By looking for a lack of homologs in a reference database of 27 well-annotated proteomes of primates and 52 well-annotated proteomes of other mammals, 170 putative human-specific proteins were identified. While most of them are deemed uncertain, 2 are known at the protein level and 23 at the transcript level, according to UniProt. Interestingly, 23 of these 25 proteins are found to be encoded or to have close homologs in an open reading frame of a long noncoding human RNA. However, half of them are predicted to be at least 80% globular, with a single structural domain, according to IUPred, and with at least 80% of ordered residues, according to flDPnn. Strikingly, there is a near-complete lack of structural knowledge about these proteins, with no tertiary structure presently available in the Protein Data Bank and a fair prediction for one of them in the AlphaFold Protein Structure Database. Moreover, knowledge about the function of these possibly key proteins remains scarce.

UNASSIGNED: Multiple sclerosis (MS) is a neurodegenerative disease characterized by inflammation and demyelination of neurons. There is evidence to suggest that level of a neurotransmitter gamma-aminobutyric acid (GABA), due to the degradation by γ-aminobutyric acid transaminase (GABAT), is reduced in certain areas of the brain in MS patients. MS is always accompanied by gut bacteria dysbiosis. In healthy individuals, Faecalibacterium sp. while in MS patients A. calcoaceticus, Clostridium sp. and S. typhimurium are found abundantly. Although all these microbes produce GABAT but only in MS patients this enzyme significantly degrades GABA.
UNASSIGNED: Present study is an attempt to characterize the GABAT protein sequences of these bacteria.
UNASSIGNED: Sequences of GABAT protein were retrieved from Uniprot database. Sequences were analyzed by Protparam, Gneg-mPLoc, SOSUI, PFP-FunDSeqE, Pepwheel program, PROTEUS and Alphafold and SAVES servers, MEME suite and HDOCK server.
UNASSIGNED: In healthy individuals gastrointestinal tract (GIT) bacteria, GABAT protein was present in inner-membrane with α helix content (61 and 62%) and β sheet content (5%), 4-helical cytokines functional domains. It has greater number of B-cell epitopes and more complex 3D configuration as compared to MS patients GIT bacterial enzymes.
UNASSIGNED: Present study might enable us to modify the GABAT encoding gene and enzyme through site-directed mutagenesis in pathogenic bacteria thus reducing their potential of causing MS.

Proteomics in respiratory allergic diseases has such a battery of techniques and programs that one would almost think there is nothing impossible to find, invent or mold. All the resources that we document here are involved in solving problems in allergic diseases, both diagnostic and prognostic treatment, and immunotherapy development. The main perspectives, according to this version, are in three strands and/or a lockout immunological system: (1) Blocking the diapedesis of the cells involved, (2) Modifications and blocking of paratopes and epitopes being understood by modifications to antibodies, antagonisms, or blocking them, and (3) Blocking FcεRI high-affinity receptors to prevent specific IgEs from sticking to mast cells and basophils. These tools and targets in the allergic landscape are, in our view, the prospects in the field. However, there are still many allergens to identify, including some homologies between allergens and cross-reactions, through the identification of structures and epitopes. The current vision of using proteomics for this purpose remains a constant; this is also true for the basis of diagnostic and controlled systems for immunotherapy. Ours is an open proposal to use this vision for treatment.

The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (www.uniprot.org) is the world\'s leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information and presents a summary of experimentally verified, or computationally predicted, functional information added by our expert biocuration team for each protein in the proteome. Researchers in the field of mass spectrometry-based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases.

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data (UniProt Consortium, 2023). The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. Along with various datasets that you can search, UniProt provides four main tools. These are the \"BLAST\" tool for sequence similarity searching, the \"Align\" tool for multiple sequence alignment, the \"Peptide Search\" tool for retrieving proteins containing a short peptide sequence, and the \"Retrieve/ID Mapping\" tool for using a list of identifiers to retrieve UniProt Knowledgebase (UniProtKB) proteins and to convert database identifiers from UniProt to external databases or vice versa. This article provides four basic protocols and seven alternate protocols for using UniProt tools. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Basic local alignment search tool (BLAST) in UniProt Alternate Protocol 1: BLAST through UniProt text search results pages Alternate Protocol 2: BLAST through UniProt basket Basic Protocol 2: Multiple sequence alignment in UniProt Alternate Protocol 3: Align tool through UniProt results pages and entry pages Alternate Protocol 4: Align tool through UniProt basket Basic Protocol 3: Peptide search in UniProt Basic Protocol 4: Batch retrieval and ID mapping in UniProt Alternate Protocol 5: Retrieve/ID Mapping tool through UniProt text search results pages and BLAST and Align results pages Alternate Protocol 6: Retrieve/ID Mapping tool through UniProt basket Alternate Protocol 7: Retrieve/ID Mapping tool through UniProt search box.

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt website receives about 800,000 unique visitors per month and is the primary means to access UniProt. It provides 10 searchable datasets and four main tools. The key UniProt datasets are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), the UniProt Archive (UniParc), and protein sets for completely sequenced genomes (Proteomes). Other supporting datasets include information about proteins that is present in UniProtKB protein entries, such as literature citations, taxonomy, and subcellular locations, among others. This article focuses on how to use UniProt datasets. The first basic protocol describes navigation and searching mechanisms for the UniProt datasets, and two additional protocols build on the first protocol to describe advanced search and query building. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Searching UniProt datasets Basic Protocol 2: Advanced search and query building Basis Protocol 3: Adding parameters using advanced search.

There are 28 unique human members of the homologous to E6AP C-terminus (HECT) E3 ubiquitin ligase family. Each member of the HECT E3 ubiquitin ligases contains a conserved bilobal HECT domain of approximately 350 residues found near their C-termini that is responsible for their respective ubiquitylation activities. Recent studies have begun to elucidate specific roles that each HECT E3 ubiquitin ligase has in various cancers, age-induced neurodegeneration, and neurological disorders. New structural models have been recently released for some of the HECT E3 ubiquitin ligases, but many HECT domain structures have yet to be examined due to chronic insolubility and/or protein folding issues. Building on these recently published structural studies coupled with our in-house experiments discussed in the present study, we suggest that the addition of ∼50 conserved residues preceding the N-terminal to the current UniProt defined boundaries of the HECT domain are required for isolating soluble, stable, and active HECT domains. We show using in silico bioinformatic analyses coupled with secondary structural prediction software that this predicted N-terminal α-helix found in all 28 human HECT E3 ubiquitin ligases forms an obligate amphipathic α-helix that binds to a hydrophobic pocket found within the HECT N-terminal lobe. The present study brings forth the proposal to redefine the residue boundaries of the HECT domain to include this N-terminal extension that will likely be critical for future biochemical, structural, and therapeutic studies on the HECT E3 ubiquitin ligase family.

Public phosphorylation databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available mass spectrometry (MS) data. However, there is no database-level control for false discovery of sites, likely leading to the overestimation of true phosphosites. By profiling the human phosphoproteome, we estimate the false discovery rate (FDR) of phosphosites and predict a more realistic count of true identifications. We rank sites into phosphorylation likelihood sets and analyze them in terms of conservation across 100 species, sequence properties, and functional annotations. We demonstrate significant differences between the sets and develop a method for independent phosphosite FDR estimation. Remarkably, we report estimated FDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr) sites, respectively, that are supported by only a single piece of identification evidence─the majority of sites in PSP. We estimate that around 62 000 Ser, 8000 Thr, and 12 000 Tyr phosphosites in the human proteome are likely to be true, which is lower than most published estimates. Furthermore, our analysis estimates that 86 000 Ser, 50 000 Thr, and 26 000 Tyr phosphosites are likely false-positive identifications, highlighting the significant potential of false-positive data to be present in phosphorylation databases.

The effectiveness of any proteomics database search depends on the theoretical candidate information contained in the protein database. Unfortunately, candidate entries from protein databases such as UniProt rarely contain all the post-translational modifications (PTMs), disulfide bonds, or endogenous cleavages of interest to researchers. These omissions can limit discovery of novel and biologically important proteoforms. Conversely, searching for a specific proteoform becomes a computationally difficult task for heavily modified proteins. Both situations require updates to the database through user-annotated entries. Unfortunately, manually creating properly formatted UniProt Extensible Markup Language (XML) files is tedious and prone to errors. ProSight Annotator solves these issues by providing a graphical interface for adding user-defined features to UniProt-formatted XML files for better informed proteoform searches. It can be downloaded from http://prosightannotator.northwestern.edu.

The parasite species of genus Plasmodium causes Malaria, which remains a major global health problem due to parasite resistance to available Antimalarial drugs and increasing treatment costs. Consequently, computational prediction of new Antimalarial compounds with novel targets in the proteome of Plasmodium sp. is a very important goal for the pharmaceutical industry. We can expect that the success of the pre-clinical assay depends on the conditions of assay per se, the chemical structure of the drug, the structure of the target protein to be targeted, as well as on factors governing the expression of this protein in the proteome such as genes (Deoxyribonucleic acid, DNA) sequence and/or chromosomes structure. However, there are no reports of computational models that consider all these factors simultaneously. Some of the difficulties for this kind of analysis are the dispersion of data in different datasets, the high heterogeneity of data, etc. In this work, we analyzed three databases ChEMBL (Chemical database of the European Molecular Biology Laboratory), UniProt (Universal Protein Resource), and NCBI-GDV (National Center for Biotechnology Information-Genome Data Viewer) to achieve this goal. The ChEMBL dataset contains outcomes for 17,758 unique assays of potential Antimalarial compounds including numeric descriptors (variables) for the structure of compounds as well as a huge amount of information about the conditions of assays. The NCBI-GDV and UniProt datasets include the sequence of genes, proteins, and their functions. In addition, we also created two partitions (cassayj = caj and cdataj = cdj) of categorical variables from theChEMBL dataset. These partitions contain variables that encode information about experimental conditions of preclinical assays (caj) or about the nature and quality of data (cdj). These categorical variables include information about 22 parameters of biological activity (ca0), 28 target proteins (ca1), and 9 organisms of assay (ca2), etc. We also created another partition of (cprotj = cpj) including categorical variables with biological information about the target proteins, genes, and chromosomes. These variables cover32 genes (cp0), 10 chromosomes (cp1), gene orientation (cp2), and 31 protein functions (cp3). We used a Perturbation-Theory Machine Learning Information Fusion (IFPTML) algorithm to map all this information (from three databases) into and train a predictive model. Shannon\'s entropy measure Shk (numerical variables) was used to quantify the information about the structure of drugs, protein sequences, gene sequences, and chromosomes in the same information scale. Perturbation Theory Operators (PTOs) with the form of Moving Average (MA) operators have been used to quantify perturbations (deviations) in the structural variables with respect to their expected values for different subsets (partitions) of categorical variables. We obtained three IFPTML models using General Discriminant Analysis (GDA), Classification Tree with Univariate Splits (CTUS), and Classification Tree with Linear Combinations (CTLC). The IFPTML-CTLC presented the better performance with Sensitivity Sn(%) = 83.6/85.1, and Specificity Sp(%) = 89.8/89.7 for training/validation sets, respectively. This model could become a useful tool for the optimization of preclinical assays of new Antimalarial compounds vs. different proteins in the proteome of Plasmodium.