Expasy is the bioinformatics resource portal of the SIB Swiss Institute of Bioinformatics.

PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family a new sequence belongs. PROSITE is based at the Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland.

BASILIScan search with human CDC7 kinase (Uniprot ID: O00311) against all vertebrate sequences available from UniprotKB (both Swissprot and TrEMBL). (CSV 263 kb)

A database of proteins identified by various 2-D PAGE and SDS-PAGE reference maps. Each SWISS-2DPAGE entry contains textual data on one protein, including mapping procedures, physiological and pathological information, experimental data (isoelectric point, molecular weight, amino acid composition, peptide masses) and bibliographical references. In addition to this textual data, SWISS-2DPAGE provides several 2-D PAGE and SDS-PAGE images showing the experimentally determined location of the protein, as well as a theoretical region computed from the sequence protein, indicating where the protein might be found in the gel. Using the database, users can locate these proteins on the 2-D PAGE maps or display the region of a 2-D PAGE map where one might expect to find a protein from UniProtKB/Swiss-Prot.

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

A comprehensive collection of annotations and structures for protein modifications including amino-terminal, carboxyl-terminal and peptide chain cross-link post-translational modifications. It provides: systematic and alternate names, atomic formulas and masses, enzyme activities generating the modifications, keywords, literature citations, Gene Ontology cross-references, Protein Information Resource (PIR) and SWISS-PROT protein sequence database feature table annotations, structure diagrams and molecular models. Each RESID Database entry presents a chemically unique modification and shows how that modification is currently annotated in the protein sequence databases, Swiss-Prot and the Protein Information Resource (PIR). The RESID Database provides a table of corresponding equivalent feature annotations that is used in the UniProt project, an international effort to combine the resources of the Swiss-Prot, TrEMBL and PIR. As an annotation tool, the RESID Database is used in standardizing and enhancing modification descriptions in the feature tables of Swiss-Prot entries.

NCBIfam is a collection of protein families, featuring curated multiple sequence alignments, hidden Markov models (HMMs) and annotation, which provides a tool for identifying functionally related proteins based on sequence homology. NCBIfam is maintained at the National Center for Biotechnology Information (Bethesda, MD). NCBIfam includes models from TIGRFAMs, another database of protein families developed at The Institute for Genomic Research, then at the J. Craig Venter Institute (Rockville, MD, US).

Repository of sequenced antibodies, integrating curated information about antibody and its antigen with cross links to standardized databases of chemical and protein entities. Manually curated repository of sequenced antibodies, developed by Geneva Antibody Facility at University of Geneva, in collaboration with CALIPHO and Swiss Prot groups at SIB Swiss Institute of Bioinformatics. Database provides list of sequenced antibodies with their known targets. Each antibody is assigned unique ID number that can be used in academic publications to increase reproducibility of experiments.

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de.

SMART (a Simple Modular Architecture Research Tool) allows the identification and annotation of genetically mobile domains and the analysis of domain architectures. SMART is based at EMBL, Heidelberg, Germany.

HAMAP stands for High-quality Automated and Manual Annotation of Proteins. HAMAP profiles are manually created by expert curators. They identify proteins that are part of well-conserved protein families or subfamilies. HAMAP is based at the SIB Swiss Institute of Bioinformatics, Geneva, Switzerland.

neXtProt is a comprehensive human-centric discovery platform, offering its users a seamless integration of and navigation through protein-related data.

Developed between 2009 and 2023 by the CALIPHO group (Computer Analysis and Laboratory Investigation of Proteins of Human Origin) at the University of Geneva and the SIB Swiss Institute of Bioinformatics, neXtProt was designed to help researchers make sense of what all these human proteins do in our bodies by

• Adding more information to the corpus of data on human proteins that is already in Swiss-Prot with data originating from a variety of high-throughput approaches (such as micro-array, antibodies screens, proteomics, interactomics, structural genomics).
• Carefully selecting all of these data sets to provide high-quality data.
• Organizing the data in such a way that it is possible to seamlessly build powerful queries in the most user-friendly way possible.
• Developing software tools ranging from sequence analysis to text and data mining to be integrated in various research environments. These tools will meet the specific needs of both academic and industrial users.

PIRSF protein classification system is a network with multiple levels of sequence diversity from superfamilies to subfamilies that reflects the evolutionary relationship of full-length proteins and domains. PIRSF is based at the Protein Information Resource, Georgetown University Medical Centre, Washington DC, US.

Scores of different structural assessment tools for the predicted models from SWISS-MODEL homology-modeling server.

Data set of manually annotated chordata-specific proteins as well as those that are widely conserved. The program keeps existing human entries up-to-date and broadens the manual annotation to other vertebrate species, especially model organisms, including great apes, cow, mouse, rat, chicken, zebrafish, as well as Xenopus laevis and Xenopus tropicalis. A draft of the complete human proteome is available in UniProtKB/Swiss-Prot and one of the current priorities of the Chordata protein annotation program is to improve the quality of human sequences provided. To this aim, they are updating sequences which show discrepancies with those predicted from the genome sequence. Dubious isoforms, sequences based on experimental artifacts and protein products derived from erroneous gene model predictions are also revisited. This work is in part done in collaboration with the Hinxton Sequence Forum (HSF), which allows active exchange between UniProt, HAVANA, Ensembl and HGNC groups, as well as with RefSeq database. UniProt is a member of the Consensus CDS project and thye are in the process of reviewing their records to support convergence towards a standard set of protein annotation. They also continuously update human entries with functional annotation, including novel structural, post-translational modification, interaction and enzymatic activity data. In order to identify candidates for re-annotation, they use, among others, information extraction tools such as the STRING database. In addition, they regularly add new sequence variants and maintain disease information. Indeed, this annotation program includes the Variation Annotation Program, the goal of which is to annotate all known human genetic diseases and disease-linked protein variants, as well as neutral polymorphisms.

This data collection accompanies the manuscript "Classifying protein kinase conformations with machine learning".

It is created using the kinactive v0.1 tool written in pure Python v3.10. Note that the data are provided for the reference and reproducibility purposes and will not be compatible with later versions of `kinactive` built upon lXtractor > 0.1.1. Refer to the kinactive documentation for instructions on how to obtain an actualized version of the structural kinome collection.

File descriptions:

• db_v3.tar.gz -- a structural kinome collection archive. One can unpack it and inspect the contents or load it into the Python interpreter using `kinactive` or `lXtractor` tools.
• db_af2.tar.gz -- an AlphaFold2 kinome collection for Swiss-Prot sequences.
• default_*_vs.tsv -- structure/sequence variables calculated with lXtractor and used in an interpretable ML pipeline.
• *_features.tsv -- lists of ranked features selected by the eBoruta tool for each classifier.
• Supplement_labels.tsv -- ML model predictions for each PK domain structure found in db_v3.
• predictions_af2.csv -- Active/Inactive and DFG labels predicted for domains in db_af2.

A prediction tool for GPCR Family Classification from sequence alone based on a probabilistic method that uses family-specific profile Hidden Markov Models. The PRED-GPCR system is based on a probabilistic method that uses family specific profile HMMs in order to determine to which GPCR family a query sequence belongs or resembles. The approach proposed in this method exploits the descriptive power of profile HMMs along with an exhaustive discrimination assessment method to select only highly selective and sensitive profiles, for each family. The collection of these profiles constitutes a signature library, which is scanned, for significant matches with a given query sequence. The output report for a query sequence consists of two sections: * A ranked list of the profile HMM matches, below the selected individual motif E-value cutoff, along with their corresponding family. * A ranked list of the Combined P-values, E-values as well as the number of profiles matched for each family. To cross-evaluate your results you can browse through Swiss-Prot, Trembl, Pfam and Prosite family related entries.

The Steinegger Lab Dataset comprises biological databases and resources critical for protein sequence and structure analysis, developed to support ColabFold, MMseqs2, and Foldseek/Foldcomp—three high-performance computational tools widely used in bioinformatics.The MMseqs2 dataset serves as the backbone for our fast structure prediction tool, ColabFold, and includes UniRef30, BFD, and the ColabFold environmental databases. These datasets are specifically designed for the rapid generation of multiple sequence alignments (MSAs), which are essential for high-accuracy structure prediction. Beyond MSA generation, these resources allow for fast taxonomy annotations and functional annotation, supporting a wide range of bioinformatics applications.The Foldseek dataset includes preprocessed databases such as the AlphaFold Database (AFDB), PDB, SwissProt, and CATH, specifically designed for protein structure similarity searches. These datasets encompass the majority of both experimental and predicted structural resources, supporting analyses for monomers and multimers alike.

Filters applied to UniProt protein entries to parse enzyme data from UniProt flatfiles.

SFLD (Structure-Function Linkage Database) is a hierarchical classification of enzymes that relates specific sequence-structure features to specific chemical capabilities.