The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

MOTIVATION:The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge. RESULTS:In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB. AVAILABILITY AND IMPLEMENTATION:UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire.

The universal protein knowledgebase (UniProtKB) collects and centralises functional information on proteins across a wide range of species. In addition to the functional information added to all protein entries, for enzymes, which represent 20–40% of most proteomes, UniProtKB provides additional information about Enzyme Commission classification, catalytic activity, cofactors, enzyme regulation, kinetics and pathways, all based on critical assessment of published experimental data. Computer‐based analysis and structural data are used to enrich the annotation of the sequence through the identification of active sites and binding sites. While the annotation of enzymes is well‐defined, the curation of pseudoenzymes in UniProtKB has highlighted some challenges: how to identify them, how to assess their lack of catalytic activity, how to annotate their lack of catalytic activity in a consistent way and how much can be inferred and propagated from experimental data obtained from other species. Through various examples, we illustrate some of these issues and discuss some of the changes we propose to enhance the annotation and discovery of pseudoenzymes. Ultimately, improving the curation of pseudoenzymes will provide the scientific community with a comprehensive resource for pseudoenzymes, which in turn will lead to a better understanding of the evolution of these molecules, the aetiology of related diseases and the development of drugs.

Two types of cholinesterases (ChEs) are present in mammalian blood and tissues: acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). While AChE regulates neurotransmission by hydrolyzing acetylcholine at the postsynaptic membranes and neuromuscular junctions, BChE in plasma has been suggested to be involved in detoxifying toxic compounds. This study was undertaken to establish the identity of circulating ChE activity in plasmas from domestic animals (bovine, ovine, caprine, porcine and equine) by assessing sensitivity to AChE-specific inhibitors (BW284c51 and edrophonium) and BChE-specific inhibitors (dibucaine, ethopropazine and Iso-OMPA) as well as binding to anti-FBS AChE monoclonal antibodies (MAbs). Based on the inhibition of ChE activity by ChE-specific inhibitors, it was determined that bovine, ovine and caprine plasma predominantly contain AChE, while porcine and equine plasma contain BChE. Three of the anti-FBS AChE MAbs, 4E5, 5E8 and 6H9, inhibited 85-98% of enzyme activity in bovine, ovine and caprine plasma, confirming that the esterase in these plasmas was AChE. These MAbs did not bind to purified recombinant human or mouse AChE, demonstrating that these MAbs were specific for AChEs from ruminant species. These MAbs did not inhibit the activity of purified human BChE, or ChE activity in porcine and equine plasma, confirming that the ChE in these plasmas was BChE. Taken together, these results demonstrate that anti-FBS AChE MAbs can serve as useful tools for distinguishing between AChEs from ruminant and non-ruminant species and BChEs.

Methods focused on predicting 'global' annotations for proteins (such as molecular function, biological process and presence of domains or membership in a family) have reached a relatively mature stage. Methods to provide fine-grained 'local' annotation of functional sites (at the level of individual amino acid) are now coming to the forefront, especially in light of the rapid accumulation of genetic variant data. We have developed a computational method and workflow that predicts functional sites within proteins using position-specific conditional template annotation rules (namely PIR Site Rules or PIRSRs for short). Such rules are curated through review of known protein structural and other experimental data by structural biologists and are used to generate high-quality annotations for the UniProt Knowledgebase (UniProtKB) unreviewed section. To share the PIRSR functional site prediction method with the broader scientific community, we have streamlined our workflow and developed a stand-alone Java software package named PIRSitePredict. We demonstrate the use of PIRSitePredict for functional annotation of de novo assembled genome/transcriptome by annotating uncharacterized proteins from Trinity RNA-seq assembly of embryonic transcriptomes of the following three cartilaginous fishes: Leucoraja erinacea (Little Skate), Scyliorhinus canicula (Small-spotted Catshark) and Callorhinchus milii (Elephant Shark). On average about 1200 lines of annotations were predicted for each species.

The Swiss- Prot, TrEMBL, Protein Information Resource ( PIR), and DNA Data Bank of Japan ( DDBJ) protein database activities have united to form the Universal Protein Resource ( UniProt) Consortium. UniProt presents three database layers: the UniProt Archive, the UniProt Knowledgebase ( UniProtKB), and the UniProt Reference Clusters. The UniProtKB consists of two sections: UniProtKB/ Swiss- Prot ( fully manually curated entries) and UniProtKB/ TrEMBL ( automated annotation, classification and extensive cross- references). New releases are published fortnightly. A specific Plant Proteome Annotation Program ( http:// www. expasy. org/ sprot/ ppap/) was initiated to cope with the increasing amount of data produced by the complete sequencing of plant genomes. Through UniProt, our aim is to provide the scientific community with a single, centralized, authoritative resource for protein sequences and functional information that will allow the plant community to fully explore and utilize the wealth of information available for both plant and nonplant model organisms.

Advances in high-throughput and advanced technologies allow researchers to routinely perform whole genome and proteome analysis. For this purpose, they need high-quality resources providing comprehensive gene and protein sets for their organisms of interest. Using the example of the human proteome, we will describe the content of a complete proteome in the UniProt Knowledgebase (UniProtKB). We will show how manual expert curation of UniProtKB/Swiss-Prot is complemented by expert-driven automatic annotation to build a comprehensive, high-quality and traceable resource. We will also illustrate how the complexity of the human proteome is captured and structured in UniProtKB. Database URL: www.uniprot.org

The mission of the Universal Protein Resource (UniProt) (http://www.uniprot.org) is to support biological research by providing a freely accessible, stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase. It integrates, interprets and standardizes data from numerous resources to achieve the most comprehensive catalogue of protein sequences and functional annotation. UniProt comprises four major components, each optimized for different uses, the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is produced by the UniProt Consortium, which consists of groups from the European Bioinformatics Institute (EBI), the SIB Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is updated and distributed every 4 weeks and can be accessed online for searches or downloads.

The primary mission of Universal Protein Resource (UniProt) is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org.

The rapid growth of protein sequence databases has necessitated the development of methods to computationally derive annotation for uncharacterized entries. Most such methods focus on "global" annotation, such as molecular function or biological process. Methods to supply high-accuracy "local" annotation to functional sites based on structural information at the level of individual amino acids are relatively rare. In this chapter we will describe a method we have developed for annotation of functional residues within experimentally-uncharacterized proteins that relies on position-specific site annotation rules (PIR Site Rules) derived from structural and experimental information. These PIR Site Rules are manually defined to allow for conditional propagation of annotation. Each rule specifies a tripartite set of conditions whereby candidates for annotation must pass a whole-protein classification test (that is, have end-to-end match to a whole-protein-based HMM), match a site-specific profile HMM and, finally, match functionally and structurally characterized residues of a template. Positive matches trigger the appropriate annotation for active site residues, binding site residues, modified residues, or other functionally important amino acids. The strict criteria used in this process have rendered high-confidence annotation suitable for UniProtKB/Swiss-Prot features.

We have identified 72 completely conserved amino acid residues in the E protein of major groups of the Flavivirus genus by computational analyses. In the dengue species we have identified 12 highly conserved sequence regions, 186 negatively selected sites, and many dengue serotype-specific negatively selected sites. The flavivirus-conserved sites included residues involved in forming six disulfide bonds crucial for the structural integrity of the protein, the fusion motif involved in viral infectivity, and the interface residues of the oligomers. The structural analysis of the E protein showed 19 surface-exposed non-conserved residues, 128 dimer or trimer interface residues, and regions, which undergo major conformational change during trimerization. Eleven consensus T(h)-cell epitopes common to all four dengue serotypes were predicted. Most of these corresponded to dengue-conserved regions or negatively selected sites. Of special interest are six singular sites (N(37), Q(211), D(215), P(217), H(244), K(246)) in dengue E protein that are conserved, are part of the predicted consensus T(h)-cell epitopes and are exposed in the dimer or trimer. We propose these sites and corresponding epitopic regions as potential candidates for prioritization by experimental biologists for development of diagnostics and vaccines that may be difficult to circumvent by natural or man-made alteration of dengue virus.

AbstractExperimentally verified information on protein function lags far behind the rapid accumulation of protein sequences. The simple approach to propagating information from characterized proteins to unknown proteins – namely, by sequence similarity search against databases of individual proteins – may fail to produce accurate results, and typically is used to transfer only protein name information. A more accurate, consistent, and comprehensive approach for large‐scale automated annotation makes use of protein family classification‐driven rules. Unannotated proteins that satisfy a set of conditions for a particular rule can be annotated with the information appropriate for that rule. The approach leads to facile, accurate prediction and functional inference for uncharacterized proteins, allows systematic detection of genome annotation errors, and provides sensible propagation and standardization of protein annotation, including position‐specific sequence features, protein names and synonyms, and Gene Ontology terms. Rule‐based annotation will be discussed in the context of the PIRSF protein classification system, PIRNR Name Rule system, and the PIRSR Site Rule system.

The high-throughput genome projects have resulted in a rapid accumulation of predicted protein sequences, however, experimentally-verified information on protein function lags far behind. The common approach to inferring function of uncharacterized proteins based on sequence similarity to annotated proteins in sequence databases often results in over-identification, underidentification, or even misannotation. To facilitate accurate, consistent and rich functional annotation of proteins, PIR employs a classification-driven rule-based automated annotation method supported by a bioinformatics framework that provides data integration and associative analysis. The PIRSF classification system provides protein classification from superfamily to subfamily levels in a network structure to reflect evolutionary relationship of full-length proteins, including the preservation of domain architecture. Family-specific rules are manually defined to allow for conditional propagation of such annotation as functional site residues and protein names from characterized proteins to unknown proteins. The iProClass integration of protein family, function and structure data allows associative analyses of proteins. The integrative approach leads to novel prediction and functional inference for uncharacterized proteins, allows systematic detection of genome annotation errors, and provides sensible propagation and standardization of protein annotation.

The Protein Information Resource (PIR) is an integrated public resource of protein informatics. To facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors, PIR has extended its superfamily concept and developed the SuperFamily (PIRSF) classification system. Based on the evolutionary relationships of whole proteins, this classification system allows annotation of both specific biological and generic biochemical functions. The system adopts a network structure for protein classification from superfamily to subfamily levels. Protein family members are homologous (sharing common ancestry) and homeomorphic (sharing full-length sequence similarity with common domain architecture). The PIRSF database consists of two data sets, preliminary clusters and curated families. The curated families include family name, protein membership, parent-child relationship, domain architecture, and optional description and bibliography. PIRSF is accessible from the website at http://pir.georgetown.edu/pirsf/ for report retrieval and sequence classification. The report presents family annotation, membership statistics, cross-references to other databases, graphical display of domain architecture, and links to multiple sequence alignments and phylogenetic trees for curated families. PIRSF can be utilized to analyze phylogenetic profiles, to reveal functional convergence and divergence, and to identify interesting relationships between homeomorphic families, domains and structural classes.

E2020 (R,S)-1-benzyl-4-[(5,6-dimethoxy-1-indanon)-2-yl]methyl)piperidine hydrochloride is a piperidine-based acetylcholinesterase (AChE) inhibitor that was approved for the treatment of Alzheimer's disease in the United States. Structure-activity studies of this class of inhibitors have indicated that both the benzoyl containing functionality and the N-benzylpiperidine moiety are the key features for binding and inhibition of AChE. In the present study, the interaction of E2020 with cholinesterases (ChEs) with known sequence differences, was examined in more detail by measuring the inhibition constants with Torpedo AChE, fetal bovine serum AChE, human butyrylcholinesterase (BChE), and equine BChE. The basis for particular residues conferring selectivity was then confirmed by using site-specific mutants of the implicated residue in two template enzymes. Differences in the reactivity of E2020 toward AChE and BChE (200- to 400-fold) show that residues at the peripheral anionic site such as Asp74(72), Tyr72(70), Tyr124(121), and Trp286(279) in mammalian AChE may be important in the binding of E2020 to AChE. Site-directed mutagenesis studies using mouse AChE showed that these residues contribute to the stabilization energy for the AChE-E2020 complex. However, replacement of Ala277(Trp279) with Trp in human BChE does not affect the binding of E2020 to BChE. Molecular modeling studies suggest that E2020 interacts with the active-site and the peripheral anionic site in AChE, but in the case of BChE, as the gorge is larger, E2020 cannot simultaneously interact at both sites. The observation that the KI value for mutant AChE in which Ala replaced Trp286 is similar to that for wild-type BChE, further confirms our hypothesis.