PROSITE: A Documented Database Using Patterns and Profiles as Motif Descriptors

By Christian J. A. Sigrist, L. Cerutti, Nicolas Hulo, Alexandre Gattiker, L. Falquet, M. Pagni, Amos Bairoch, Philipp Bucher. Briefings Bioinform., 2002

962 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/170761842a0568f0cc7762343eebd9983aa9d28e

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Citations in the corpus

Recent improvements to the PROSITE database (2004, methodology): “The PROSITE database uses two kinds of descriptor to identify conserved regions, patterns and generalized profiles, which each have their own strengths and weaknesses defining their area of optimum application ( 1 ).”
The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. (2003, methodology): “families, domains, and sites that combines the complementary efforts of the PROSITE (Falquet et al. 2002), PRINTS (Attwood et al. 2002), Pfam (Bateman et al. 2002), ProDom (Corpet et al. 2000), SMART (Letunic et al. 2002), and TIGRFAMs (Haft et al. 2001) databases.”
Diet and dioxins: the need to cut back. (2004, methodology): “Although there are many databases of sequence motifs available [20-23], these databases were created in a supervised way to have motifs that characterize different known protein families, domains, or functional sites.”
Tools for integrated sequence-structure analysis with UCSF Chimera (2006, background): “There are many additional features, including searching for a particular sub-sequence or PROSITE [9] pattern, display of secondary structure elements on the alignment, calculation of pairwise percent identities, and superposition assessment by generating a "closeness of fit" residue attribute.”
PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction (2018, background): “According to a previous study 22 , PROSITE motifs could only recognize 10% of annotated Y phosphorylation sites 40 , while being able to recognize 48% and 38% of the respective S and T phosphorylation sites.”
VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology (2017, neutral): “[39] PSORTb3.”
The PROSITE database (2005, methodology): “As a more detailed description of the PROSITE database has already been provided in previous publications (1,2), this paper will only focus on recent developments that have taken place during 40 the last 2 years.”
The Human Plasma Proteome (2004, methodology): “Structural and Sequence-based Domain Annotation—Sequences were scanned against a library of BioPendium and iPSI-BLAST (9, 11)-like protein profiles constructed from SCOP (14), PFAM (15), PRINTS (16), and PROSITE (17) domain families.”
GlobPlot: exploring protein sequences for globularity and disorder (2003, methodology): “There are many methods, such as SMART (Simple Modular Architecture Research Tool) (3), PRODOM (4) , Pfam (5,6), PROSITE (7) and ELM (Eukaryotic Linear Motif, http:// elm.eu.org) (8), available for finding globular domains (e.g. SH3, TyrKc, active sites) and linear motifs (e.g. SH3 ligands, LXXLL nuc”
Fast model-based protein homology detection without alignment (2007, background): “Because of an explicit representation of the linear classifier (the weight vector) in feature space, the time complexity has been reduced to OðL2Þ in Lingner and Meinicke (2006).”
New and continuing developments at PROSITE (2012, background): “These regions are identified using two types of signatures: generalized profiles (weight matrices) that describe protein families and modular protein domains and patterns (regular expressions) that describe short sequence motifs often corresponding to functionally or structurally important residues ”
The TetR Family of Transcriptional Repressors (2005, methodology): “Interpro (258) assigns proteins to the TetR family based on PROSITE signature PS01081 (364), PRINTS motif PR00455 (15, 16), and Pfam Hidden Markov Model (HMM) profile PF00440 (26, 27).”
Hidden Markov Models and their Applications in Biological Sequence Analysis (2009, methodology): “large number of profile-HMMs for various protein families: the PROSITE database [28], [ 53 ] and the Pfam database [20], [55].”
ExPASy: the proteomics server for in-depth protein knowledge and analysis (2003, background): “� PROSITE (6, 7 ) (http://www.expasy.org/prosite/) is a database of protein domains and families.”
Consensus protein design (2016, background): “…manually curated seed alignments for the development of hidden Markov model (HMM) profiles (Finn et al., 2011) or motif-specific rules and patterns (Sigrist et al., 2002), which can then be applied to larger collections such as the UniProtKB/Swiss-Prot (UniProt Consortium, 2008), Protein Data Bank…”