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توضیحات

Optimized Protein Language Model

ویژگی	مقدار
سیستم عامل	-
نام فایل	ankh-1.0.0
نام	ankh
نسخه کتابخانه	1.0.0
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Ahmed Elnaggar
ایمیل نویسنده	amit.najjar@gmail.com
آدرس صفحه اصلی	https://github.com/agemagician/Ankh
آدرس اینترنتی	https://pypi.org/project/ankh/
مجوز	CC-BY-NC-SA-4.0

<h1 align="center">Ankh ☥: Optimized Protein Language Model Unlocks General-Purpose Modelling </h1> [Ankh](https://github.com/agemagician/Ankh/) is the first general-purpose protein language model trained on Google's **TPU-V4** surpassing the state-of-the-art performance with dramatically less parameters, promoting accessibility to research innovation via attainable resources. <div align="center"><img width=700 height=500 src="https://github.com/agemagician/Ankh/blob/main/images/logo.jpeg?raw=true"></div> This repository will be updated regulary with **new pre-trained models for proteins** in part of supporting the **biotech** community in revolutinizing protein engineering using AI. Table of Contents ================= * [  Models Availability](#models) * [  Dataset Availability](#datasets) * [  Usage](#usage) * [  Original downstream Predictions](#results) * [  Followup use-cases](#inaction) * [  Comparisons to other tools](#comparison) * [  Community and Contributions](#community) * [  Have a question?](#question) * [  Found a bug?](#bug) * [  Requirements](#requirements) * [  Sponsors](#sponsors) * [  Team](#team) * [  License](#license) * [  Citation](#citation) <a name="models"></a> ##   Models Availability | Model | ankh | HuggingFace | |------------------------------------|-----------------------------------|-----------------------------------------------------------| | Ankh Large | `ankh.load_large_model()` |[Ankh Large](https://huggingface.co/ElnaggarLab/ankh-large)| | Ankh Base | `ankh.load_base_model()` |[Ankh Base](https://huggingface.co/ElnaggarLab/ankh-base) | <a name="datasets"></a> ##   Datasets Availability | Dataset | HuggingFace | | ----------------------------- |---------------------------------------------------------------------------------------------------| | Remote Homology | `load_dataset("proteinea/remote_homology")` | | CASP12 | `load_dataset("proteinea/secondary_structure_prediction", data_files={'test': ['CASP12.csv']})`| | CASP14 | `load_dataset("proteinea/secondary_structure_prediction", data_files={'test': ['CASP14.csv']})`| | CB513 | `load_dataset("proteinea/secondary_structure_prediction", data_files={'test': ['CB513.csv']})` | | TS115 | `load_dataset("proteinea/secondary_structure_prediction", data_files={'test': ['TS115.csv']})` | | DeepLoc | `load_dataset("proteinea/deeploc")` | | Fluorosence | `load_dataset("proteinea/flourosence")` | | Solubility | `load_dataset("proteinea/solubility")` | | Nearest Neighbor Search | `load_dataset("proteinea/nearest_neighbor_search")` | <a name="usage"></a> ##   Usage * Loading pre-trained models: ```python import ankh # To load large model: model, tokenizer = ankh.load_large_model() model.eval() # To load base model. model, tokenizer = ankh.load_base_model() model.eval() ``` * Feature extraction using ankh large example: ```python model, tokenizer = ankh.load_large_model() model.eval() protein_sequences = ['MKALCLLLLPVLGLLVSSKTLCSMEEAINERIQEVAGSLIFRAISSIGLECQSVTSRGDLATCPRGFAVTGCTCGSACGSWDVRAETTCHCQCAGMDWTGARCCRVQPLEHHHHHH', 'GSHMSLFDFFKNKGSAATATDRLKLILAKERTLNLPYMEEMRKEIIAVIQKYTKSSDIHFKTLDSNQSVETIEVEIILPR'] protein_sequences = [list(seq) for seq in protein_sequences] ids = tokenizer.batch_encode_plus(protein_sequences, add_special_tokens=True, padding=True, is_split_into_words=True, return_tensors="pt")['input_ids'] with torch.no_grad(): embeddings = model(input_ids=ids) ``` * Loading downstream models example: ```python # To use downstream model for binary classification: binary_classification_model = ankh.ConvBertForBinaryClassification(input_dim=768, nhead=4, hidden_dim=384, num_hidden_layers=1, num_layers=1, kernel_size=7, dropout=0.2, pooling='max') # To use downstream model for multiclass classification: multiclass_classification_model = ankh.ConvBertForMultiClassClassification(num_tokens=2, input_dim=768, nhead=4, hidden_dim=384, num_hidden_layers=1, num_layers=1, kernel_size=7, dropout=0.2) # To use downstream model for regression: # training_labels_mean is optional parameter and it's used to fill the output layer's bias with it, # it's useful for faster convergence. regression_model = ankh.ConvBertForRegression(input_dim=768, nhead=4, hidden_dim=384, num_hidden_layers=1, num_layers=1, kernel_size=7, dropout=0, pooling='max', training_labels_mean=0.38145) ``` <a name="results"></a> ##   Original downstream Predictions <a name="q3"></a> *   Secondary Structure Prediction (Q3): | Model | CASP12 | CASP14 (HARD) | TS115 | CB513 | |--------------------------|:----------------:|:-------------:|:-------------:|:------------:| |**Ankh Large** | 83.59% | 77.48% | 88.22% | 88.48% | |Ankh Base | 80.81% | 76.67% | 86.92% | 86.94% | |ProtT5-XL-UniRef50 | 83.34% | 75.09% | 86.82% | 86.64% | |ESM2-15B | 83.16% | 76.56% | 87.50% | 87.35% | |ESM2-3B | 83.14% | 76.75% | 87.50% | 87.44% | |ESM2-650M | 82.43% | 76.97% | 87.22% | 87.18% | |ESM-1b | 79.45% | 75.39% | 85.02% | 84.31% | <a name="q8"></a> *   Secondary Structure Prediction (Q8): | Model | CASP12 | CASP14 (HARD) | TS115 | CB513 | |--------------------------|:----------------:|:-------------:|:-------------:|:------------:| |**Ankh Large** | 71.69% | 63.17% | 79.10% | 78.45% | |Ankh Base | 68.85% | 62.33% | 77.08% | 75.83% | |ProtT5-XL-UniRef50 | 70.47% | 59.71% | 76.91% | 74.81% | |ESM2-15B | 71.17% | 61.81% | 77.67% | 75.88% | |ESM2-3B | 71.69% | 61.52% | 77.62% | 75.95% | |ESM2-650M | 70.50% | 62.10% | 77.68% | 75.89% | |ESM-1b | 66.02% | 60.34% | 73.82% | 71.55% | <a name="CP"></a> *   Contact Prediction Long Precision Using Embeddings: | Model | ProteinNet (L/1) | ProteinNet (L/5) | CASP14 (L/1) | CASP14 (L/5) | |--------------------------|:----------------:|:----------------:|:-------------:|:------------:| |**Ankh Large** | 48.93% | 73.49% | 16.01% | 29.91% | |Ankh Base | 43.21% | 66.63% | 13.50% | 28.65% | |ProtT5-XL-UniRef50 | 44.74% | 68.95% | 11.95% | 24.45% | |ESM2-15B | 31.62% | 52.97% | 14.44% | 26.61% | |ESM2-3B | 30.24% | 51.34% | 12.20% | 21.91% | |ESM2-650M | 29.36% | 50.74% | 13.71% | 22.25% | |ESM-1b | 29.25% | 50.69% | 10.18% | 18.08% | <a name="CP"></a> *   Contact Prediction Long Precision Using attention scores: | Model | ProteinNet (L/1) | ProteinNet (L/5) | CASP14 (L/1) | CASP14 (L/5) | |--------------------------|:----------------:|:----------------:|:-------------:|:------------:| |**Ankh Large** | 31.44% | 55.58% | 11.05% | 20.74% | |Ankh Base | 25.93% | 46.28% | 9.32% | 19.51% | |ProtT5-XL-UniRef50 | 30.85% | 51.90% | 8.60% | 16.09% | |ESM2-15B | 33.32% | 57.44% | 12.25% | 24.60% | |ESM2-3B | 33.92% | 56.63% | 12.17% | 21.36% | |ESM2-650M | 31.87% | 54.63% | 10.66% | 21.01% | |ESM-1b | 25.30% | 42.03% | 7.77% | 15.77% | <a name="Loc"></a> *   Localization (Q10): | Model | DeepLoc Dataset | |--------------------------|:----------------:| |**Ankh Large** | 83.01% | |Ankh Base | 81.38% | |ProtT5-XL-UniRef50 | 82.95% | |ESM2-15B | 81.22% | |ESM2-3B | 81.22% | |ESM2-650M | 82.08% | |ESM-1b | 80.51% | <a name="RH"></a> *   Remote Homology: | Model | SCOPe (Fold) | |--------------------------|:----------------:| |Ankh Large | 61.01% | |**Ankh Base** | 61.14% | |ProtT5-XL-UniRef50 | 59.38% | |ESM2-15B | 54.48% | |ESM2-3B | 59.24% | |ESM2-650M | 51.36% | |ESM-1b | 56.93% | <a name="Sol"></a> *   Solubility: | Model | Solubility | |--------------------------|:----------------:| |**Ankh Large** | 76.41% | |Ankh Base | 76.36% | |ProtT5-XL-UniRef50 | 76.26% | |ESM2-15B | 60.52% | |ESM2-3B | 74.91% | |ESM2-650M | 74.56% | |ESM-1b | 74.91% | <a name="Flu"></a> *   Fluorosence (Spearman Correlation): | Model | Fluorosence | |--------------------------|:----------------:| |**Ankh Large** | 0.62 | |Ankh Base | 0.62 | |ProtT5-XL-UniRef50 | 0.61 | |ESM2-15B | 0.56 | |ESM-1b | 0.48 | |ESM2-650M | 0.48 | |ESM2-3B | 0.46 | <a name="CATH"></a> *   Nearest Neighbor Search using Global Pooling: | Model | Lookup69K (C) | Lookup69K (A) | Lookup69K (T) | Lookup69K (H) | |--------------------------|:----------------:|:----------------:|:----------------:|:----------------:| |Ankh Large | 0.83 | 0.72 | 0.60 | 0.70 | |**Ankh Base** | 0.85 | 0.77 | 0.63 | 0.72 | |ProtT5-XL-UniRef50 | 0.83 | 0.69 | 0.57 | 0.73 | |ESM2-15B | 0.78 | 0.63 | 0.52 | 0.67 | |ESM2-3B | 0.79 | 0.65 | 0.53 | 0.64 | |ESM2-650M | 0.72 | 0.56 | 0.40 | 0.53 | |ESM-1b | 0.78 | 0.65 | 0.51 | 0.63 | <a name="team"></a> ##   Team * Technical University of Munich: | [Ahmed Elnaggar](https://github.com/agemagician) | Burkhard Rost | |:------------------------------------------------:|:-------------------------:| | <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/AhmedElNaggar.jpg?raw=true"> | <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/Rost.jpg?raw=true"> | * Proteinea: | [Hazem Essam](https://github.com/hazemessamm) | [Wafaa Ashraf](https://github.com/wafaaashraf) | [Walid Moustafa](https://github.com/wmustafaawad) | [Mohamed Elkerdawy](https://github.com/melkerdawy) | |:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:| | <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/HazemEssam.jpeg?raw=true"> | <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/WafaaAshraf.jpeg?raw=true"> | <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/WalidMoustafa.jpg?raw=true"> | <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/MohamedElKerdawy.jpeg?raw=true"> | * University of Columbia: | [Charlotte Rochereau](https://github.com/crochereau) | |:----------------------------------------------------:| | <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/CharlotteRochereau.jpg?raw=true"> | <a name="sponsors"></a> ##   Sponsors | Google Cloud | :------------------------------------------------------------------------------------------------------------------------:| <img width=120 src="https://github.com/agemagician/Ankh/blob/main/images/google_cloud_logo.jpg?raw=true"> | <a name="license"></a> ##   License Ankh pretrained models are released under the under terms of the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by/4.0/). <a name="community"></a> ##   Community and Contributions The Ankh project is a **open source project** supported by various partner companies and research institutions. We are committed to **share all our pre-trained models and knowledge**. We are more than happy if you could help us on sharing new ptrained models, fixing bugs, proposing new feature, improving our documentation, spreading the word, or support our project. <a name="question"></a> ##   Have a question? We are happy to hear your question in our issues page [Ankh](https://github.com/agemagician/Ankh/issues)! Obviously if you have a private question or want to cooperate with us, you can always **reach out to us directly** via [Hello](mailto:hello@proteinea.com?subject=[GitHub]Ankh). <a name="bug"></a> ##   Found a bug? Feel free to **file a new issue** with a respective title and description on the the [Ankh](https://github.com/agemagician/Ankh/issues) repository. If you already found a solution to your problem, **we would love to review your pull request**!.

نیازمندی

مقدار	نام
>=1.80,<2.0	biopython
>=2.7.1,<3.0.0	datasets
>=0.1.97,<0.2.0	sentencepiece
>=4.25.1,<5.0.0	transformers

زبان مورد نیاز

مقدار	نام
>=3.7,<4.0	Python

نحوه نصب

نصب پکیج whl ankh-1.0.0:

pip install ankh-1.0.0.whl

نصب پکیج tar.gz ankh-1.0.0:

pip install ankh-1.0.0.tar.gz