E-PROT-1
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: PXD000561MSV000079514PAe005095PAe004968PAe004965PAe004962PAe005041PAe005027PAe004741PAe004746PAe004935PAe004880PAe004896PAe004876PAe004953PAe004999PAe004826PAe005001PAe005004PAe004974PAe005097PAe004744PAe005076PAe005042PAe005019PAe005031PAe004740PAe004727PAe004846PAe005035PAe005073PAe004904PAe005015PAe005038PAe004834PAe004737PAe004856PAe004957PAe004850PAe004925PAe004735PAe004950PAe004824PAe005055PAe005098PAe004729PAe005009PAe004833PAe004733PAe005057PAe004726PAe004831PAe005016PAe004724PAe004728PAe004736PAe004988PAe005029PAe005014PAe004730PAe004914PAe004966PAe004828PAe004747PAe004743PAe004973PAe004723PAe004825PAe005059PAe004963PAe005078PAe004738PAe005003PAe004901PAe004991PAe004827PAe004734PAe004731PAe004969MODEL1909260003MODEL1909260004MODEL1909260005MODEL1909260006
PROVIDER: E-PROT-1 | ExpressionAtlas | 2025-01-21
REPOSITORIES: ExpressionAtlas
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| E-PROT-1-analysis-methods.tsv | Tabular | |||
| E-PROT-1-configuration.xml | Xml | |||
| E-PROT-1-factors.xml | Xml | |||
| E-PROT-1-zoomifications-log.tsv | Tabular | |||
| E-PROT-1.condensed-sdrf.tsv | Tabular |
Items per page: 5 1 - 5 of 12 |
Shared Molecules
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Similar Datasets
Project description:Animal toxins are of interest to a wide range of scientists, due to their numerous applications in pharmacology, neurology, hematology, medicine, and drug research. This, and to a lesser extent the development of new performing tools in transcriptomics and proteomics, has led to an increase in toxin discovery. In this context, providing publicly available data on animal toxins has become essential. The UniProtKB/Swiss-Prot Tox-Prot program (http://www.uniprot.org/program/Toxins) plays a crucial role by providing such an access to venom protein sequences and functions from all venomous species. This program has up to now curated more than 5000 venom proteins to the high-quality standards of UniProtKB/Swiss-Prot (release 2012_02). Proteins targeted by these toxins are also available in the knowledgebase. This paper describes in details the type of information provided by UniProtKB/Swiss-Prot for toxins, as well as the structured format of the knowledgebase.
| S-EPMC3393831 | biostudies-literature
Project description:BackgroundThe accuracy of protein 3D structure prediction has been dramatically improved with the help of advances in deep learning. In the recent CASP14, Deepmind demonstrated that their new version of AlphaFold (AF) produces highly accurate 3D models almost close to experimental structures. The success of AF shows that the multiple sequence alignment of a sequence contains rich evolutionary information, leading to accurate 3D models. Despite the success of AF, only the prediction code is open, and training a similar model requires a vast amount of computational resources. Thus, developing a lighter prediction model is still necessary.ResultsIn this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein language models. An MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions for a given MSA. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14.ConclusionThese results imply that A-Prot accurately captures the evolutionary and structural information of proteins with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein property prediction methods.
| S-EPMC8925138 | biostudies-literature
Project description:The ever-growing global health threat of antibiotic resistance is compelling researchers to explore alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging as a promising solution to fill this need. Naturally occurring AMPs are produced by all forms of life as part of the innate immune system. High-throughput bioinformatics tools have enabled fast and large-scale discovery of AMPs from genomic, transcriptomic, and proteomic resources of selected organisms. Public protein sequence databases, comprising over 200 million records and growing, serve as comprehensive compendia of sequences from a broad range of source organisms. Yet, large-scale in silico probing of those databases for novel AMP discovery using modern deep learning techniques has rarely been reported. In the present study, we propose an AMP mining workflow to predict novel AMPs from the UniProtKB/Swiss-Prot database using the AMP prediction tool, AMPlify, as its discovery engine. Using this workflow, we identified 8008 novel putative AMPs from all eukaryotic sequences in the database. Focusing on the practical use of AMPs as suitable antimicrobial agents with applications in the poultry industry, we prioritized 40 of those AMPs based on their similarities to known chicken AMPs in predicted structures. In our tests, 13 out of the 38 successfully synthesized peptides showed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. AMPlify and the companion scripts supporting the AMP mining workflow presented herein are publicly available at https://github.com/bcgsc/AMPlify.
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Project description:We present RVDB-prot, a database corresponding to the protein equivalent of the nucleic acid reference virus database RVDB. Protein databases can be helpful to perform more sensitive protein sequence comparisons. Similarly to its homologous public repository, RVDB-prot aims to provide reliable and accurately annotated unique entries, while including also an Hidden Markov Model (HMM) protein profiles database for distant protein searching.
| S-EPMC7492780 | biostudies-literature
Project description:Proline acquired via specific transporters can serve as a proteinogenic substrate, carbon and nitrogen source, or osmolyte. Previous reports have documented that Staphylococcus aureus, a major community and nosocomial pathogen, encodes at least four proline transporters, PutP, OpuC, OpuD, and ProP. A combination of genetic approaches and 3H-proline transport assays reveal that a previously unrecognized transporter, ProT, in addition to PutP, are the major proline transporters in S. aureus. Complementation experiments using constitutively expressed non-cognate promoters found that proline transport via OpuD, OpuC, and ProP is minimal. Both proline biosynthesis from arginine and proline transport via ProT are critical for growth when S. aureus is grown under conditions of high salinity. Further, proline transport mediated by ProT or PutP are required for growth in media with and without glucose, indicating both transporters function to acquire proline for proteinogenic purposes in addition to acquisition of proline as a carbon/nitrogen source. Lastly, inactivation of proT and putP resulted in a significant reduction (5 log10) of bacterial burden in murine skin-and-soft tissue infection and bacteremia models, suggesting that proline transport is required to establish a S. aureus infection.
| S-EPMC9886301 | biostudies-literature
Project description:The SWISS-PROT protein knowledgebase (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) connects amino acid sequences with the current knowledge in the Life Sciences. Each protein entry provides an interdisciplinary overview of relevant information by bringing together experimental results, computed features and sometimes even contradictory conclusions. Detailed expertise that goes beyond the scope of SWISS-PROT is made available via direct links to specialised databases. SWISS-PROT provides annotated entries for all species, but concentrates on the annotation of entries from human (the HPI project) and other model organisms to ensure the presence of high quality annotation for representative members of all protein families. Part of the annotation can be transferred to other family members, as is already done for microbes by the High-quality Automated and Manual Annotation of microbial Proteomes (HAMAP) project. Protein families and groups of proteins are regularly reviewed to keep up with current scientific findings. Complementarily, TrEMBL strives to comprise all protein sequences that are not yet represented in SWISS-PROT, by incorporating a perpetually increasing level of mostly automated annotation. Researchers are welcome to contribute their knowledge to the scientific community by submitting relevant findings to SWISS-PROT at swiss-prot@expasy.org.
| S-EPMC165542 | biostudies-literature
Project description:The UniProt knowledgebase, UniProtKB, is the main product of the UniProt consortium. It consists of two sections, UniProtKB/Swiss-Prot, the manually curated section, and UniProtKB/TrEMBL, the computer translation of the EMBL/GenBank/DDBJ nucleotide sequence database. Taken together, these two sections cover all the proteins characterized or inferred from all publicly available nucleotide sequences. The Plant Proteome Annotation Program (PPAP) of UniProtKB/Swiss-Prot focuses on the manual annotation of plant-specific proteins and protein families. Our major effort is currently directed towards the two model plants Arabidopsis thaliana and Oryza sativa. In UniProtKB/Swiss-Prot, redundancy is minimized by merging all data from different sources in a single entry. The proposed protein sequence is frequently modified after comparison with ESTs, full length transcripts or homologous proteins from other species. The information present in manually curated entries allows the reconstruction of all described isoforms. The annotation also includes proteomics data such as PTM and protein identification MS experimental results. UniProtKB and the other products of the UniProt consortium are accessible online at www.uniprot.org.
| S-EPMC2689360 | biostudies-literature
Project description:DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97-9.52% in ACC and 0.08-0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83-16.63% in terms of ACC and 0.02-0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public.
| S-EPMC4058174 | biostudies-literature
Project description:The mutation-induced changes across protein-protein interfaces have often been observed to lead to severe diseases. Therefore, several computational tools have been developed to predict the impact of such mutations. Among these tools, FoldX and EvoEF1 stand out as fast and accurate alternatives. Expanding on the capabilities of these tools, we have developed the PROT-ON (PROTein-protein interface mutatiONs) framework, which aims at delivering the most critical protein interface mutations that can be used to design new protein binders. To realize this aim, PROT-ON takes the 3D coordinates of a protein dimer as an input. Then, it probes all possible interface mutations on the selected protein partner with EvoEF1 or FoldX. The calculated mutational energy landscape is statistically analyzed to find the most enriching and depleting mutations. Afterward, these extreme mutations are filtered out according to stability and optionally according to evolutionary criteria. The final remaining mutation list is presented to the user as the designer mutation set. Together with this set, PROT-ON provides several residue- and energy-based plots, portraying the synthetic energy landscape of the probed mutations. The stand-alone version of PROT-ON is deposited at https://github.com/CSB-KaracaLab/prot-on. The users can also use PROT-ON through our user-friendly web service http://proton.tools.ibg.edu.tr:8001/ (runs with EvoEF1 only). Considering its speed and the range of analysis provided, we believe that PROT-ON presents a promising means to estimate designer mutations.
| S-EPMC10018488 | biostudies-literature
Project description:BackgroundDNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods.ResultsIn this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.ConclusionsOur method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA. In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.
| S-EPMC4165999 | biostudies-literature