[{"_1":2,"_155":-5,"_156":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_134":135},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":17},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":-5,"_85":86,"_125":-5,"_126":11,"_127":17,"_128":11,"_129":-5,"_130":11,"_131":-5,"_132":133},"canonicalUrl","https://hub.docker.com/r/mircare/brewery/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","mircare","reponame","brewery","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":11,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2019-10-31T18:11:53.193094Z","description","Fast, state-of-the-art ab initio prediction of 1D protein structure annotations","is_private","last_updated","2021-02-28T22:53:15.9293Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",1667,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","[![PWC](https://camo.githubusercontent.com/6887feb0136db5156c4f4146e3dd2681d06d9c75/68747470733a2f2f692e6372656174697665636f6d6d6f6e732e6f72672f6c2f62792d6e632d73612f342e302f38387833312e706e67)](https://github.com/mircare/Brewery/#license)\n\n# Brewery: prediction of 1D protein structure annotations\n\nThe web server, train and test sets of Brewery are available at http://distilldeep.ucd.ie/brewery/. \nThe docker container is available at https://hub.docker.com/r/mircare/brewery ([HOWTO](https://github.com/mircare/Brewery#use-the-docker-image)). \n\n*The predictions of the UniProtKB entries for **COVID-19** are available at http://distilldeep.ucd.ie/brewery/.* \nSee https://github.com/mircare/Porter5 to predict protein secondary structure only.\n\n

$\"Pipeline$ Diagram of the pipeline we propose to gather and exploit deeper profiles.

\n\n## Setup\n```\n$ git clone https://github.com/mircare/Brewery/ --depth 1 && rm -rf Brewery/.git\n```\n\n### Requirements\n1. Python3 (https://www.python.org/downloads/);\n1. NumPy (https://www.scipy.org/scipylib/download.html);\n1. HHblits (https://github.com/soedinglab/hh-suite/);\n1. uniprot20 (http://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/old-releases/uniprot20_2016_02.tgz).\n\n#### Optionally (for more accurate predictions):\n1. PSI-BLAST (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/); \n1. UniRef90 (ftp://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref90/uniref90.fasta.gz).\n\n\n## How to run Brewery with/without PSI-BLAST\n```\n# To exploit HHblits only (for fast and accurate predictions)\n$ python3 Brewery/Brewery.py -i Brewery/example/2FLGA.fasta --cpu 4 --fast\n\n# To exploit both PSI-BLAST and HHblits (for very accurate predictions)\n$ python3 Brewery/Brewery.py -i Brewery/example/2FLGA.fasta --cpu 4\n```\n\n### How to run Brewery on multiple sequences\n```\n# To split a FASTA file with multiple sequences (Optionally)\n$ python3 Brewery/split_fasta.py many_sequences.fasta\n\n# To predict all the fasta files in a given directory (Fastas)\n$ python3 Brewery/multiple_fasta.py -i Fastas/ --cpu 4 --fast\n\n# To run multiple predictions in parallel (using a total of 8 cores)\n$ python3 Brewery/multiple_fasta.py -i Fastas/ --cpu 4 --parallel 2 --fast\n```\n\n## How to visualize the help of Brewery\n```\n$ python3 Brewery/Brewery.py --help\nusage: Brewery.py [-h] [-input fasta_file] [--cpu CPU] [--fast] [--noSS]\n [--noTA] [--noSA] [--noCD] [--distill] [--setup]\n\nThis is the standalone of Brewery5. Run it on a FASTA file to predict its\nSecondary Structure in 3- and 8-classes (Porter5), Solvent Accessibility in 4\nclasses (PaleAle5), Torsional Angles in 14 classes (Porter+5) and Contact\nDensity in 4 classes (BrownAle).\n\noptional arguments:\n -h, --help show this help message and exit\n -input fasta_file FASTA file containing the protein to predict\n --cpu CPU How many cores to perform this prediction\n --fast Use only HHblits (skip PSI-BLAST)\n --bfd Harness also the BFD database (https://bfd.mmseqs.com/)\n --noSS Skip Secondary Structure prediction with Porter5\n --noTA Skip Torsional Angles prediction with Porter+5\n --noSA Skip Solvent Accessibility prediction with PaleAle5\n --noCD Skip Contact Density prediction with BrownAle5\n --distill Generate useful outputs for 3D protein structure prediction\n --setup Initialize Brewery5 from scratch (it is recommended when\n there has been any change involving PSI-BLAST, HHblits,\n Brewery itself, etc).\n\nE.g., run Brewery on 4 cores: python3 Brewery5.py -i example/2FLGA --cpu 4\n```\n\n### Use the docker image\n```\n# Set the absolute PATHs for databases and query sequences (stored locally)\n$ docker run --name brewery -v /**PATH_to_uniprot20_2016_02**:/uniprot20 \\\n-v /**PATH_to_UniRef90_optional**:/uniref90 -v /**PATH_to_fasta_to_predict**:/Brewery/query \\\n--cap-add IPC_LOCK mircare/brewery sleep infinity &\n\n# Run a prediction using 5 cores and HHblits only\n$ docker exec brewery python3 Brewery.py -i query/2FLGA.fasta --cpu 5 --fast\n```\n\n\n## Performances of Secondary Structure Predictors in 3 classes\n| Method | Q3 per AA | SOV'99 per AA | Q3 per protein | SOV'99 per protein |\n| :--- | :---: | :---: | :---: | :---: |\n| **Brewery** | **83.81%** | **80.41%** | **84.32%** | **81.05%** |\n| SPIDER 3 | 83.15% | 79.43% | 83.42% | 79.79% |\n| **Brewery *HHblits only*** | **83.06%** | **79.49%** | **83.68%** | **80.26%** |\n| SSpro 5.1 *with templates* | 82.58% | 78.54% | 83.94% | 80.29% |\n| PSIPRED 4.01 | 81.88% | 77.36% | 82.48% | 78.22% |\n| RaptorX-Property | 81.86% | 78.08% | 82.57% | 78.99% |\n| Porter 4 | 81.66% | 78.05% | 82.29% | 78.61% | \n| SSpro 5.1 *ab initio* | 81.17% | 76.87% | 81.10% | 76.92% |\n| DeepCNF | 81.04% | 76.74% | 81.16% | 76.99% |\n\nReference: Table 1 in https://doi.org/10.1101/289033.\n\n\n## Performances of Secondary Structure Predictors in 8 classes\n| Method | Q8 per AA | SOV8_refine per AA | Q8 per protein | SOV8_refine per protein |\n| :--- | :---: | :---: | :---: | :---: |\n| **Brewery** | **73.02%** | **72.09%** | **73.92%** | **72.64%** |\n| SSpro 5.1 *with templates* | 71.91% | 70.72% | 74.46% | 73.45% |\n| **Brewery *HHblits only*** | **71.8%** | **71.16%** | **72.83%** | **71.74%** |\n| RaptorX-Property | 70.74% | 69.65% | 71.78% | 70.03% |\n| DeepCNF | 69.76% | 68.5% | 70.14% | 68.06% |\n| SSpro 5.1 *ab initio* | 68.85% | 67.54% | 69.27% | 67.91% |\n\nReference: Table 2 in https://doi.org/10.1101/289033.\n\n\n## Performances of Solvent Accessibility Predictors in up to 4 classes\n| Method | Q2 per AA | Q3 per AA | Q4 per AA |\n| :--- | :---: | :---: | :---: |\n| ACCpro 5.1 *with templates* | 80.5% | N.A. | N.A. |\n| **Brewery** | **80.48%** | **66.41%** | **56.46%** |\n| PaleAle 4 | 78.21% | N.A. | 52.53% |\n| SPIDER 3 | 77.91% | 61.19% | 49.01% |\n| ACCpro 5.1 *ab initio* | 76.6% | N.A. | N.A. |\n| RaptorX-Property | N.A. | 63.25% | N.A. |\n\n\n## Performances of Torsion Angles Predictors in 14 classes\n| Method | Q14 per AA | Q14 per protein |\n| :--- | :---: | :---: |\n| **Brewery** | **69.93%** | **70.59%** |\n| SPIDER 3 | 66.58% | 66.27% |\n| Porter+ | 64.73% | 66% |\n\n\n## Performances of Contact Density Predictors in 4 classes\n| Method | Q4 per AA | Q4 per protein |\n| :--- | :---: | :---: |\n| **Brewery** | **50.01%** | **48%** |\n| BrownAle | 46.5% | N.A. |\n\n\n## Citation\nIf you use Brewery, please cite our Bioinformatics paper:\n```\n@article{torrisi_brewery_2020,\n\ttitle = {Brewery: Deep Learning and deeper profiles for the prediction of 1D protein structure annotations},\n\tdoi = {10.1093/bioinformatics/btaa204},\n\tjournal = {Bioinformatics},\n\tauthor = {Torrisi, Mirko and Pollastri, Gianluca}\n}\n```\n\n\n## References\nBrewery: Deep Learning and deeper profiles for the prediction of 1D protein structure annotations,
\nBioinformatics, Oxford University Press; Mirko Torrisi and Gianluca Pollastri;
\nGuest link: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa204/5811232?guestAccessKey=9a73ae2a-2cb6-4fe1-b333-a4f3261f02cf.\n\nProtein Structure Annotations; Essentials of Bioinformatics, Volume I. Springer Nature;
\nMirko Torrisi and Gianluca Pollastri; Post-print: https://www.researchgate.net/publication/332048741_Protein_Structure_Annotations.\n\nDeeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction, Scientific Reports, Nature Publishing Group; Mirko Torrisi, Manaz Kaleel and Gianluca Pollastri;
\ndoi: https://doi.org/10.1038/s41598-019-48786-x.\n\nPaleAle 5.0: prediction of protein relative solvent accessibility by deep learning, Amino Acids, Springer
\nManaz Kaleel, Mirko Torrisi, Catherine Mooney and Gianluca Pollastri; Guest Link: https://rdcu.be/bNlXS.\n\n\n## License\nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.\n\nEmail us at gianluca[dot]pollastri[at]ucd[dot]ie if you wish to use it for purposes not permitted by the CC BY-NC-SA 4.0.\n\n $\"Creative$ \n","has_starred","hub_user","immutable_tags_settings",{"_71":11,"_72":73},"enabled","rules",[74],".*","is_automated","permissions",{"_78":17,"_79":11,"_80":11},"read","write","admin","user","storage_size",2596140171,"orgData","recentTags",[87],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":62,"_124":17},"content_type","creator",5857526,"full_size",261558262,"id",75301263,"images",[97],{"_98":99,"_100":101,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":92,"_59":62,"_113":-5},"architecture","amd64","digest","sha256:b262c7a3d216fa4f762a6654bcfa2b2c3dee6d464bfad393f90a0d5760fa89a6","features","","last_pulled","2026-09-24T22:01:46.086163853Z","last_pushed","2021-02-28T22:09:14Z","os","linux","os_features","os_version","size","variant","2021-02-28T22:53:15.556817Z","last_updater","last_updater_username","media_type","latest","repository",7945010,"tag_last_pulled","tag_last_pushed","tag_status","v2","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":136,"_143":144,"_132":153,"_154":11},{"_36":137,"_38":138,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":139,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":140,"_75":11,"_76":142,"_81":29,"_82":83},[],[40],[52],{"_71":11,"_72":141},[74],{"_78":17,"_79":11,"_80":11},"tags",{"_145":60,"_146":-5,"_147":-5,"_148":149},"count","next","previous","results",[150],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":151,"_46":114,"_115":90,"_116":29,"_117":52,"_53":118,"_119":120,"_121":105,"_122":114,"_123":62,"_124":17},[152],{"_98":99,"_100":101,"_102":103,"_104":105,"_106":107,"_108":109,"_110":103,"_111":-5,"_112":92,"_59":62,"_113":-5},[],"canDeleteTags","actionData","errors"]

mircare/brewery

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