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semantic-protein-folding

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Experiments in protein folding through language modeling

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# Semantic Protein Folding

## A pipeline for Protein Language Modeling + Protein Folding experimentation

Based on Distillation of MSA Embeddings to Protein Folded Structures ([biorxiv preprint](https://www.biorxiv.org/content/10.1101/2021.06.02.446809v1.full.pdf) and [full latest text](https://www.media.mit.edu/publications/distillation-of-language-model-semantics-to-folded-three-dimensional-protein-structures/)).

This repository stands on shoulders of giant work by the scientific community:

- [Evolutionary Scale Modeling](https://github.com/facebookresearch/esm)
- [SidechainNet](https://github.com/jonathanking/sidechainnet)
- [Massively Parallel Natural Extension of Reference Frame](https://github.com/EleutherAI/mp_nerf)
- [Axial-Attention](https://github.com/lucidrains/En-transformer/)
- [En-Transformer](https://github.com/lucidrains/En-transformer/)
- [Graph-Transformer](https://github.com/lucidrains/graph-transformer-pytorch)
- [E3NN](https://github.com/e3nn/e3nn)

For experimental and prototypical access to internal code, these repos are collected under `building_blocks` (except sidechainnet). As development progresses they will be incorporated as original `imports`.

## Experimentation Pipeline

#### General
- `debug`: whether to use wandb logging
- `submit`: submit training to a SLURM scheduler
- `name`: name of experiment
- `note`: experimental note
- `report_frequency`: how often to log metrics in log file

#### Data
- `dataset_source`: path to sidechainnet-formatted dataset
- `downsample`: whether to uniformly at random downsample dataset
- `seq_clamp`: clamp data at the sequence level, size of clamp
- `max_seq_len`: throw out data with sequences larger than max_seq_len
- `num_workers`: number of CPU workers for data fetching and loading
- `batch_size`: batch size

### Architecture

- `wipe_edge_information`: drops out all hij
- `topography_giveaway`: instead of providing language-model-based hij, produces hij based on ground truth distance and orientation
- `giveaway_distance_resolution`: number of bins of relative distance information to input
- `giveaway_angle_resolution`: number of bins of relative orientation information to input

- `wiring_checkpoint`: checkpoint model-inbetween Dense networks

- `use_msa`: use ESM-MSA-1 embeddings
- `use_seq`: use ESM-1b embeddings

- `use_at`: process hij with an Axial Transformer after distillation
- `use_gt`: project 3D coordinates with Graph Transformer after distillation
- `use_en`: refine with E(n)-Transformer given coords

#### ESM-MSA-1 Distillation
- `node_msa_distill_layers`: hidden layer enumeration of Dense for msa node information extraction [768, 256, 256, 128]
- `edge_msa_distill_layers`: hidden layer enumeration of Dense for msa edge information extraction [96, 64, 64]

#### ESM-1B Distillation
- `node_seq_distill_layers`: hidden layer enumeration of Dense for msa node information extraction [1280, 256, 128]
- `edge_seq_distill_layers`: hidden layer enumeration of Dense for msa edge information extraction [160, 64, 64]

#### Seq + MSA ensemble
- `node_ens_distill_layers`: hidden layer enumeration of Dense for msa node information extraction [128, 128, 128]
- `edge_ens_distill_layers`: hidden layer enumeration of Dense for msa edge information extraction [64, 64]

#### AXIAL TRANSFORMER
- `at_checkpoint`: if the axial transformer should be checkpointed
- `at_dim`: axial transformer dim
- `at_depth`: axial transformer depth
- `at_heads`: axial transformer number of attention heads
- `at_dim_head`: axial transformer dim head
- `at_window_size`: axial transformer window size (for internal Long-Short optimization)

#### GRAPH TRANSFORMER
- `gt_checkpoint`: graph transformer checkpoint
- `gt_dim`: graph transformer dim
- `gt_edim`: graph transformer edge dim
- `gt_depth`: graph transformer depth
- `gt_heads`: graph transformer number of heads
- `gt_dim_head`: graph trnasformer dim head

#### EN TRANSFORMER
- `gaussian_noise`: if graph transformer is not used, which gaussian noise to be added to backbone as starting point
- `et_checkpoint`: checkpoint en transformer
- `et_dim`: dim of en transformer
- `et_edim`: en transformer edge dim
- `et_depth`: en transformer depth
- `et_heads`: en transformer num heads
- `et_dim_head`: en transformer dim head
- `et_coors_hidden_dim`: hidden dim of internal coordinate-head mixer 
- `en_num_neighbors`: num neighbors to consider in 3d space
- `en_num_seq_neighbors`: num neighbors to consider in sequence space

#### FOLDING STEPS
- `unroll_steps` - during training, applies en transformer without gradients up to N, where N ~ U(0, unroll_steps) and each batch gets a different sample
- `train_fold_steps` - during training, how many en transformer iterations to perform with gradients
- `eval_fold_steps` - during testing, how many en trasnformer iterations to perform

#### PREDICTIONS
- `angle_number_of_bins` - number of bins to use for predicting relative orientations
- `distance_number_of_bins` - number of bins to use for predicting relative distances
- `distance_max_radius` - maximum radius for predicitng relative distances

#### OPTIM
- `lr`: learning rate
- `at_loss_coeff`: axial transformer loss coefficient
- `gt_loss_coeff`: graph transformer loss coefficient
- `et_loss_coeff`: en transformer loss coefficient

- `et_drmsd`: use drmsd for en transformer

- `max_epochs`: number of epochs
- `validation_check_rate`: how often to perform validation checks
- `validation_start`: when to start validating

#### STOCHASTICITY
- `coordinate_reset_prob`: legacy, will be removed
- `msa_wipe_out_prob`: probability of selecting MSA embeddings 
- `msa_wipe_out_dropout`: dropout of edge and node information for selected MSA embeddings

### Test, Retrain
- `test_model`: path to model weights for testing
- `retrain_model`: path to model weights for retraining

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