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Hereditary transthyretin amyloidosis (ATTRv) is a rare, fatal, autosomal dominant disease with more than 140 mutations discovered. Three phenotypes of amyloid infiltration are neuropathy (ATTRv-PN), cardiopathy (ATTRv-CM), and neuropathy + cardiopathy (ATTRv-MIX). The lack of ATTR-specific biomarkers, difficulties in biopsy evidence, and limited known pathogenic mechanisms have made diagnosis difficult. Newly emerging noninvasive measures for monitoring progression and disease-modifying therapies have improved early diagnosis and patient management. Our research applies the latest technology, Data-Independent Acquisition-Based Quantitative Proteomics (DIA), to reveal comprehensive plasma protein profiles in the natural history of Chinese patients with hereditary transthyretin amyloidosis (ATTRv). We analyzed differentially expressed proteins (DEPs) in three phenotypes (ATTRv-PN, ATTRv-CM, and ATTRv-MIX). Serum samples were collected from a total of 18 patients (6 ATTRv-PN, 5 ATTRv-CM, and 7 ATTRv-MIX patients) and 20 healthy participants as a control group. Combined with the results of the proteomic and bioinformatic analyses, we found 30 DEPs and protein interaction networks clustered in KRT family proteins and DSC3 between ATTRv-PN and the control, which were rich in the estrogen signaling pathway and the cell adhesion molecule (CAM) pathway. This study demonstrates a global and significant proteomic profile in different stages of ATTRv. [ABSTRACT FROM AUTHOR]

More than seventy million protein sequences exist now, with just around 1% of their functions known. Multi-domain protein prediction is one of the problems in the Bioinformatics field, which was conducted to find the function of proteins. As a result, the researchers attempt to develop algorithms that predict protein functions based on their sequences. We might think of the tremendous rise in sequencing and structural genomics as providing us with a lot of data to expose the complicated sequence, structure, and functional correlations that exist in proteins. However, because of the critical functions that these macromolecules perform in biological mechanisms, acquiring a thorough understanding of their activity has recently emerged as a major challenge. Furthermore, multi-domain protein function prediction approaches are more efficient than methods that are fully processed based on protein sequencing. In this research, we used a model for predicting multi-domain protein function using a Fuzzy Convolutional Neural Network. In this research, we used a novel hybrid CNN and Fuzzy for sequence labelling, specifically to predict the function of the protein. Our hybrid model managed to outperform recent previous studies in terms of accuracy (95.02%) and performance on the UniProtKB dataset, and they showed significant improvements, in the execution time spent in the prediction process. It's crucial to keep your attention on this strategy. Further, study will be needed. [ABSTRACT FROM AUTHOR]

Biomedical information mining is increasingly recognized as a promising technique to accelerate drug discovery and development. Especially, integrative approaches which mine data from several (open) data sources have become more attractive with the increasing possibilities to programmatically access data through Application Programming Interfaces (APIs). The use of open data in conjunction with free, platform-independent analytic tools provides the additional advantage of flexibility, re-usability, and transparency. Here, we present a strategy for performing ligand-based in silico drug repurposing with the analytics platform KNIME. We demonstrate the usefulness of the developed workflow on the basis of two different use cases: a rare disease (here: Glucose Transporter Type 1 (GLUT-1) deficiency), and a new disease (here: COVID 19). The workflow includes a targeted download of data through web services, data curation, detection of enriched structural patterns, as well as substructure searches in DrugBank and a recently deposited data set of antiviral drugs provided by Chemical Abstracts Service. Developed workflows, tutorials with detailed step-by-step instructions, and the information gained by the analysis of data for GLUT-1 deficiency syndrome and COVID-19 are made freely available to the scientific community. The provided framework can be reused by researchers for other in silico drug repurposing projects, and it should serve as a valuable teaching resource for conveying integrative data mining strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Phosphatases play an essential role in the regulation of protein phosphorylation. Less abundant than kinases, many phosphatases are components of one or more macromolecular complexes with different substrate specificities and specific functionalities. The expert scientific curation of phosphatase complexes for the UniProt and Complex Portal databases supports the whole scientific community by collating and organising small‐ and large‐scale experimental data from the scientific literature into context‐specific central resources, where the data can be freely accessed and used to further academic and translational research. In this review, we discuss how the diverse biological functions of phosphatase complexes are presented in UniProt and the Complex Portal, and how understanding the biological significance of phosphatase complexes in Caenorhabditis elegans offers insight into the mechanisms of substrate diversity in a variety of cellular and molecular processes. [ABSTRACT FROM AUTHOR]

Abstract: Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and a database of GO term predictions for proteomes of several organisms in UniProt Knowledgebase (UniProtKB). UniGOPred provides function predictions for 514 molecular function (MF), 2909 biological process (BP), and 438 cellular component (CC) GO terms for each protein sequence. UniGOPred covers nearly the whole functionality spectrum in Gene Ontology system and it can predict both generic and specific GO terms. UniGOPred was run on CAFA2 challenge target protein sequences and it is categorized within the top 10 best performing methods for the molecular function category. In addition, the performance of UniGOPred is higher compared to the baseline BLAST classifier in all categories of GO. UniGOPred predictions are compared with UniProtKB/TrEMBL database annotations as well. Furthermore, the proposed tool's ability to predict negatively associated GO terms that defines the functions that a protein does not possess, is discussed. UniGOPred annotations were also validated by case studies on PTEN protein variants experimentally and on CHD8 protein variants with literature. UniGOPred protein functional annotation system is available as an open access tool at http://cansyl.metu.edu.tr/UniGOPred.html. [ABSTRACT FROM AUTHOR]

The cellulases from different cellulolytic organisms have evolutionary relationships, which range from single-celled prokaryotes to the complex eukaryotes of the living world. This in silico analysis revealed the presence of a conserved cellulase domain along with evolutionary relationships among cellulases from several species of Archaea, Bacteria, and Eukarya. The amino acid sequences of cellulases from Archaea and Bacteria showed closer identity with their domain or phylum members that provided insights into convergent and divergent evolution of cellulases from other enzymes with different substrate specificities. Evolutionary relatedness was also observed in phylogenetic trees among a number of cellulase sequences of diverse taxa. In cellulases, propensity for alanine, glycine, leucine, serine, and threonine was high, but low for cysteine, histidine, and methionine. Catalytic aspartic acid had a higher propensity than glutamic acid, and both were involved in regular expression patterns. Characteristic group and multigroup-specific conserved signature indels located in the catalytic domains of cellulases were observed that further clarified evolutionary relationships. These indels can be distinctive molecular tools for understanding phylogeny and identification of unknown cellulolytic species of common evolutionary descent in different environments. [ABSTRACT FROM AUTHOR]

With the advent of biological research in the field of environmental science, several microbes were found to act as the most important biodegradable molecules. Maleylacetate reductase being a member of oxidoreductase is mostly found in Pseudomonas species. This enzyme participates in three metabolic pathways: gamma-hexachlorocyclohexane degradation, benzoate degradation which are higher alkyl compounds involved in chemical pollutions in metropolitan cities. Determining its three-dimensional structure will lead to the structure function analysis and also might be helpful for designing receptors for the degradation of new chemical compounds. For this purpose, the amino acid sequence of the protein had been imported from UniProtKB database and the template was searched in BLASTp2.2.27. This template was then used for the prediction of the three dimensional structure of the protein by using SWISS-MODEL Workspace. The predicted model was then validated using SAVES server which gave an almost result for better prediction.

With the advent of biological research in the field of environmental science, several microbes were found to act as the most important biodegradable molecules. Maleylacetate reductase being a member of oxidoreductase is mostly found in Pseudomonas species. This enzyme participates in three metabolic pathways: gamma-hexachlorocyclohexane degradation, benzoate degradation which are higher alkyl compounds involved in chemical pollutions in metropolitan cities. Determining its three-dimensional structure will lead to the structure function analysis and also might be helpful for designing receptors for the degradation of new chemical compounds. For this purpose, the amino acid sequence of the protein had been imported from UniProtKB database and the template was searched in BLASTp2.2.27. This template was then used for the prediction of the three dimensional structure of the protein by using SWISS-MODEL Workspace. The predicted model was then validated using SAVES server which gave an almost result for better prediction. [ABSTRACT FROM AUTHOR]

Cellulase plays an important role in modern industry and holds great potential in biofuel production. Many different types of organisms produce cellulase, which go through secretory pathways to reach the extracellular space, where enzymatic reactions take place. Secretory pathways in various cells have been the focus of many research fields; however, there are few studies on secretory pathways of cellulases in the literature. It is therefore necessary and important to review the current knowledge on the secretory pathways of cellulases. In this mini-review, we address the subcellular locations of cellulases in different organisms, discuss the secretory pathways of cellulases in different organisms, and examine the secretory mechanisms of cellulases. These sections start with a description of general secreted proteins, advance to the situation of cellulases, and end with the knowledge of cellulases, as documented in UniProt Knowledgebase (UniProtKB). Finally, gaps in existing knowledge are highlighted, which may shed light on future studies for biofuel engineering. [ABSTRACT FROM AUTHOR]

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website (). It also evokes precautions that are necessary for successful predictions and extrapolations. [ABSTRACT FROM AUTHOR]

UniprotR is a software package designed to easily retrieve, cluster and visualize protein data from UniProt knowledgebase (UniProtKB) using R language. The package is implemented mainly to process, parse and illustrate proteomics data in a handy and time-saving approach allowing researchers to summarize all required protein information available at UniProtKB in a readable data frame, Excel CSV file, and/or graphical output. UniprotR generates a set of graphics including gene ontology, chromosomal location, protein scoring and status, protein networking, sequence phylogenetic tree, and physicochemical properties. In addition, the package supports clustering of proteins based on primary gene name or chromosomal location, facilitating additional downstream analysis. In this work, we implemented a robust package for retrieving and visualizing information from multiple sources such UniProtKB, SWISS-MODEL, and STRING. UniprotR Contains functions that enable retrieving and cluster data in a handy way and visualize data in publishable graphs to facilitate researcher's work and fulfill their needs. UniprotR will aid in saving time for downstream data analysis instead of manual time consuming data analysis. UniprotR released as free open source code under the license of GPLv3, and available in CRAN (The Comprehensive R Archive Network) and GitHub. (https://cran.r-project.org/web/packages/UniprotR/index.html). (https://github.com/Proteomicslab57357/UniprotR). Unlabelled Image • Repacking data from UniProtKB, SWISS-MODEL, and STRING • Clustering data retrieved from multiple sources based on multiple factors such gene name, chromosomal location, and multiple sequence alignment in a handy way • Visualizing data in publishable graphs to facilitate researcher's work • Rapid downstream data analysis and illustration of researcher's significant candidates. [ABSTRACT FROM AUTHOR]

Transthyretin (TTR), normally a plasma circulating protein, can become misfolded and aggregated, ultimately leading to extracellular deposition of amyloid fibrils usually targeted to heart or nerve tissues. Referred to as TTR-associated amyloidoses (ATTR), this group of diseases is frequently life threatening and fatal if untreated. ATTR, caused by amyloid-forming variant TTR proteins (ATTRm) that arise from point mutations in the TTR gene, were classically referred to as familial amyloid cardiomyopathy (FAC) or familial amyloid polyneuropathy (FAP), reflecting the clinical phenotype. FAC and FAP are pathologies that can be challenging to diagnose as there are no definitive biomarkers of disease; moreover, disease-specific measures of progression are lacking, and treatment options are limited. Thus, the discovery of sensitive and specific indicators of disease has the potential to improve recognition, enable accurate measurement of amyloid progression and response to treatment, and reveal key information regarding FAC and FAP pathobiological mechanisms. In this study, the goal was to investigate serum proteomic features unique to FAC and FAP types of ATTRm. Multiple-reaction monitoring mass spectrometry (MRM-MS), a powerful technique in profiling proteomes, was used to measure the serum concentrations of 160 proteins in samples from FAC and FAP patients. Results were compared to data from healthy control sera obtained from individuals matched to age (≥60 years), gender (male), and race (Caucasian). Proteomic analyses of ATTRm (FAC and FAP) and control samples showed significant concentration differences in 107 of 192 (56%) of the serum proteins that were studied. In comparing FAC to FAP, differences in concentrations as well as interactions and functions of several proteins were identified as unique to each disease; significantly lower levels of TTR were specific to FAC, but not to FAP. Annotated functional clustering identified extracellular region, signal, and signal peptide as terms common to FAC and FAP. Conversely, disulfide bond was unique to FAC; secreted, glycosylation site: N-linked, glycosylation, glycoprotein, polymorphism, and sequence variant were associated solely with FAP. Predicted protein-protein associations in FAC were seen for reaction, binding, and activation processes; no associations were found in FAP. This study demonstrates significant proteomic differences between ATTRm patient and control sera, as well as ATTRm phenotype-associated variations in the circulating levels of several proteins including TTR. The identification of serum proteins unique to FAC and FAP may have diagnostic and prognostic utility and could possibly provide important clues about disease mechanisms.

Transthyretin-associated forms of cardiac amyloidosis are fatal protein misfolding diseases that can be inherited (ATTRm) or acquired (ATTRwt). An accurate diagnosis of ATTR amyloidosis can be challenging as biopsy evidence, usually from the affected organ, is required. Precise biomarkers for ATTR disease identification and monitoring are undiscovered, disease-specific therapeutic options are needed, and the current understanding of ATTR molecular pathogenesis is limited. The aim of this study was to investigate and compare the serum proteomes in ATTRm and ATTRwt cardiac amyloidosis to identify differentially expressed blood proteins that were disease-specific. Using multiple-reaction monitoring mass spectrometry (MRM-MS), the concentrations of 160 proteins were analyzed in serum samples from ATTRm and ATTRwt patients, and a healthy control group. Patient and control sera were matched to age (≥60 years), gender (male), and race (Caucasian). The circulating concentrations of 123/160 proteins were significantly different in patient vs control sera; TTR and retinol-binding protein (RBP4) levels were significantly decreased (p < 0.03) in ATTRm compared to controls. In ATTRm, 14/123 proteins were identified as unique to that group and found generally to be lower than controls; moreover, the concentrations of RBP4 and 6 other proteins in this group were significantly different (p < 0.04) compared to ATTRwt. Predicted interactions among the 14 proteins unique to ATTRm were categorized as reaction and binding associations. Alternatively, 27 proteins were found to be unique to ATTRwt with associated interactions defined as activation, catalysis, and inhibition, in addition to reaction and binding. This study demonstrates significant proteomic differences between ATTR patient and control sera, and disease-associated variations in circulating levels of several proteins including TTR and RBP4. The identification of serum proteins unique to ATTRm and ATTRwt cardiac amyloidosis may have diagnostic and prognostic utility, and may provide important clues about disease mechanisms.

Protein kinases form one of the largest protein families and are found in all species, from viruses to humans. They catalyze the reversible phosphorylation of proteins, often modifying their activity and localization. They are implicated in virtually all cellular processes and are one of the most intensively studied protein families. In recent years, they have become key therapeutic targets in drug development as natural mutations affecting kinase genes are the cause of many diseases. The vast amount of data contained in the primary literature and across a variety of biological data collections highlights the need for a repository where this information is stored in a concise and easily accessible manner. The UniProt Knowledgebase meets this need by providing the scientific community with a comprehensive, high-quality and freely accessible resource of protein sequence and functional information. Here, we describe the expert curation process for kinases, focusing on the Caenorhabditis elegans kinome. The C. elegans kinome is composed of 438 kinases and almost half of them have been functionally characterized, highlighting that C. elegans is a valuable and versatile model organism to understand the role of kinases in biological processes., (© 2017 The Author(s).)

Computer analysis of protein maps obtained from the separation of proteins with two-dimensional polyacrylamide gel electrophoresis (2-DE), in combination with mass spectrometry (MS) analysis and selected bioinformatic tools is used in the strategy for the identification of oat proteins. In proteomic research the most often used MS technique is the combination of ion sources: matrix-assisted laser desorption/ionization (MALDI) and the analyzer of the time of flight (TOF), i.e., MALDI-TOF MS.This chapter describes the possibilities of the use of selected bioinformatic tools (UniProtKB database, ProtParam, Compute pI/MW programs) for initial identification of separated oat proteins (especially prolamin fractions) with the 2-DE technique. Also the procedure of preparation of samples obtained from cut out protein spots for analysis with the MALDI-TOF MS and peptide mass fingerprinting (PMF) technique is presented.Among oat prolamins separated with the 2-DE technique (see Chapter 17 ), 13 protein spots are considered to be the most characteristic (range of MW 27.0-34.6 kDa, pI 5.7-7.6) for this fraction of proteins. Among them there are four protein spots (MW 27.0-28.0 kDa) and two spots (MW 31.4-32.1 kDa) which can correspond to avenins (Accession numbers (AC) in UniProtKB: L0L5I0, I4EP88, I4EP64, L0L4I8 and F2Q9W5, L0L6J0, respectively).

A fundamental goal of medical genetics is the accurate prediction of genotype-phenotype correlations. As an approach to develop more accurate in silico tools for prediction of disease-causing mutations of structural proteins, we present a gene- and disease-specific prediction tool based on a large systematic analysis of missense mutations from hemophilia A (HA) patients. Our HA-specific prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly available prediction software. In contrast to those methods, its performance is not limited to non-synonymous mutations. Given the role of synonymous mutations in disease and drug codon optimization, we propose that utilizing a gene- and disease-specific method can be highly useful to make functional predictions possible even for synonymous mutations. Incorporating computational metrics at both nucleotide and amino acid levels along with multiple protein sequence/structure alignment significantly improved the predictive performance of our tool. HApredictor is freely available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/HA&#95;Predict/index.htm., (© 2013. Published by Elsevier Ltd. All rights reserved.)