{"created":"2024-01-29T09:39:52.841285+00:00","id":"d35zf-1ja47","links":{"files":"https://data.narodni-repozitar.cz/general/datasets/d35zf-1ja47/files/","self":"https://data.narodni-repozitar.cz/general/datasets/d35zf-1ja47","transitions":{}},"metadata":{"$schema":"https://data.narodni-repozitar.cz/schemas/nr_datasets/nr-datasets-v1.0.0.json","InvenioID":"d35zf-1ja47","_bucket":"49e88a5f-7902-4c23-82ee-ebf43f7395ab","_files":[{"bucket":"49e88a5f-7902-4c23-82ee-ebf43f7395ab","checksum":"etag:","file_id":"c14b98b6-b232-48df-91d6-c4db65e61816","key":"embeddings.tar.gz","mime_type":"application/gzip","name":"embeddings.tar.gz","size":7555058878,"type":"application/gzip","url":"https://data.narodni-repozitar.cz/general/datasets/d35zf-1ja47/files/embeddings.tar.gz","version_id":"95aa0288-088f-4bb5-8904-446ea8744b70"},{"bucket":"49e88a5f-7902-4c23-82ee-ebf43f7395ab","checksum":"etag:","file_id":"a5aa0ecb-63c2-49b5-91b7-5d0d7678e01b","key":"model.pt","mime_type":"application/octet-stream","name":"model.pt","size":3643498,"type":"application/octet-stream","url":"https://data.narodni-repozitar.cz/general/datasets/d35zf-1ja47/files/model.pt","version_id":"5aa8b93a-ca78-481a-8bc0-e7a7d0de7419"}],"abstract":{"en":"<span style=\"background-color: rgb(249, 249, 249);\"><font color=\"#000000\"><span style=\"font-size: 16px; letter-spacing: normal;\">This repository contains the data used to create AlphaFind (<a href=\"https://alphafind.fi.muni.cz/\">https://alphafind.fi.muni.cz/</a>),&nbsp;</span></font></span><font color=\"#000000\"><span style=\"font-size: 16px; letter-spacing: normal;\">a web-based search engine that allows for fast structure-based search of the entire set of AlphaFold DB protein structures. First, we include&nbsp;</span></font><span style=\"background-color: rgb(249, 249, 249); color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; text-decoration-line: inherit; text-transform: inherit;\">compact (dimensionality=45) vector embeddings for 214M protein structures from the AlphaFold database (</span><a href=\"https://alphafold.ebi.ac.uk/\" style=\"font-size: 16px; letter-spacing: normal; text-transform: inherit;\">https://alphafold.ebi.ac.uk/</a>, available for download at&nbsp;<a href=\"https://github.com/google-deepmind/alphafold/tree/main/afdb\">https://github.com/google-deepmind/alphafold/tree/main/afdb</a><span style=\"background-color: rgb(249, 249, 249);\"><font color=\"#000000\"><span style=\"font-size: 16px; letter-spacing: normal; text-transform: inherit;\">). We used these embeddings for fast protein similarity searching, by quickly pre-filtering the candidate answer (214M \u2192 1M structures). This is made possible by the grouping of proteins into 2000 similar clusters (the form in which the data is provided) using a classification neural network (provided as well). These compact vector embeddings as well as the grouping can be freely used and distributed and given the vector are suitable for processing by several data processing tools. For reproducing the results, see also our GitHub repository (</span><span style=\"font-size: 16px; letter-spacing: normal;\"><a href=\"https://github.com/Coda-Research-Group/AlphaFind\">https://github.com/Coda-Research-Group/AlphaFind</a></span><span style=\"font-size: 16px; letter-spacing: normal; text-transform: inherit;\">) with open-sourced code.</span></font></span>"},"accessRights":[{"is_ancestor":false,"level":1,"links":{"self":"https://data.narodni-repozitar.cz/2.0/taxonomies/accessRights/c-abf2"},"relatedURI":{"COAR":"http://purl.org/coar/access_right/c_abf2"},"title":{"cs":"otev\u0159en\u00fd p\u0159\u00edstup","en":"open access"}}],"contributors":[],"creators":[{"affiliation":[{"aliases":["MU"],"busy_count":4,"data":{"aliases":["MU"],"busy_count":4,"descendants_busy_count":0,"descendants_count":0,"fullName":"Masarykova univerzita","ico":"00216224","institutionCategory":["ve\u0159ejn\u00e1 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rgb(249, 249, 249);\">The embedding method, first published in <a href=\"https://\">Learned Indexing in&nbsp;Proteins: Substituting Complex Distance Calculations with&nbsp;Embedding and&nbsp;Clustering Techniques</a>&nbsp;proposed dividing the protein sequence into N consecutive sections \u2013 the positions of the atoms within each section are averaged, and the section is subsequently treated as a single point in space. 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weights&nbsp;</span><span style=\"color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; background-color: rgb(249, 249, 249);\">of the classification neural network used to find the most similar category given an input embedding vector - <b>model.pt</b></span><div><span style=\"color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; background-color: rgb(249, 249, 249);\"><b><br></b></span></div><div><span style=\"color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; background-color: rgb(249, 249, 249);\">Embeddings: Upon uncompression (e.g. tar -xvf embeddings.tar.gz), a folder with 2000 pickle files will be created, each representing one category identified by the neural network. Each pickle file can be opened using the pickle library in Python.&nbsp;</span><span style=\"background-color: rgb(249, 249, 249); color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; text-decoration-line: inherit; text-transform: inherit;\">Each file is a Pandas DataFrame with Index corresponding to the protein's UniProt ID.</span><span style=\"color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; background-color: rgb(249, 249, 249);\"><b><br></b></span></div><div><span style=\"background-color: rgb(249, 249, 249); color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; text-decoration-line: inherit; text-transform: inherit;\"><br></span></div><div><span style=\"background-color: rgb(249, 249, 249); color: rgb(0, 0, 0); font-size: 16px; letter-spacing: normal; text-decoration-line: inherit; text-transform: inherit;\">The neural network weights, training losses and state were exported using PyTorch library. For instructions to load them see our code repository --&nbsp;</span><font color=\"#000000\"><span style=\"font-size: 16px; letter-spacing: normal;\"><a href=\"https://github.com/Coda-Research-Group/AlphaFind\">https://github.com/Coda-Research-Group/AlphaFind</a></span></font><br></div>"},"titles":[{"title":{"cs":"AlphaFind: Discover structure similarity across the entire known proteome \u2013 data and model"},"titleType":"mainTitle"}]},"revision":555,"updated":"2026-05-16T08:55:36.485600+00:00"}