diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/Dyna-1/LICENSE.txt b/Dyna-1/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..99e3444b5df8c6ecf5b503879464df92b0069f6b
--- /dev/null
+++ b/Dyna-1/LICENSE.txt
@@ -0,0 +1,73 @@
+Non-Commercial License Agreement.
+
+Copyright (c) 2025 Brandeis University, Gina El Nesr, Hannah Wayment-Steele.
+
+IMPORTANT: PLEASE READ THIS NON-COMMERCIAL LICENSE AGREEMENT ("AGREEMENT") CAREFULLY BEFORE USING THE SOFTWARE. BY DOWNLOADING, INSTALLING, OR USING THE SOFTWARE, YOU (THE "LICENSEE") AGREE TO BE BOUND BY THE TERMS AND CONDITIONS OF THIS AGREEMENT. IF YOU DO NOT AGREE TO THE TERMS AND CONDITIONS OF THIS AGREEMENT, DO NOT DOWNLOAD, INSTALL, OR USE THE SOFTWARE.
+
+WHEREAS, Brandeis University (the "Licensor") are the licensor of the Dyna-1 model (the "Model");
+
+DEFINITIONS, in addition to other terms defined elsewhere have the following meanings:
+
+THE MODEL (the "Model" or the "Software") means the Dyna-1 released model as a combination of the Dyna-1 Model Code and the Dyna-1 Model Weights and all software, algorithms, machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code, and other elements of the foregoing distributed and made available by the Licensor on the GitHub Page (https://github.com/WaymentSteeleLab/Dyna-1) or other code expressly listed within the GitHub Page including but not limited to Google Colab Notebooks, each as may be updated and amended from time to time, whether in source or object form. Any instances of any source code or weights that depend explicitly on ESM-3 Models are licensed under the EvolutionaryScale Cambrian Non-Commercial License Agreement and subject to the terms that are more restrictive.
+
+COMMERCIAL USE means any use or activity intended for or directed towards commercial advantage or monetary compensation, including without limitation, the development of any product, method, or service intended to be sold or made available for a fee.
+
+COMMERCIAL ENTITY means any individual, entity, or organization involved in any capacity in Commercial Use. For the purposes of this Agreement, references to a Commercial Entity expressly exclude any universities, non-profit organizations, not-for-profit entities, research institutions, educational bodies, and government bodies.
+
+NON-COMMERCIAL USE means use not leading to or directed towards commercial advantage or monetary compensations, or the facilitation of development of any product, method, or service to be sold or made available for a fee. Non-commercial use excludes any activities conducted by commercial entities, even if those activities themselves do not directly generate revenue. Examples of non-commercial use include academic research, personal projects, and use by non-profit organizations.
+
+DERIVATIVE WORKS means any work, in source or object form, that is based on or derived from the Model.
+
+OUTPUT DERIVATIVES means any outputs resulting from use of the Model. This includes, but is not limited to, any predictions that result from the Model.
+
+NOW, THEREFORE, the Licensor and the Licensee hereto agree as follows:
+
+GRANT OF LICENSE.
+
+ Non-Commercial Use. The Model is provided under the MIT License for non-commercial use. The MIT License is included below.
+
+ Commercial Use. If the Licensee is or represents a Commercial Entity and wishes to use the Model for Commercial Use, the Licensee must first contact the Licensor for a commercial license. Examples of Commercial Use include but are not limited to the:
+
+ Selling the Model, either as is or as part of a larger software package;
+
+ Using the Model as an interface or component of a service where customers pay to use it;
+
+ Providing the Model, either as is or as part of a larger software package;
+
+ Incorporating the Model, in part or in whole, into a any commercial operation or workflow that may lead to commercial use of a product, method, or service;
+
+ Use of any derivative works or output derivatives towards any commercial operation or workflow that may lead to commercial use.
+
+ RESTRICTIONS. The Licensee shall not:
+
+ Use, modify, or distribute the Model for commercial purposes without receiving a commercial license.
+
+ Sublicense, assign, or transfer the Model without Licensor's prior written consent.
+
+ Use the Model in any manner that infringes upon any third-party rights.
+
+ Use the Model in any way that is not expressly permitted by this Agreement.
+
+OWNERSHIP. Licensor retains all right, title, and interest in and to the Model, including all intellectual property rights therein. No rights are granted to Licensee other than those expressly set forth in this Agreement.
+
+TERM AND TERMINATION. This Agreement shall commence upon Licensee's initial use of the Model and continue unless terminated by either party upon providing written notice to the other party. Upon termination, Licensee shall immediately cease using the Model and destroy all copies thereof.
+
+LIABILITY. Licensor shall not be liable for any direct, indirect, incidental, special, consequential, or exemplary damages, including but not limited to damages for loss of profits, goodwill, use, data, or other intangible losses, resulting from the use of the Model.
+
+GOVERNING LAW. This Agreement shall be governed by and construed in accordance with the laws of the United States of America.
+
+MIT LICENSE
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+By using the Software, Licensee acknowledges that they have read this Agreement, understood it, and agreed to be bound by its terms and conditions.
+
+CONTACT INFORMATION.
+For commercial license inquiries, please contact: Brandeis University Office of Technology Licensing at otl@brandeis.edu
+For technical inquiries, please contact: gina.elnesr@me.com, hannah.wayment.steele@gmail.com
+
+[END OF AGREEMENT]
diff --git a/Dyna-1/README.md b/Dyna-1/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..153a7a6474341868526519478fc6645be269ac25
--- /dev/null
+++ b/Dyna-1/README.md
@@ -0,0 +1,115 @@
+# Dyna-1
+[](https://python.org/downloads)
+[](https://colab.research.google.com/github/WaymentSteeleLab/Dyna-1/blob/main/colab/Dyna_1.ipynb)
+
+
+
+Dyna-1 is a model introduced in our paper, ["Learning millisecond protein dynamics from what is missing in NMR spectra"](https://www.biorxiv.org/content/10.1101/2025.03.19.642801v1).
+
+Given a sequence and/or structure, Dyna-1 will predict the probability that each residue experiences micro-millisecond motions.
+
+Dyna-1 was achieved using the `esm3-sm-open-v1` weights from ESM-3. Inference with this model is subject to the EvolutionaryScale Cambrian Non-Commercial License Agreement of the ESM-3 Model and requires read permission of the weights found [here](https://huggingface.co/EvolutionaryScale/esm3-sm-open-v1). We also make available an alternate version of Dyna-1 that uses ESM-2 embeddings; use of this model is subject to a Non-Commercial License Agreement.
+
+To make Dyna-1 readily accessible for research purposes, we also provide a [Google Colab](https://colab.research.google.com/github/WaymentSteeleLab/Dyna-1/blob/main/colab/Dyna_1.ipynb).
+
+We provide the curated datasets used to evaluate Dyna-1: 133 curated R1/R2/NOE datasets "RelaxDB" and 10 relaxation-dispersion Carr-Purcell-Meiboom-Gill datasets "RelaxDB-CPMG".
+
+In this repository we provide:
+* [Installation](#installation)
+* [Inference code for Dyna-1](#inference)
+* [PyMol visualization](#visualization)
+* [RelaxDB and RelaxDB-CPMG datasets](#datasets)
+* [Training code for Dyna-1](#training)
+* [Citation](#citation)
+* [Acknowledgements](#acknowledgement)
+
+If you have any questions not covered here, please create an issue and we will get back to you ASAP.
+
+# Installation
+To run the scripts in this repository, we recommend using a conda environment. First, clone this repository. Then navigate to the root directory and run:
+```
+conda create -n dyna1 python=3.11
+conda activate dyna1
+```
+This package requires PyTorch, ideally with GPU support. For more information, follow instructions from https://pytorch.org/get-started/locally/ for your system and CUDA version. We used PyTorch 2.5.0 with CUDA 12.4. To install all of the requirements:
+```
+pip install -r requirements.txt
+```
+Then, download the model weights and upload them to the `model/weights` folder. The weights can be found on ð€HuggingFace at gelnesr/Dyna-1. More information on how to download them can be found here.
+
+# Inference
+
+The best-performing Dyna-1 is based on ESM-3. To run this version, you will have to request access to the ESM-3 `esm3-sm-open-v1` weights at HuggingFace [here](https://huggingface.co/EvolutionaryScale/esm3-sm-open-v1). Follow the steps to agree to their License terms and receive your access token to the model weights.
+
+> [!NOTE]
+> If this is your first time requesting access to the ESM-3 weights, you may need to set up your access token. For more information on how to set up an SSH token, please consult this tutorial. Alternatively, you can use the huggingface login prompt, which will prompt you for the access token each time you re-instantiate Dyna-1. This can be configured by adding the following code to the inference script: `from huggingface_hub import login; login()`
+
+To run inference using our best-performing model, run:
+
+```
+python dyna1.py --pdb --chain --name --use_pdb_seq --write_to_pdb
+```
+
+We provide three options for running inference: sequence and structure input (best performance!), sequence only, or structure only. Additionally, we make it possible to test different sequences for the same backbone. Examples on how to run each of these modes can be found in the `scripts/` folder.
+
+To output the probabilities to the input structure, make sure to pass the `--write_to_pdb` flag.
+
+Alternatively, we also provide a version of Dyna-1 based on ESM-2. To run inference using this version of the model, run:
+
+```
+python dyna1-esm2.py --sequence --name
+```
+
+# Visualization
+
+We visualize probabilities of exchange on protein structures using [PyMol](https://www.pymol.org). To re-create the putty visualization on your protein, import the pdb file into PyMol and copy-paste the following commands into the PyMol command line:
+
+```
+cartoon putty; set cartoon_putty_transform, 6; set cartoon_putty_radius, 0.25; set cartoon_putty_range, 0.1; set cartoon_putty_scale_max, 10
+```
+
+Annotated:
+```
+cartoon putty
+set cartoon_putty_transform, 6 #scaled linear transformation
+set cartoon_putty_radius, 0.25 # min radius for p=0
+set cartoon_putty_range, 0.1 # min_radius / max_radius, sets max_radius=2.5
+set cartoon_putty_scale_max, 10 #max_radius / min_radius
+```
+
+# Datasets
+
+*RelaxDB* contains 133 R1/R2/NOE datasets curated from the [BMRB](https://bmrb.io/) and from literature.
+
+*RelaxDB-CPMG* contains motion labels derived from 10 CPMG relaxation-dispersion datasets curated from literature.
+
+These datasets are made available on ð€HuggingFace at datasets/gelnesr/RelaxDB.
+
+In this repo, you can find:
+- data formatted for input into Dyna-1 is in `data/RelaxDB_pkls_22jan2025.zip`
+- datasets in json format is in `data/RelaxDB_datasets/`
+- demo notebooks for demo notebooks for visualizing and using datasets to evaluate model outputs in `analysis/`
+
+# Training
+
+Training code will be made available upon journal publication.
+
+# Citation
+
+If you are using our code, datasets, or model, please use the following citation:
+```bibtex
+@article {Dyna-1,
+ author = {Wayment-Steele, Hannah K. and El Nesr, Gina and Hettiarachchi, Ramith and Kariyawasam, Hasindu and Ovchinnikov, Sergey and Kern, Dorothee},
+ title = {Learning millisecond protein dynamics from what is missing in NMR spectra},
+ year = {2025},
+ doi = {10.1101/2025.03.19.642801},
+ journal = {bioRxiv}
+}
+```
+# Acknowledgements
+
+We would like to acknowledge the Evolutionary Scale Team for their contributions to the field with ESM-3. The code in `esm` is imported from `evolutionaryscale/esm` with all modifications identified and includes the associated LICENSE terms for the ESM-3 model.
+
+We would also like to acknowledge the FAIR Team for their contributions to the field with ESM-2. The ESM-2 model is called using the HuggingFace API call.
+
+We thank Katie Henzler-Wildman, Magnus Wolf-Watz, Elan Eisenmesser, J. Patrick Loria, Marcellus Ubbelink, George Lisi, Sam Butcher, and Nicolas Doucet for sharing data. We thank Martin Stone for sharing the Indiana Dynamics Database data his group curated in 2000.
diff --git a/Dyna-1/configs/af2.yml b/Dyna-1/configs/af2.yml
new file mode 100644
index 0000000000000000000000000000000000000000..7b8edaced1f5834733d908d7ce0c7f45d653fa3f
--- /dev/null
+++ b/Dyna-1/configs/af2.yml
@@ -0,0 +1,31 @@
+model_name: 'af2'
+train:
+ epochs: 500
+ batchsize: 4
+ accum_steps: 4
+ lr: 0.00001
+ dropout: 0.1
+ num_workers: 1
+model:
+ hidden_size: 128
+ res_count: 32
+ length: 400
+ nheads: 8
+ nlayers: 12
+dir:
+ save_dir: '/path/to/save/dir'
+data:
+ pair_rep: None
+ sample_clusters: True
+ relaxdb:
+ split: 'relaxdb'
+ crop_len: 400
+ type: 'rex'
+ cpmg:
+ split: 'relaxdb-cpmg'
+ crop_len: 400
+ type: 'cpmg'
+wandb:
+ project: "New-Project"
+ team: 'my-project-team'
+ dir: '/wandb/log/dir'
diff --git a/Dyna-1/configs/baseline.yml b/Dyna-1/configs/baseline.yml
new file mode 100644
index 0000000000000000000000000000000000000000..264b67170e7cd2e147144cf42c2180d48b1c10b6
--- /dev/null
+++ b/Dyna-1/configs/baseline.yml
@@ -0,0 +1,28 @@
+model_name: 'baseline'
+train:
+ epochs: 500
+ batchsize: 16
+ accum_steps: 1
+ lr: 0.000001
+ dropout: 0.1
+ num_workers: 2
+model:
+ nheads: 6
+ nlayers: 10
+dir:
+ save_dir: '/path/to/save/dir'
+data:
+ pair_rep: None
+ sample_clusters: True
+ relaxdb:
+ split: 'relaxdb'
+ crop_len: 367
+ type: 'rex'
+ cpmg:
+ split: 'relaxdb-cpmg'
+ crop_len: 367
+ type: 'cpmg'
+wandb:
+ project: "New-Project"
+ team: 'my-project-team'
+ dir: '/wandb/log/dir'
diff --git a/Dyna-1/configs/esm2.yml b/Dyna-1/configs/esm2.yml
new file mode 100644
index 0000000000000000000000000000000000000000..29834523c963656e6afe6f62b7ec7c2a98e50c77
--- /dev/null
+++ b/Dyna-1/configs/esm2.yml
@@ -0,0 +1,28 @@
+model_name: 'esm2_t30_150M_UR50D'
+train:
+ epochs: 500
+ batchsize: 16
+ accum_steps: 1
+ lr: 0.000001
+ dropout: 0.1
+ num_workers: 2
+model:
+ nheads: 8
+ nlayers: 12
+dir:
+ save_dir: '/path/to/save/dir'
+data:
+ pair_rep: None
+ sample_clusters: True
+ relaxdb:
+ split: 'relaxdb'
+ crop_len: 367
+ type: 'rex'
+ cpmg:
+ split: 'relaxdb-cpmg'
+ crop_len: 367
+ type: 'cpmg'
+wandb:
+ project: "New-Project"
+ team: 'my-project-team'
+ dir: '/wandb/log/dir'
\ No newline at end of file
diff --git a/Dyna-1/configs/esm3.yml b/Dyna-1/configs/esm3.yml
new file mode 100644
index 0000000000000000000000000000000000000000..02b8ed7f9893136f8f20d506e94a854cda5dc460
--- /dev/null
+++ b/Dyna-1/configs/esm3.yml
@@ -0,0 +1,28 @@
+model_name: 'esm3'
+train:
+ epochs: 500
+ batchsize: 16
+ accum_steps: 1
+ lr: 0.000001
+ dropout: 0.1
+ num_workers: 2
+model:
+ nheads: 6
+ nlayers: 12
+dir:
+ save_dir: '/path/to/save/dir'
+data:
+ pair_rep: None
+ sample_clusters: True
+ relaxdb:
+ split: 'relaxdb'
+ crop_len: 367
+ type: 'rex'
+ cpmg:
+ split: 'relaxdb-cpmg'
+ crop_len: 367
+ type: 'cpmg'
+wandb:
+ project: "New-Project"
+ team: 'my-project-team'
+ dir: '/wandb/log/dir'
\ No newline at end of file
diff --git a/Dyna-1/data/dataloader.py b/Dyna-1/data/dataloader.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b411ec9ac9a0997db5254b2c3b40d2369095472
--- /dev/null
+++ b/Dyna-1/data/dataloader.py
@@ -0,0 +1,341 @@
+import numpy as np
+import warnings
+import random
+import torch
+import os
+import pickle
+import data.vocab as vocab
+import pandas as pd
+from typing import Tuple, List, Any
+from esm.sdk.api import ESMProtein
+from transformers import AutoTokenizer
+import utils
+
+class DynaData(torch.utils.data.Dataset):
+ """
+ For each protein, we use a pkl file that contains:
+ seq: The domain sequence, stored as an L-length string
+ assns: string containing labels of dynamics type
+ """
+
+ def __init__(
+ self,
+ split,
+ type = 'missing',
+ sample_clusters = False,
+ cluster_file = None,
+ crop_len = 300,
+ missing_only = False,
+ rex_only = False,
+ unsuppressed = False,
+ method = None,
+ pair_rep = None,
+ return_dssp = False
+ ):
+ super().__init__()
+
+ self.return_dssp = return_dssp
+ self.crop_len = crop_len
+ self.sample_clusters = sample_clusters
+ self.label_tokenizer = vocab.label_tokenizer(type = type,
+ missing_only = missing_only,
+ rex_only = rex_only,
+ unsuppressed = unsuppressed)
+
+ # tokenization is the same for all ESM models, use the lightest one
+ self.tokenizer = AutoTokenizer.from_pretrained(f"facebook/esm2_t6_8M_UR50D")
+ self.proline = self.tokenizer.get_vocab()['P']
+
+ self.method = method[0]
+ self.model = method[1]
+
+ if isinstance(split, str):
+ split = [split]
+
+ self.all_names, self.names = [], []
+
+ # read in all pdb names
+ for fil in split:
+ filename = f'data/split_files/{fil}.txt'
+ with open(filename,'r') as f:
+ self.all_names.extend(f.read().splitlines())
+
+ # set up cluster sampling
+ if self.sample_clusters:
+ self.cluster_info = pd.read_csv(f'data/{cluster_file}.tsv', sep='\t')
+ self.cluster_info['cluster'] = self.cluster_info.apply(lambda row: row['cluster'], axis=1)
+ for nm in self.all_names:
+ subset = self.cluster_info.loc[self.cluster_info.entry_ID==nm]
+ if len(subset) == 0:
+ print('NO!', nm)
+ cluster_ind = subset['cluster'].iloc[0]
+ if cluster_ind not in self.names:
+ self.names.append(cluster_ind)
+ else:
+ self.names = self.all_names
+
+ self.pair_rep_dir = pair_rep
+
+ def __len__(self):
+ return len(self.names)
+
+ def __baseline_get_item__(self, name, obj, crop_start):
+ if crop_start > -1:
+ sequence = obj['sequence'][crop_start:crop_start+self.crop_len]
+ else:
+ sequence = obj['sequence'][:self.crop_len]
+
+ sequence_tokens = self.tokenizer.encode(sequence,
+ add_special_tokens=False,
+ padding='max_length',
+ max_length=self.crop_len,
+ return_tensors='np').squeeze()
+
+ # set mask to 1 for length of seq, padded tokens then are 0
+ eval_mask = np.zeros_like(sequence_tokens)
+ eval_mask[:len(sequence)] = 1
+
+ sequence_id = sequence_tokens != 0
+
+ # mask prolines in eval
+ eval_mask[sequence_tokens==self.proline] = 0
+
+ return sequence_tokens, sequence_id, eval_mask
+
+ def __af2_get_item__(self, name, obj, crop_start):
+ """
+ Prepares input for the AF2-pair model
+ """
+
+ pair_rep = np.load(f"{self.pair_rep_dir}/{name}.npy")
+ labels, seq = obj['label'], obj['sequence']
+
+ if crop_start > -1:
+ pair_rep = pair_rep[crop_start:crop_start+self.crop_len,
+ crop_start:crop_start+self.crop_len, :]
+ labels = labels[crop_start:crop_start+self.crop_len]
+ seq = seq[crop_start:crop_start+self.crop_len]
+
+ eval_mask = np.zeros((pair_rep.shape[0],))
+
+ prolines = [i for i, aa in enumerate(seq) if aa == 'P']
+ eval_mask[:len(labels)] = 1
+
+ sequence_id = eval_mask != 0
+ eval_mask[prolines] = 0
+ x = pair_rep.shape[0]
+
+ eval_mask = np.pad(eval_mask, (0, self.crop_len - len(eval_mask)), mode='constant')
+ sequence_id = np.pad(sequence_id, (0, self.crop_len - len(sequence_id)), mode='constant')
+ if x <