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multitask_MHA_esm2_t30_150M_UR50D_neg_ratio_8+8_shift_30_mask_0.2_2023-03-25_90.txt

@@ -0,0 +1 @@
+https://zenodo.org/record/8170005

phos-ST_Example_Code.ipynb

@@ -0,0 +1,312 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b1b4e507-0ca2-4309-ac5c-0461f99edc72",
+   "metadata": {},
+   "source": [
+    "# Phosformer-ST Example Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a23dd26-2060-4cb1-a1a0-dd97b168a329",
+   "metadata": {},
+   "source": [
+    "## imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec3bd89c-8aa1-408c-b569-c89dc2bb768d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import hashlib\n",
+    "import warnings\n",
+    "sys.dont_write_bytecode=True\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import torch\n",
+    "\n",
+    "from tokenization_esm import EsmTokenizer\n",
+    "from modeling_esm import EsmForSequenceClassificationMHACustom\n",
+    "#for versioning spesfics see ReadMe \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a42e87fc-bd23-4b7b-8234-06cbdcb25bc0",
+   "metadata": {},
+   "source": [
+    "## loading in pre-trained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9def3d4b-822d-44b8-a896-3e6ee5aca13d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dir = 'multitask_MHA_esm2_t30_150M_UR50D_neg_ratio_8+8_shift_30_mask_0.2_2023-03-25_90'\n",
+    "\n",
+    "tokenizer = EsmTokenizer.from_pretrained(model_dir)\n",
+    "model     = EsmForSequenceClassificationMHACustom.from_pretrained(model_dir, num_labels=2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff3f7f18-6cb2-4818-9583-bb729e848b81",
+   "metadata": {},
+   "source": [
+    "## configureing paramaters of the Phos-ST model\n",
+    "\n",
+    "## also orginizing the data for the input into Phos-ST   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1dbece0a-39a3-4932-8781-a679dd699587",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_model(peptides, kinases, model=model, tokenizer=tokenizer, device='cuda', batch_size=50, output_hidden_states=True, output_attentions=True):\n",
+    "    torch.cuda.empty_cache()\n",
+    "    \n",
+    "    model.eval()\n",
+    "    model = model.to(device)\n",
+    "    \n",
+    "    size = len(peptides)\n",
+    "    breaks = set(np.cumsum([batch_size]*(size//batch_size)+[size%batch_size])-1)\n",
+    "\n",
+    "    pairs = []\n",
+    "    for n, pair in enumerate(zip(peptides, kinases)):\n",
+    "        sys.stderr.write(f'{1+n}\\r')\n",
+    "        pairs += [pair]\n",
+    "        if n in breaks:\n",
+    "            \n",
+    "            output = dict(zip(('peptide','kinase'),zip(*pairs)))\n",
+    "            ids = tokenizer(pairs, padding=True, return_tensors='pt')\n",
+    "            ids = ids.to(device)\n",
+    "            \n",
+    "            with torch.no_grad():\n",
+    "                results, classifier_attn_outputs, classifier_attn_output_weights = model(ids['input_ids'], \n",
+    "                                attention_mask=ids['attention_mask'], \n",
+    "                                output_hidden_states=output_hidden_states, \n",
+    "                                output_attentions=output_attentions)\n",
+    "                \n",
+    "                attention_mask = ids['attention_mask'].cpu().type(torch.bool)\n",
+    "\n",
+    "                output['probability'] = results['logits'].softmax(1)[:,1].cpu().numpy()\n",
+    "                \n",
+    "                if output_hidden_states:\n",
+    "                    last_embeddings = results['hidden_states'][-1].cpu().numpy()\n",
+    "                    output['embedding'] = [i[m] for i, m in zip(last_embeddings, attention_mask)]\n",
+    "                    \n",
+    "                if output_attentions:\n",
+    "                    last_attentions = results['attentions'][-1].cpu().numpy()\n",
+    "                    output['attention'] = [i[:,m,:][:,:,m] for i, m in zip(last_attentions, attention_mask)]\n",
+    "                \n",
+    "                classifier_attn_outputs = classifier_attn_outputs.cpu()\n",
+    "                output['classifier_attn_outputs'] = classifier_attn_outputs\n",
+    "\n",
+    "                classifier_attn_output_weights = classifier_attn_output_weights.cpu()\n",
+    "                output['classifier_attn_output_weights'] = [i[:,m[16:]] for i, m in zip(classifier_attn_output_weights, attention_mask)]\n",
+    "                \n",
+    "            keys = output.keys()\n",
+    "            for data in zip(*(output[k] for k in keys)):\n",
+    "                yield dict(zip(keys, data))\n",
+    "            \n",
+    "            pairs = []\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fbfc05d-970c-4db9-bae8-61ea2ffb06af",
+   "metadata": {},
+   "source": [
+    "## helper funtion to use Phos-ST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "98e90bc6-28db-449c-8a0b-805ef22cd9ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this could be modified to take in a list of substrate and kinase domains\n",
+    "# just drop the square brackets on the kinaseDomainSeq variable and substrate15mer variable around the job fuction's 1st and 2nd argument\n",
+    "def phosST(kinaseDomainSeq,substrate15mer):\n",
+    "    job = run_model(\n",
+    "        [substrate15mer],\n",
+    "        [kinaseDomainSeq],\n",
+    "        model=model, \n",
+    "        tokenizer=tokenizer, \n",
+    "        device='cuda', \n",
+    "        batch_size=10,\n",
+    "        output_hidden_states=False,\n",
+    "        output_attentions=False,\n",
+    "    )\n",
+    "    \n",
+    "    #total = dataset.shape[0]\n",
+    "    results = {\n",
+    "        'kinase' : [],\n",
+    "        'peptide' : [],\n",
+    "        'prob' : [],\n",
+    "    }\n",
+    "\n",
+    "    \n",
+    "    for n, i in enumerate(job):\n",
+    "        #sys.stderr.write(f'{n+1} / {total}\\r')\n",
+    "        results['kinase' ] += [i['kinase']]\n",
+    "        results['peptide'] += [i['peptide']]\n",
+    "        results['prob'   ] += [i['probability']]\n",
+    "    \n",
+    "    result = pd.DataFrame(results)\n",
+    "    print(\"The Predictive score is \"+str(i['probability']))\n",
+    "    \n",
+    "    return result\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "151c217b-1ee1-4cf7-b41f-b52f5ce22719",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee511f8b-5d9e-4c8a-8191-bd2a7fd3a5e9",
+   "metadata": {},
+   "source": [
+    "# Postive Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5bd6e8ee-444e-49d5-a617-d2343759759a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# P17612 KAPCA_HUMAN\n",
+    "kinDomain=\"FERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWF\"\n",
+    "# P53602_S96_LARKRRNSRDGDPLP\n",
+    "substrate=\"LARKRRNSRDGDPLP\"\n",
+    "\n",
+    "phosST(kinDomain,substrate).to_csv('PostiveExample.csv')\n",
+    "#the score should be listed in the csv file aswell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1f27f0f-5bda-4107-adef-a8712ace540c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f432840c-f56e-40f2-959f-157dc65f57d6",
+   "metadata": {},
+   "source": [
+    "# Negitive Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41e2c0de-9088-4cf1-a744-a451ce19d7a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# P17612 KAPCA_HUMAN\n",
+    "kinDomain=\"FERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWF\"\n",
+    "# 'Q01831_T169_PVEIEIETPEQAKTR'\n",
+    "substrate=\"PVEIEIETPEQAKTR\"\n",
+    "\n",
+    "phosST(kinDomain,substrate).to_csv('NegitiveExample.csv')\n",
+    "#the score should be listed in the csv file aswell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac3b5b10-3cde-4f66-ba7a-f137538fa880",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85509eea-3217-492f-bf77-9da8ee123b76",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bfe47df-7e6b-487b-92b6-33ba2d9c6eb7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6c2b239-f7d1-418b-bd1d-916fb1db8933",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5af09564-6b23-4dea-a0a3-76bc8362b7b4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

phosST.yml

@@ -0,0 +1,188 @@
+name: phosST
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - anyio=3.6.2=pyhd8ed1ab_0
+  - argon2-cffi=21.3.0=pyhd8ed1ab_0
+  - argon2-cffi-bindings=21.2.0=py39hb9d737c_2
+  - asttokens=2.2.1=pyhd8ed1ab_0
+  - async-lru=2.0.2=pyhd8ed1ab_0
+  - attrs=23.1.0=pyh71513ae_1
+  - babel=2.12.1=pyhd8ed1ab_1
+  - backcall=0.2.0=pyh9f0ad1d_0
+  - backports=1.0=pyhd8ed1ab_3
+  - backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
+  - beautifulsoup4=4.12.2=pyha770c72_0
+  - bleach=6.0.0=pyhd8ed1ab_0
+  - brotli=1.0.9=h166bdaf_7
+  - brotli-bin=1.0.9=h166bdaf_7
+  - ca-certificates=2023.5.7=hbcca054_0
+  - certifi=2023.5.7=pyhd8ed1ab_0
+  - cffi=1.15.0=py39h4bc2ebd_0
+  - charset-normalizer=3.1.0=pyhd8ed1ab_0
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - defusedxml=0.7.1=pyhd8ed1ab_0
+  - entrypoints=0.4=pyhd8ed1ab_0
+  - executing=1.2.0=pyhd8ed1ab_0
+  - flit-core=3.9.0=pyhd8ed1ab_0
+  - idna=3.4=pyhd8ed1ab_0
+  - importlib-metadata=6.6.0=pyha770c72_0
+  - importlib_metadata=6.6.0=hd8ed1ab_0
+  - importlib_resources=5.12.0=pyhd8ed1ab_0
+  - ipykernel=5.5.5=py39hef51801_0
+  - ipython=8.13.2=pyh41d4057_0
+  - ipython_genutils=0.2.0=py_1
+  - jedi=0.18.2=pyhd8ed1ab_0
+  - jinja2=3.1.2=pyhd8ed1ab_1
+  - json5=0.9.5=pyh9f0ad1d_0
+  - jsonschema=4.17.3=pyhd8ed1ab_0
+  - jupyter-lsp=2.1.0=pyhd8ed1ab_0
+  - jupyter_client=8.2.0=pyhd8ed1ab_0
+  - jupyter_core=5.3.0=py39hf3d152e_0
+  - jupyter_events=0.6.3=pyhd8ed1ab_0
+  - jupyter_server=2.6.0=pyhd8ed1ab_0
+  - jupyter_server_terminals=0.4.4=pyhd8ed1ab_1
+  - jupyterlab=4.0.0=pyhd8ed1ab_1
+  - jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
+  - jupyterlab_server=2.22.1=pyhd8ed1ab_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - libbrotlicommon=1.0.9=h166bdaf_7
+  - libbrotlidec=1.0.9=h166bdaf_7
+  - libbrotlienc=1.0.9=h166bdaf_7
+  - libffi=3.4.4=h6a678d5_0
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libsodium=1.0.18=h36c2ea0_1
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - markupsafe=2.1.1=py39h7f8727e_0
+  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
+  - mistune=2.0.5=pyhd8ed1ab_0
+  - nbclient=0.8.0=pyhd8ed1ab_0
+  - nbconvert-core=7.4.0=pyhd8ed1ab_0
+  - nbformat=5.8.0=pyhd8ed1ab_0
+  - ncurses=6.4=h6a678d5_0
+  - notebook-shim=0.2.3=pyhd8ed1ab_0
+  - openssl=1.1.1t=h7f8727e_0
+  - overrides=7.3.1=pyhd8ed1ab_0
+  - packaging=23.1=pyhd8ed1ab_0
+  - pandocfilters=1.5.0=pyhd8ed1ab_0
+  - parso=0.8.3=pyhd8ed1ab_0
+  - pexpect=4.8.0=pyh1a96a4e_2
+  - pickleshare=0.7.5=py_1003
+  - pip=23.0.1=py39h06a4308_0
+  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0
+  - platformdirs=3.5.1=pyhd8ed1ab_0
+  - prometheus_client=0.17.0=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.38=pyha770c72_0
+  - prompt_toolkit=3.0.38=hd8ed1ab_0
+  - ptyprocess=0.7.0=pyhd3deb0d_0
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - pycparser=2.21=pyhd8ed1ab_0
+  - pygments=2.15.1=pyhd8ed1ab_0
+  - pyrsistent=0.18.0=py39heee7806_0
+  - pysocks=1.7.1=pyha2e5f31_6
+  - python=3.9.16=h7a1cb2a_2
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python-fastjsonschema=2.17.1=pyhd8ed1ab_0
+  - python-json-logger=2.0.7=pyhd8ed1ab_0
+  - python_abi=3.9=2_cp39
+  - pytz=2023.3=pyhd8ed1ab_0
+  - pyyaml=6.0=py39hb9d737c_4
+  - pyzmq=25.0.2=py39h6a678d5_0
+  - readline=8.2=h5eee18b_0
+  - requests=2.31.0=pyhd8ed1ab_0
+  - rfc3339-validator=0.1.4=pyhd8ed1ab_0
+  - rfc3986-validator=0.1.1=pyh9f0ad1d_0
+  - send2trash=1.8.2=pyh41d4057_0
+  - setuptools=66.0.0=py39h06a4308_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sniffio=1.3.0=pyhd8ed1ab_0
+  - soupsieve=2.3.2.post1=pyhd8ed1ab_0
+  - sqlite=3.41.2=h5eee18b_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - terminado=0.17.1=pyh41d4057_0
+  - tinycss2=1.2.1=pyhd8ed1ab_0
+  - tk=8.6.12=h1ccaba5_0
+  - tomli=2.0.1=pyhd8ed1ab_0
+  - tornado=6.2=py39h5eee18b_0
+  - traitlets=5.9.0=pyhd8ed1ab_0
+  - typing-extensions=4.6.2=hd8ed1ab_0
+  - typing_extensions=4.6.2=pyha770c72_0
+  - typing_utils=0.1.0=pyhd8ed1ab_0
+  - urllib3=2.0.2=pyhd8ed1ab_0
+  - wcwidth=0.2.6=pyhd8ed1ab_0
+  - webencodings=0.5.1=py_1
+  - websocket-client=1.5.2=pyhd8ed1ab_0
+  - wheel=0.38.4=py39h06a4308_0
+  - xz=5.4.2=h5eee18b_0
+  - yaml=0.2.5=h7f98852_2
+  - zeromq=4.3.4=h9c3ff4c_1
+  - zipp=3.15.0=pyhd8ed1ab_0
+  - zlib=1.2.13=h5eee18b_0
+  - pip:
+      - aiohttp==3.8.4
+      - aiosignal==1.3.1
+      - async-timeout==4.0.2
+      - blosc2==2.0.0
+      - cmake==3.26.3
+      - contourpy==1.0.7
+      - cycler==0.11.0
+      - cython==0.29.35
+      - datasets==2.12.0
+      - dill==0.3.6
+      - filelock==3.12.0
+      - fonttools==4.39.4
+      - frozenlist==1.3.3
+      - fsspec==2023.5.0
+      - huggingface-hub==0.14.1
+      - joblib==1.2.0
+      - kiwisolver==1.4.4
+      - lit==16.0.5
+      - llvmlite==0.40.1
+      - logomaker==0.8
+      - matplotlib==3.7.1
+      - mpmath==1.3.0
+      - msgpack==1.0.5
+      - multidict==6.0.4
+      - multiprocess==0.70.14
+      - networkx==3.1
+      - numba==0.57.1
+      - numexpr==2.8.4
+      - numpy==1.24.3
+      - nvidia-cublas-cu11==11.10.3.66
+      - nvidia-cuda-cupti-cu11==11.7.101
+      - nvidia-cuda-nvrtc-cu11==11.7.99
+      - nvidia-cuda-runtime-cu11==11.7.99
+      - nvidia-cudnn-cu11==8.5.0.96
+      - nvidia-cufft-cu11==10.9.0.58
+      - nvidia-curand-cu11==10.2.10.91
+      - nvidia-cusolver-cu11==11.4.0.1
+      - nvidia-cusparse-cu11==11.7.4.91
+      - nvidia-nccl-cu11==2.14.3
+      - nvidia-nvtx-cu11==11.7.91
+      - pandas==2.0.2
+      - pillow==9.5.0
+      - py-cpuinfo==9.0.0
+      - pyarrow==12.0.0
+      - pynndescent==0.5.10
+      - pyparsing==3.0.9
+      - regex==2023.5.5
+      - responses==0.18.0
+      - scikit-learn==1.2.2
+      - scipy==1.10.1
+      - sympy==1.12
+      - tables==3.8.0
+      - threadpoolctl==3.1.0
+      - tokenizers==0.13.3
+      - torch==2.0.1
+      - tqdm==4.65.0
+      - transformers==4.29.2
+      - triton==2.0.0
+      - tzdata==2023.3
+      - umap-learn==0.5.3
+      - xxhash==3.2.0
+      - yarl==1.9.2
+prefix: /home/esbg/anaconda3/envs/phosST