, Ashok and Tong, Catherine
and Yakneen, Sergei and Zhong, Ellen D and Zielinski, Michal and
{\v Z}{\'\i}dek, Augustin and Bapst, Victor and Kohli, Pushmeet
and Jaderberg, Max and Hassabis, Demis and Jumper, John M",
journal = "Nature",
month = "May",
year = 2024
}@inproceedings{Darcet2023VisionTN,
title = {Vision Transformers Need Registers},
author = {Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
year = {2023},
url = {https://api.semanticscholar.org/CorpusID:263134283}
}@article{Arora2024SimpleLA,
title = {Simple linear attention language models balance the recall-throughput tradeoff},
author = {Simran Arora and Sabri Eyuboglu and Michael Zhang and Aman Timalsina and Silas Alberti and Dylan Zinsley and James Zou and Atri Rudra and Christopher R'e},
journal = {ArXiv},
year = {2024},
volume = {abs/2402.18668},
url = {https://api.semanticscholar.org/CorpusID:268063190}
}@article{Puny2021FrameAF,
title = {Frame Averaging for Invariant and Equivariant Network Design},
author = {Omri Puny and Matan Atzmon and Heli Ben-Hamu and Edward James Smith and Ishan Misra and Aditya Grover and Yaron Lipman},
journal = {ArXiv},
year = {2021},
volume = {abs/2110.03336},
url = {https://api.semanticscholar.org/CorpusID:238419638}
}@article{Duval2023FAENetFA,
title = {FAENet: Frame Averaging Equivariant GNN for Materials Modeling},
author = {Alexandre Duval and Victor Schmidt and Alex Hernandez Garcia and Santiago Miret and Fragkiskos D. Malliaros and Yoshua Bengio and David Rolnick},
journal = {ArXiv},
year = {2023},
volume = {abs/2305.05577},
url = {https://api.semanticscholar.org/CorpusID:258564608}
}@article{Wang2022DeepNetST,
title = {DeepNet: Scaling Transformers to 1, 000 Layers},
author = {Hongyu Wang and Shuming Ma and Li Dong and Shaohan Huang and Dongdong Zhang and Furu Wei},
journal = {ArXiv},
year = {2022},
volume = {abs/2203.00555},
url = {https://api.semanticscholar.org/CorpusID:247187905}
}@inproceedings{Ainslie2023CoLT5FL,
title = {CoLT5: Faster Long-Range Transformers with Conditional Computation},
author = {Joshua Ainslie and Tao Lei and Michiel de Jong and Santiago Ontan'on and Siddhartha Brahma and Yury Zemlyanskiy and David Uthus and Mandy Guo and James Lee-Thorp and Yi Tay and Yun-Hsuan Sung and Sumit Sanghai},
year = {2023}
}
