Resource List
List all resources, or create a new resource.
GET /api/t/?inputDataType=%22Protein%20sequence%22
{ "count": 352, "next": "?page=2", "previous": null, "list": [ { "name": "DeepSTABp", "description": "An AI based web tool to predict the melting temperature (Tm) of proteins based on their amino acid sequence and various growth conditions.", "homepage": "https://csb-deepstabp.bio.rptu.de", "biotoolsID": "deepstabp", "biotoolsCURIE": "biotools:deepstabp", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3096", "term": "Editing" }, { "uri": "http://edamontology.org/operation_0331", "term": "Variant effect prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1964", "term": "plain text format (unformatted)" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0897", "term": "Protein property" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0130", "term": "Protein folding, stability and design" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0080", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/topic_0601", "term": "Protein modifications" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "JavaScript" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/CSBiology/deepStabP", "type": [ "Repository" ], "note": null }, { "url": "https://git.nfdi4plants.org/f_jung/deepstabp", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.3390/IJMS24087444", "pmid": "37108605", "pmcid": "PMC10138888", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability", "abstract": "Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields.", "date": "2023-04-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Jung F." }, { "name": "Frey K." }, { "name": "Zimmer D." }, { "name": "Muhlhaus T." } ], "journal": "International Journal of Molecular Sciences" } } ], "credit": [ { "name": "Timo Mühlhaus", "email": "timo.muehlhaus@rptu.de", "url": null, "orcidid": "https://orcid.org/0000-0003-3925-6778", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-25T17:54:30.731190Z", "lastUpdate": "2023-09-25T17:54:30.733846Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "ACP-MLC", "description": "A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types.", "homepage": "https://github.com/Nicole-DH/ACP-MLC", "biotoolsID": "acp_mlc", "biotoolsCURIE": "biotools:acp_mlc", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3631", "term": "Peptide identification" }, { "uri": "http://edamontology.org/operation_3936", "term": "Feature selection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://www.amazon.com/clouddrive/share/dW6uIgXPXfNBCLIcMcMJiKJ9rr5aXaJAo78VgGKVKRH", "type": "Software package", "note": null, "version": null } ], "documentation": [ { "url": "https://github.com/Nicole-DH/ACP-MLC/blob/master/Manual.pdf", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1016/J.COMPBIOMED.2023.106844", "pmid": "37058760", "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "ACP-MLC: A two-level prediction engine for identification of anticancer peptides and multi-label classification of their functional types", "abstract": "Anticancer peptides (ACPs), a series of short bioactive peptides, are promising candidates in fighting against cancer due to their high activity, low toxicity, and not likely cause drug resistance. The accurate identification of ACPs and classification of their functional types is of great importance for investigating their mechanisms of action and developing peptide-based anticancer therapies. Here, we provided a computational tool, called ACP-MLC, to address binary classification and multi-label classification of ACPs for a given peptide sequence. Briefly, ACP-MLC is a two-level prediction engine, in which the 1st-level model predicts whether a query sequence is an ACP or not by random forest algorithm, and the 2nd-level model predicts which tissue types the sequence might target by the binary relevance algorithm. Development and evaluation by high-quality datasets, our ACP-MLC yielded an area under the receiver operating characteristic curve (AUC) of 0.888 on the independent test set for the 1st-level prediction, and obtained 0.157 hamming loss, 0.577 subset accuracy, 0.802 F1-scoremacro, and 0.826 F1-scoremicro on the independent test set for the 2nd-level prediction. A systematic comparison demonstrated that ACP-MLC outperformed existing binary classifiers and other multi-label learning classifiers for ACP prediction. Finally, we interpreted the important features of ACP-MLC by the SHAP method. User-friendly software and the datasets are available at https://github.com/Nicole-DH/ACP-MLC. We believe that the ACP-MLC would be a powerful tool in ACP discovery.", "date": "2023-05-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Deng H." }, { "name": "Ding M." }, { "name": "Wang Y." }, { "name": "Li W." }, { "name": "Liu G." }, { "name": "Tang Y." } ], "journal": "Computers in Biology and Medicine" } } ], "credit": [ { "name": "Yun Tang", "email": "ytang234@ecust.edu.cn", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T14:44:29.501966Z", "lastUpdate": "2023-09-22T14:44:29.504583Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "A2TEA", "description": "A2TEA is a software workflow facilitating identification of candidate genes for stress adaptation based on comparative genomics and transcriptomics. It combines differential gene expression with gene family expansion as an indicator for the evolution of adaptive traits.", "homepage": "https://github.com/tgstoecker/A2TEA.Workflow", "biotoolsID": "a2tea", "biotoolsCURIE": "biotools:a2tea", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_3223", "term": "Differential gene expression profiling" }, { "uri": "http://edamontology.org/operation_3501", "term": "Enrichment analysis" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" }, { "uri": "http://edamontology.org/operation_3192", "term": "Sequence trimming" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_2977", "term": "Nucleic acid sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_1255", "term": "Sequence features" }, "format": [ { "uri": "http://edamontology.org/format_2306", "term": "GTF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3754", "term": "GO-term enrichment data" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0872", "term": "Phylogenetic tree" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_0928", "term": "Gene expression profile" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Workflow", "Web application", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" }, { "uri": "http://edamontology.org/topic_0084", "term": "Phylogeny" }, { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" }, { "uri": "http://edamontology.org/topic_0203", "term": "Gene expression" }, { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python", "R" ], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/tgstoecker/A2TEA.WebApp", "type": [ "Repository" ], "note": null }, { "url": "https://tgstoecker.shinyapps.io/A2TEA-WebApp", "type": [ "Other" ], "note": null }, { "url": "https://tgstoecker.github.io/A2TEA.WebApp", "type": [ "Other" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.12688/F1000RESEARCH.126463.2", "pmid": "37224329", "pmcid": "PMC10186066", "type": [ "Primary" ], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Tyll Stöcker", "email": "tyll.stoecker@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0001-7184-9472", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Heiko Schoof", "email": "schoof@uni-bonn.de", "url": null, "orcidid": "https://orcid.org/0000-0002-1527-3752", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-22T11:57:14.178569Z", "lastUpdate": "2023-09-22T11:57:14.182267Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "FAS", "description": "Assessing the similarity between proteins using multi-layered feature architectures.", "homepage": "https://pypi.org/project/greedyFAS/", "biotoolsID": "fas", "biotoolsCURIE": "biotools:fas", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2474", "term": "Protein architecture comparison" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_3672", "term": "Gene functional annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1277", "term": "Protein features" }, "format": [ { "uri": "http://edamontology.org/format_2332", "term": "XML" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0089", "term": "Ontology and terminology" }, { "uri": "http://edamontology.org/topic_3520", "term": "Proteomics experiment" }, { "uri": "http://edamontology.org/topic_0736", "term": "Protein folds and structural domains" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0621", "term": "Model organisms" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://pypi.org/project/greedyFAS/", "type": [ "Repository" ], "note": null }, { "url": "https://github.com/BIONF/FAS", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD226", "pmid": "37084276", "pmcid": "PMC10185405", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "FAS: assessing the similarity between proteins using multi-layered feature architectures", "abstract": "Motivation: Protein sequence comparison is a fundamental element in the bioinformatics toolkit. When sequences are annotated with features such as functional domains, transmembrane domains, low complexity regions or secondary structure elements, the resulting feature architectures allow better informed comparisons. However, many existing schemes for scoring architecture similarities cannot cope with features arising from multiple annotation sources. Those that do fall short in the resolution of overlapping and redundant feature annotations. Results: Here, we introduce FAS, a scoring method that integrates features from multiple annotation sources in a directed acyclic architecture graph. Redundancies are resolved as part of the architecture comparison by finding the paths through the graphs that maximize the pair-wise architecture similarity. In a large-scale evaluation on more than 10 000 human-yeast ortholog pairs, architecture similarities assessed with FAS are consistently more plausible than those obtained using e-values to resolve overlaps or leaving overlaps unresolved. Three case studies demonstrate the utility of FAS on architecture comparison tasks: benchmarking of orthology assignment software, identification of functionally diverged orthologs, and diagnosing protein architecture changes stemming from faulty gene predictions. With the help of FAS, feature architecture comparisons can now be routinely integrated into these and many other applications. Availability and implementation: FAS is available as python package: https://pypi.org/project/greedyFAS/.", "date": "2023-05-01T00:00:00Z", "citationCount": 3, "authors": [ { "name": "Dosch J." }, { "name": "Bergmann H." }, { "name": "Tran V." }, { "name": "Ebersberger I." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Ingo Ebersberger", "email": "ebersberger@bio.uni-frankfurt.de", "url": null, "orcidid": "https://orcid.org/0000-0001-8187-9253", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-18T10:18:12.761756Z", "lastUpdate": "2023-09-18T10:18:12.764098Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PreTP-2L", "description": "Identification of therapeutic peptides and their types using two-layer ensemble learning framework.", "homepage": "http://bliulab.net/PreTP-2L", "biotoolsID": "pretp_2l", "biotoolsCURIE": "biotools:pretp_2l", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3631", "term": "Peptide identification" }, { "uri": "http://edamontology.org/operation_3937", "term": "Feature extraction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0865", "term": "Sequence similarity score" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_3374", "term": "Biotherapeutics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD125", "pmid": "37010503", "pmcid": "PMC10076046", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework", "abstract": "Motivation: Therapeutic peptides play an important role in immune regulation. Recently various therapeutic peptides have been used in the field of medical research, and have great potential in the design of therapeutic schedules. Therefore, it is essential to utilize the computational methods to predict the therapeutic peptides. However, the therapeutic peptides cannot be accurately predicted by the existing predictors. Furthermore, chaotic datasets are also an important obstacle of the development of this important field. Therefore, it is still challenging to develop a multi-classification model for identification of therapeutic peptides and their types. Results: In this work, we constructed a general therapeutic peptide dataset. An ensemble-learning method named PreTP-2L was developed for predicting various therapeutic peptide types. PreTP-2L consists of two layers. The first layer predicts whether a peptide sequence belongs to therapeutic peptide, and the second layer predicts if a therapeutic peptide belongs to a particular species.", "date": "2023-04-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Yan K." }, { "name": "Guo Y." }, { "name": "Liu B." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Bin Liu", "email": "bliu@bliulab.net", "url": null, "orcidid": "https://orcid.org/0000-0003-3685-9469", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-15T17:09:55.300644Z", "lastUpdate": "2023-09-15T17:09:55.303383Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "FuzPred", "description": "Web server for the sequence-based prediction of the context-dependent binding modes of proteins.", "homepage": "https://fuzpred.bio.unipd.it", "biotoolsID": "fuzpred", "biotoolsCURIE": "biotools:fuzpred", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2492", "term": "Protein interaction prediction" }, { "uri": "http://edamontology.org/operation_0474", "term": "Protein structure prediction" }, { "uri": "http://edamontology.org/operation_2476", "term": "Molecular dynamics" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [] } ], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0078", "term": "Proteins" }, { "uri": "http://edamontology.org/topic_0176", "term": "Molecular dynamics" }, { "uri": "http://edamontology.org/topic_0196", "term": "Sequence assembly" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/NAR/GKAD214", "pmid": "36987846", "pmcid": "PMC10320189", "type": [], "version": null, "note": null, "metadata": { "title": "FuzPred: A web server for the sequence-based prediction of the context-dependent binding modes of proteins", "abstract": "Proteins form complex interactions in the cellular environment to carry out their functions. They exhibit a wide range of binding modes depending on the cellular conditions, which result in a variety of ordered or disordered assemblies. To help rationalise the binding behavior of proteins, the FuzPred server predicts their sequence-based binding modes without specifying their binding partners. The binding mode defines whether the bound state is formed through a disorder-To-order transition resulting in a well-defined conformation, or through a disorder-To-disorder transition where the binding partners remain conformationally heterogeneous. To account for the context-dependent nature of the binding modes, the FuzPred method also estimates the multiplicity of binding modes, the likelihood of sampling multiple binding modes. Protein regions with a high multiplicity of binding modes may serve as regulatory sites or hot-spots for structural transitions in the assembly. To facilitate the interpretation of the predictions, protein regions with different interaction behaviors can be visualised on protein structures generated by AlphaFold. The FuzPred web server (https://fuzpred.bio.unipd.it) thus offers insights into the structural and dynamical changes of proteins upon interactions and contributes to development of structure-function relationships under a variety of cellular conditions.", "date": "2023-07-05T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Hatos A." }, { "name": "Teixeira J.M.C." }, { "name": "Barrera-Vilarmau S." }, { "name": "Horvath A." }, { "name": "Tosatto S.C.E." }, { "name": "Vendruscolo M." }, { "name": "Fuxreiter M." } ], "journal": "Nucleic Acids Research" } } ], "credit": [ { "name": "Andras Hatos", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "João M C Teixeira", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Susana Barrera-Vilarmau", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Monika Fuxreiter", "email": "ti.dpinu@retierxuf.akinom", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-15T16:50:38.013206Z", "lastUpdate": "2023-09-15T16:50:38.016968Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "AcrNET", "description": "Predicting anti-CRISPR with deep learning.", "homepage": "https://proj.cse.cuhk.edu.hk/aihlab/AcrNET/", "biotoolsID": "acrnet", "biotoolsCURIE": "biotools:acrnet", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_1777", "term": "Protein function prediction" }, { "uri": "http://edamontology.org/operation_0272", "term": "Residue contact prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_2275", "term": "Molecular modelling" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0160", "term": "Sequence sites, features and motifs" }, { "uri": "http://edamontology.org/topic_3293", "term": "Phylogenetics" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/banma12956/AcrNET", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/BIOINFORMATICS/BTAD259", "pmid": "37084259", "pmcid": "PMC10174705", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "AcrNET: predicting anti-CRISPR with deep learning", "abstract": "Motivation: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance. Results: On both the cross-fold and cross-dataset validation, our method outperforms the state-of-the-art methods. Notably, AcrNET improves the prediction performance by at least 15% regarding the F1 score for the cross-dataset test problem comparing with state-of-art Deep Learning method. Moreover, AcrNET is the first computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model ESM-1b, which was pre-trained on 250 million protein sequences, AcrNET overcomes the data scarcity problem. Extensive experiments and analysis suggest that the Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. AlphaFold prediction, further motif analysis, and docking experiments further demonstrate that AcrNET can capture the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly.", "date": "2023-05-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Li Y." }, { "name": "Wei Y." }, { "name": "Xu S." }, { "name": "Tan Q." }, { "name": "Zong L." }, { "name": "Wang J." }, { "name": "Wang Y." }, { "name": "Chen J." }, { "name": "Hong L." }, { "name": "Li Y." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "Yu Li", "email": "liyu@cse.cuhk.edu.hk", "url": null, "orcidid": "https://orcid.org/0000-0002-3664-6722", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-15T11:15:49.737634Z", "lastUpdate": "2023-09-15T11:16:49.084749Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PurificationDB", "description": "Database of purification conditions for proteins.", "homepage": "https://purificationdatabase.herokuapp.com/", "biotoolsID": "purificationdb", "biotoolsCURIE": "biotools:purificationdb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3280", "term": "Named-entity and concept recognition" }, { "uri": "http://edamontology.org/operation_2421", "term": "Database search" }, { "uri": "http://edamontology.org/operation_2422", "term": "Data retrieval" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2764", "term": "Protein name (UniProt)" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0897", "term": "Protein property" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Database portal" ], "topic": [ { "uri": "http://edamontology.org/topic_0121", "term": "Proteomics" }, { "uri": "http://edamontology.org/topic_0078", "term": "Proteins" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_3336", "term": "Drug discovery" }, { "uri": "http://edamontology.org/topic_0218", "term": "Natural language processing" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1093/DATABASE/BAAD016", "pmid": "37010519", "pmcid": "PMC10069378", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "PurificationDB: Database of purification conditions for proteins", "abstract": "The isolation of proteins of interest from cell lysates is an integral step to study protein structure and function. Liquid chromatography is a technique commonly used for protein purification, where the separation is performed by exploiting the differences in physical and chemical characteristics of proteins. The complex nature of proteins requires researchers to carefully choose buffers that maintain stability and activity of the protein while also allowing for appropriate interaction with chromatography columns. To choose the proper buffer, biochemists often search for reports of successful purification in the literature; however, they often encounter roadblocks such as lack of accessibility to journals, non-exhaustive specification of components and unfamiliar naming conventions. To overcome such issues, we present PurificationDB (https://purificationdatabase.herokuapp.com/), an open-Access and user-friendly knowledge base that contains 4732 curated and standardized entries of protein purification conditions. Buffer specifications were derived from the literature using named-entity recognition techniques developed using common nomenclature provided by protein biochemists. PurificationDB also incorporates information associated with well-known protein databases: Protein Data Bank and UniProt. PurificationDB facilitates easy access to data on protein purification techniques and contributes to the growing effort of creating open resources that organize experimental conditions and data for improved access and analysis. Database URL https://purificationdatabase.herokuapp.com/.", "date": "2023-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Garland O." }, { "name": "Radaeva M." }, { "name": "Pandey M." }, { "name": "Cherkasov A." }, { "name": "Lallous N." } ], "journal": "Database" } } ], "credit": [ { "name": "Artem Cherkasov", "email": "acherkasov@prostatecentre.com", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Nada Lallous", "email": "nlallous@prostatecentre.com", "url": null, "orcidid": "https://orcid.org/0000-0002-8665-8641", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-04T17:36:56.324234Z", "lastUpdate": "2023-09-04T17:59:24.362390Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "shic", "description": "shic is a collection of shims for use in automated workflow composition", "homepage": "https://github.com/magnuspalmblad/shic", "biotoolsID": "shic", "biotoolsCURIE": "biotools:shic", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3434", "term": "Conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_3747", "term": "protXML" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2872", "term": "ID list" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3434", "term": "Conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3651", "term": "MGF" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2536", "term": "Mass spectrometry data" }, "format": [ { "uri": "http://edamontology.org/format_3651", "term": "MGF" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3434", "term": "Conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0870", "term": "Sequence distance matrix" }, "format": [ { "uri": "http://edamontology.org/format_1912", "term": "Nexus format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0870", "term": "Sequence distance matrix" }, "format": [ { "uri": "http://edamontology.org/format_1991", "term": "mega" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3434", "term": "Conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3021", "term": "UniProt accession" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3434", "term": "Conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0945", "term": "Peptide identification" }, "format": [ { "uri": "http://edamontology.org/format_3655", "term": "pepXML" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3434", "term": "Conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0945", "term": "Peptide identification" }, "format": [ { "uri": "http://edamontology.org/format_3247", "term": "mzIdentML" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null }, { "operation": [ { "uri": "http://edamontology.org/operation_3434", "term": "Conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0972", "term": "Text mining report" }, "format": [ { "uri": "http://edamontology.org/format_2332", "term": "XML" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1174", "term": "ChEBI ID" }, "format": [ { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Script" ], "topic": [], "operatingSystem": [], "language": [], "license": "MIT", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [ "Proteomics", "Galaxy", "Metabolomics" ], "link": [ { "url": "https://github.com/magnuspalmblad/shic", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/magnuspalmblad/shic/README.md", "type": [ "General" ], "note": null } ], "publication": [], "credit": [ { "name": "Magnus Palmblad", "email": "magnus.palmblad@gmail.com", "url": null, "orcidid": "https://orcid.org/0000-0002-5865-8994", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact" ], "note": null }, { "name": "Veit Schwämmle", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-9708-6722", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Dirk Winkelhardt", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-8770-2221", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Vedran Kasalica", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-0097-1056", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Anna-Lena Lamprecht", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-1953-5606", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "community": null, "owner": "n.m.palmblad@lumc.nl", "additionDate": "2023-08-30T13:03:57.407598Z", "lastUpdate": "2023-09-04T16:01:20.376269Z", "editPermission": { "type": "group", "authors": [ "VKasalica", "veits@bmb.sdu.dk" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "CRISPR-Cas-Docker", "description": "Web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins.", "homepage": "http://www.crisprcasdocker.org", "biotoolsID": "crispr_cas_docker", "biotoolsCURIE": "biotools:crispr_cas_docker", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3899", "term": "Protein-protein docking" }, { "uri": "http://edamontology.org/operation_3901", "term": "RNA-binding protein prediction" }, { "uri": "http://edamontology.org/operation_2441", "term": "RNA structure prediction" }, { "uri": "http://edamontology.org/operation_4009", "term": "Small molecule design" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] }, { "data": { "uri": "http://edamontology.org/data_3495", "term": "RNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" }, { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] }, { "data": { "uri": "http://edamontology.org/data_1465", "term": "RNA structure" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_2275", "term": "Molecular modelling" }, { "uri": "http://edamontology.org/topic_0099", "term": "RNA" }, { "uri": "http://edamontology.org/topic_2814", "term": "Protein structure analysis" }, { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/hshimlab/CRISPR-Cas-Docker", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [], "publication": [ { "doi": "10.1186/S12859-023-05296-Y", "pmid": "37098485", "pmcid": "PMC10127312", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins", "abstract": "Background: CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. Results: CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. Conclusion: CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA–protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at www.crisprcasdocker.org as a web server, and at https://github.com/hshimlab/CRISPR-Cas-Docker as an open-source tool.", "date": "2023-12-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Park H.-M." }, { "name": "Won J." }, { "name": "Park Y." }, { "name": "Anzaku E.T." }, { "name": "Vankerschaver J." }, { "name": "Van Messem A." }, { "name": "De Neve W." }, { "name": "Shim H." } ], "journal": "BMC Bioinformatics" } } ], "credit": [ { "name": "Hyunjin Shim", "email": "hyunjin.shim@ghent.ac.kr", "url": null, "orcidid": "https://orcid.org/0000-0002-7052-0971", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2023-09-04T12:50:43.054505Z", "lastUpdate": "2023-09-04T12:50:43.057176Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }