Structural genomics.
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Amino Acid Sequence↗
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Computational tools for analysing structural changes in proteins in solution.
Many important structural changes in proteins involve long-time dynamics, which are outside the timescale presently accessible by a straightforward integration of Newton's equations of motion. This problem is addressed with minimisation-based algorithms, which are applied on possible reaction pathways using atomic-detail models. For reasons of efficiency, an implicit treatment of solvent is imperative. We present the charge reparameterisation protocol, which is a method that approximates the interaction energies obtained by a numerical solution of the Poisson-Boltzmann equation. Furthermore, we present a number of methods that can be used to compute possible reaction pathways associated with a particular conformational change. Two of them, the self-penalty walk and the nudged elastic band method, define an objective function, which is minimised to find optimal paths. A third method, conjugate peak refinement, is a heuristic method, which finds minimum energy paths without the use of an explicit objective function. Finally, we discuss problems and limitations with these methods and give a perspective on future research.
Algorithms↗
[Trends in interactomics].
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Computational Biology↗
Combining experimental and predicted datasets for determination of the subcellular location of proteins in Arabidopsis.
Substantial experimental datasets defining the subcellular location of Arabidopsis (Arabidopsis thaliana) proteins have been reported in the literature in the form of organelle proteomes built from mass spectrometry data (approximately 2,500 proteins). Subcellular location for specific proteins has also been published based on imaging of chimeric fluorescent fusion proteins in intact cells (approximately 900 proteins). Further, the more diverse history of biochemical determination of subcellular location is stored in the entries of the Swiss-Prot database for the products of many Arabidopsis genes (approximately 1,800 proteins). Combined with the range of bioinformatic targeting prediction tools and comparative genomic analysis, these experimental datasets provide a powerful basis for defining the final location of proteins within the wide variety of subcellular structures present inside Arabidopsis cells. We have analyzed these published experimental and prediction data to answer a range of substantial questions facing researchers about the veracity of these approaches to determining protein location and their interrelatedness. We have merged these data to form the subcellular location database for Arabidopsis proteins (SUBA), providing an integrated understanding of protein location, encompassing the plastid, mitochondrion, peroxisome, nucleus, plasma membrane, endoplasmic reticulum, vacuole, Golgi, cytoskeleton structures, and cytosol (www.suba.bcs.uwa.edu.au). This includes data on more than 4,400 nonredundant Arabidopsis protein sequences. We also provide researchers with an online resource that may be used to query protein sets or protein families and determine whether predicted or experimental location data exist; to analyze the nature of contamination between published proteome sets; and/or for building theoretical subcellular proteomes in Arabidopsis using the latest experimental data.
Arabidopsis↗
Clinical infrastructures to support proteomic studies of tissue and fluids in breast cancer.
The Danish Breast Cancer Cooperative Group (DBCG) was established in 1977 with the aim to ensure optimal breast cancer diagnostics and therapeutic modalities on a nationwide basis. DBCG was organized in such a way so it represents a broad interdisciplinary collaboration with established clinical databases and biobanks. This review summarizes the infrastructures, such as those of the DBCG, that are required to facilitate translational research studies aiming at further diagnostic and therapeutic improvements through interactions directed at prevention, early diagnosis, and treatment of primary breast cancer.
Body Fluids↗
The predicted candidates of Arabidopsis plastid inner envelope membrane proteins and their expression profiles.
Plastid envelope proteins from the Arabidopsis nuclear genome were predicted using computational methods. Selection criteria were: first, to find proteins with NH(2)-terminal plastid-targeting peptides from all annotated open reading frames from Arabidopsis; second, to search for proteins with membrane-spanning domains among the predicted plastidial-targeted proteins; and third, to subtract known thylakoid membrane proteins. Five hundred forty-one proteins were selected as potential candidates of the Arabidopsis plastid inner envelope membrane proteins (AtPEM candidates). Only 34% (183) of the AtPEM candidates could be assigned to putative functions based on sequence similarity to proteins of known function (compared with the 69% function assignment of the total predicted proteins in the genome). Of the 183 candidates with assigned functions, 40% were classified in the category of "transport facilitation," indicating that this collection is highly enriched in membrane transporters. Information on the predicted proteins, tissue expression data from expressed sequence tags and microarrays, and publicly available T-DNA insertion lines were collected. The data set complements proteomic-based efforts in the increased detection of integral membrane proteins, low-abundance proteins, or those not expressed in tissues selected for proteomic analysis. Digital northern analysis of expressed sequence tags suggested that the transcript levels of most AtPEM candidates were relatively constant among different tissues in contrast to stroma and the thylakoid proteins. However, both digital northern and microarray analyses identified a number of AtPEM candidates with tissue-specific expression patterns.
Arabidopsis↗
Proteomic analysis of rat hippocampal plasma membrane: characterization of potential neuronal-specific plasma membrane proteins.
The hippocampus is a distinct brain structure that is crucial in memory storage and retrieval. To identify comprehensively proteins of hippocampal plasma membrane (PM) and detect the neuronal-specific PM proteins, we performed a proteomic analysis of rat hippocampus PM using the following three technical strategies. First, proteins of the PM were purified by differential and density-gradient centrifugation from hippocampal tissue and separated by one-dimensional electophoresis, digested with trypsin and analyzed by electrospray ionization (ESI) quadrupole time-of-flight (Q-TOF) tandem mass spectrometry (MS/MS). Second, the tryptic peptide mixture from PMs purified from hippocampal tissue using the centrifugation method was analyzed by liquid chromatography ion-trap ESI-MS/MS. Finally, the PM proteins from primary hippocampal neurons purified by a biotin-directed affinity technique were separated by one-dimensional electrophoresis, digested with trypsin and analyzed by ESI-Q-TOF-MS/MS. A total of 345, 452 and 336 non-redundant proteins were identified by each technical procedure respectively. There was a total of 867 non-redundant protein entries, of which 64.9% are integral membrane or membrane-associated proteins. One hundred and eighty-one proteins were detected only in the primary neurons and could be regarded as neuronal PM marker candidates. We also found some hypothetical proteins with no functional annotations that were first found in the hippocampal PM. This work will pave the way for further elucidation of the mechanisms of hippocampal function.
Animals↗
Verification of biological markers of subacute cutaneous lupus erythematosus via TMT labelling proteomics combined with transcriptome data.
OBJECTIVE: This study aimed to investigate biological markers in subacute cutaneous lupus erythematosus (SCLE). METHODS: The tandem mass tag (TMT)-labelling proteomics method was used to explore differentially expressed proteins between SCLE lesions and normal skin tissues. The differences in transcriptomic data between SCLE tissues and normal skin tissues were analysed from the GEO database (GSE81071, GSE109248 and GSE112943). The differences in transcriptomic data from peripheral blood mononuclear cells (PBMCs) of patients with systemic lupus erythematosus (SLE) and normal controls were analysed (GSE81622 and GSE154851). The 35 healthy controls, 30 SCLE patients, 35 SLE patients and 30 lupus nephritis (LN) patients were diagnosed and enrolled. The serum expression levels of IFI44 and EPSTI1 were detected. Data were presented as the mean ± standard deviation or frequency and were analysed using Student's t-test, Chi-square test and one-way ANOVA between the groups. Receiver operating characteristic (ROC) curves were used to analyse the clinical efficacy of IFI44 and EPSTI1 in distinguishing SCLE from SLE. RESULTS: In a comparative analysis of SCLE lesions and normal skin tissues, proteomics studies identified 376 proteins that exhibited significant differential expression. In GO and KEGG analyses, the enriched terms mainly included the interferon-gamma-mediated signalling pathway (p < .001), immune receptor activity (p < .001) and cell adhesion molecules (p < .001). The top 10 hub genes were screened in SCLE as follows: CD8A, CXCL10, IFI44, CD7, CCL5, TLR4, EPSTI1, ISG15, KLRD1 and SELL using Cytoscape (3.10.1) software. The 15 common proteins/genes between proteomics and three datasets results were found, including CXCL10, OAS1, DDX60L, CFB, IFI6, HERC6, IFI44L, GBP1, EPSTI1, OAS2, CXCL11, TYMP, IFI44, ISG15 and IFIT3. The 61 differentially expressed genes in GSE81622 and the top 100 differentially expressed genes in GSE154851, alongside the 15 identified genes described above through Venn diagram analysis. Four common genes, IFI44L, IFI44, EPSTI1 and OAS1, were identified. Two common genes, IFI44 and EPSTI1, were found in hub genes from the proteomics results. The serum levels of IFI44 and EPSTI1 in LN were significantly higher than those in SLE patients (p < .05). ROC curve analysis demonstrated that serum levels of IFI44 and EPSTI1 could differentiate SCLE from SLE with an area under the curve (AUC) of 0.898 and 0.847, respectively. CONCLUSIONS: The IFI44 and EPSTI1 proved to be closely involved in the progression from SCLE to SLE, and can represent new candidate diagnostic molecular markers of occurrence and progression of SCLE.
Humans↗
Proteomic approach to probe for larval proteins of the mussel Mytilus galloprovincialis.
A proteomic approach was used to search for larval proteins specific to the mussel Mytilus galloprovincialis from Galicia in northwest Spain. The study included both a comparative analysis, through two-dimensional electrophoresis, of protein expression maps of the larvae of the mussel and of 5 abundant and commercially important bivalve species from the region (Ostrea edulis, Cerastoderma edule, Pecten maximus, Tapes decussatus, and Venarupis pullastra) and subsequent mass spectrometric analysis of some of the protein spots. A total of 18 spots were selected and isolated from gels of M. galloprovincialis larvae. From their relative position on the electrophoresis gels, 6 of these were clearly exclusive to the mussel species. However, it was not clear whether the other spots were shared by other species. To overcome this ambiguity, first an analysis using matrix assisted laser desorption ionization with time-of-flight (MALDI-TOF) was conducted on the 6 spots of Mytilus that could possibly be shared with only one species. The peptide mass fingerprinting was completely different for the proteins compared. This result confirmed that the 6 proteins were exclusively mussel proteins, but demonstrated the utility of this approach when working with species that are poorly represented at the protein level in databases.
Animals↗
Factors that affect ion trap data-dependent MS/MS in proteomics.
Quadrupole ion trap scanning parameters for performing bottom-up proteomics in a data-dependent fashion were evaluated on a Finnigan LCQ Deca mass spectrometer. Evaluation of parameters such as the number of averaged full scans, the number of averaged MS/MS scans, and ion injection times were necessary for acquiring high quality MS/MS spectra that yield favorable b and y ion coverage and high correlation to proteins using database searching algorithms. In this study, we demonstrated how the duty cycle of the mass spectrometer affects the number of peptides that can be successfully identified by SEQUEST using a model system of tryptic BSA peptides to mimic a typical complex mixture associated with bottom-up proteomics. The number of averaged scans and the duration of ion accumulation in the trap had a significant effect on the quality of acquired MS/MS spectra. For example, by increasing the ion injection time from 500 ms to 600 ms, peptide HLVDEPQNLIK improved from being improperly identified to being correctly identified with a SEQUEST cross-correlation score of 3.60. As a result of these experiments, we have devised the following set of ion trap parameters for performing bottom-up proteomics analysis in our laboratory: Three averaged full scans, five averaged MS/MS scans, and a maximum ion injection time of 600 ms.
Amino Acid Sequence↗
An insight into the salivary transcriptome and proteome of the adult female mosquito Culex pipiens quinquefasciatus.
To obtain an insight into the salivary transcriptome and proteome (sialome) of the adult female mosquito Culex quinquefasciatus, a cDNA library was randomly sequenced, and aminoterminal information for selected proteins and peptides was obtained. cDNA sequence clusters coding for secreted proteins were further analyzed. The transcriptome revealed messages coding for several proteins of known families previously reported in the salivary glands of other blood-feeding insects as well as immune-related products such as C-type lectin, gambicin, and members of the prophenol oxidase cascade. Additionally, several transcripts coding for low-complexity proteins were found, some clearly coding for mucins. Many novel transcripts were found, including a novel endonuclease previously described in crabs and shrimps but not in insects; a hyaluronidase, not described before in mosquito salivary glands but found in venom glands and in salivary glands of sand flies and black flies; several cysteine-rich peptides with possible anticlotting function, including one similar to a previously described nematode family of anti-proteases; and a completely novel family of cysteine- and tryptophane-rich proteins (CWRC family) for which 12 full-length sequences are described. Also described are 14 additional novel proteins and peptides whose function and/or family affiliation are unknown. In total, 54 transcripts coding for full-length proteins are described. That several of these are translated into proteins was confirmed by finding the corresponding aminoterminal sequences in the SDS-PAGE/Edman degradation experiments. Electronic versions of all tables and sequences can be found at http://www.ncbi.nlm.nih.gov/projects/Mosquito/C_quinquefasciatus_sialome.
Amino Acid Sequence↗
Harnessing deep learning for proteome-scale detection of amyloid signaling motifs.
MOTIVATION: Amyloid signaling sequences adopt the cross-β fold that is capable of self-replication in the templating process. Propagation of the amyloid fold from the receptor to the effector protein is used for signal transduction in the immune response pathways in animals, fungi, and bacteria. So far, a dozen of families of amyloid signaling motifs (ASMs) have been classified. Unfortunately, due to the wide variety of ASMs it is difficult to identify them in large protein databases available, which limits the possibility of conducting experimental studies. To date, various deep learning (DL) models have been applied across a range of protein-related tasks, including domain family classification and the prediction of protein structure and protein-protein interactions. RESULTS: In this study, we develop tailor-made bidirectional LSTM and BERT-based architectures to model ASM, and compare their performance against a state-of-the-art machine learning grammatical model. Our research is focused on developing a discriminative model of generalized ASMs, capable of detecting ASMs in large datasets. The DL-based models are trained on a diverse set of motif families and a global negative set, and used to identify ASMs from remotely related families. We analyze how both models represent the data and demonstrate that the DL-based approaches effectively detect ASMs, including novel motifs, even at the genome scale. AVAILABILITY AND IMPLEMENTATION: The models are provided as a Python package, asmscan-bilstm, and a Docker image at https://github.com/chrispysz/asmscan-proteinbert-run. The source code can be accessed at https://github.com/jakub-galazka/asmscan-bilstm and https://github.com/chrispysz/asmscan-proteinbert. Data and results are at https://github.com/wdyrka-pwr/ASMscan.
Deep Learning↗
Proteome and proteomics for the research on protein alterations in aging.
Functional decline of tissues in aged animals is a result of cellular aging. Though any process of somatic cell aging basically depends on genomic instructions, phenotypes of aged cells are expressed in a given internal environment of each cell type that was made with translated proteins and post-translationally modified products. Therefore, research on age-dependent protein alterations in each cell type is very important in clarifying mechanisms of aging. The novel term "proteome" is a compound of "protein" and "genome," which means constitutive whole proteins including post-translationally modified products in a cell type. Proteomics is a novel strategy for analyzing proteomes. In proteomics, high resolution two-dimensional electrophoresis is exclusively performed for isolation of proteins followed by mass spectrometry for identification of proteins and determination of modifications. Thus, proteomics is becoming appreciated as a powerful tool to find out proteins responsible for cellular aging, symptoms of senility and geriatric diseases.
Aging↗
Whole-proteome interaction mining.
MOTIVATION: A major post-genomic scientific and technological pursuit is to describe the functions performed by the proteins encoded by the genome. One strategy is to first identify the protein-protein interactions in a proteome, then determine pathways and overall structure relating these interactions, and finally to statistically infer functional roles of individual proteins. Although huge amounts of genomic data are at hand, current experimental protein interaction assays must overcome technical problems to scale-up for high-throughput analysis. In the meantime, bioinformatics approaches may help bridge the information gap required for inference of protein function. In this paper, a previously described data mining approach to prediction of protein-protein interactions (Bock and Gough, 2001, Bioinformatics, 17, 455-460) is extended to interaction mining on a proteome-wide scale. An algorithm (the phylogenetic bootstrap) is introduced, which suggests traversal of a phenogram, interleaving rounds of computation and experiment, to develop a knowledge base of protein interactions in genetically-similar organisms. RESULTS: The interaction mining approach was demonstrated by building a learning system based on 1,039 experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori. An estimate of the generalization performance of the classifier was derived from 10-fold cross-validation, which indicated expected upper bounds on precision of 80% and sensitivity of 69% when applied to related organisms. One such organism is the enteric pathogen Campylobacter jejuni, in which comprehensive machine learning prediction of all possible pairwise protein-protein interactions was performed. The resulting network of interactions shares an average protein connectivity characteristic in common with previous investigations reported in the literature, offering strong evidence supporting the biological feasibility of the hypothesized map. For inferences about complete proteomes in which the number of pairwise non-interactions is expected to be much larger than the number of actual interactions, we anticipate that the sensitivity will remain the same but precision may decrease. We present specific biological examples of two subnetworks of protein-protein interactions in C. jejuni resulting from the application of this approach, including elements of a two-component signal transduction systems for thermoregulation, and a ferritin uptake network.
Algorithms↗
Proteome analysis of the Chlamydia pneumoniae elementary body.
Chlamydia pneumoniae is an obligate intracellular human pathogen that causes acute and chronic respiratory tract diseases and that has been implicated as a possible risk factor in the development of atherosclerotic heart disease. C. pneumoniae cultivated in Hep-2 cells were 35S-labeled and infectious elementary bodies (EB) were purified. The EB proteins were separated by two-dimensional gel electrophoresis. Excised protein spots were in-gel digested with trypsin and peptides were concentrated on reverse-phase chromatographic beads for identification analysis by matrix-assisted laser desorption/ionization-mass spectrometry. In the pH range from 3-11, 263 C. pneumoniae protein spots encoded from 167 genes were identified. These genes constitute 15% of the genome. The identified proteins include 31 hypothetical proteins. It has recently been suggested that EB should be able to synthesize ATP. This view may be strengthened by the identification of several proteins involved in energy metabolism. Furthermore, proteins have been found which are involved in the type III secretion apparatus important for pathogenesis of intracellular bacteria. Proteome maps and a table of all identified proteins have been made available on the world wide web at www.gram.au.dk.
Amino Acids↗
Applied proteomics: mitochondrial proteins and effect on function.
The identification of a majority of the polypeptides in mitochondria would be invaluable because they play crucial and diverse roles in many cellular processes and diseases. The endogenous production of reactive oxygen species (ROS) is a major limiter of life as illustrated by studies in which the transgenic overexpression in invertebrates of catalytic antioxidant enzymes results in increased lifespans. Mitochondria have received considerable attention as a principal source---and target---of ROS. Mitochondrial oxidative stress has been implicated in heart disease including myocardial preconditioning, ischemia/reperfusion, and other pathologies. In addition, oxidative stress in the mitochondria is associated with the pathogenesis of Alzheimer's disease, Parkinson's disease, prion diseases, and amyotrophic lateral sclerosis (ALS) as well as aging itself. The rapidly emerging field of proteomics can provide powerful strategies for the characterization of mitochondrial proteins. Current approaches to mitochondrial proteomics include the creation of detailed catalogues of the protein components in a single sample or the identification of differentially expressed proteins in diseased or physiologically altered samples versus a reference control. It is clear that for any proteomics approach prefractionation of complex protein mixtures is essential to facilitate the identification of low-abundance proteins because the dynamic range of protein abundance within cells has been estimated to be as high as 10(7). The opportunities for identification of proteins directly involved in diseases associated with or caused by mitochondrial dysfunction are compelling. Future efforts will focus on linking genomic array information to actual protein levels in mitochondria.
Animals↗
IMGT unique numbering for immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains.
IMGT, the international ImMunoGeneTics database (http://imgt.cines.fr) is a high quality integrated information system specializing in immunoglobulins (IG), T cell receptors (TR) and major histocompatibility complex (MHC) of human and other vertebrates. IMGT provides a common access to expertly annotated data on the genome, proteome, genetics and structure of the IG and TR, based on the IMGT Scientific chart and IMGT-ONTOLOGY. The IMGT unique numbering defined for the IG and TR variable regions and domains of all jawed vertebrates has allowed a redefinition of the limits of the framework (FR-IMGT) and complementarity determining regions (CDR-IMGT), leading, for the first time, to a standardized description of mutations, allelic polymorphisms, 2D representations (Colliers de Perles) and 3D structures, whatever the antigen receptor, the chain type, or the species. The IMGT numbering has been extended to the V-like domain and is, therefore, highly valuable for comparative analysis and evolution studies of proteins belonging to the IG superfamily.
Amino Acid Sequence↗
New insights into the rat spermatogonial proteome: identification of 156 additional proteins.
Despite the essential role played by spermatogonia in testicular function, little is known about these cells. To improve our understanding of their biology, our group recently identified a set of 53 spermatogonial proteins using two-dimensional (2-D) gel electrophoresis and mass spectrometry. To continue this work, we investigated a subset of the spermatogonial proteome using narrow range immobilized pH gradients to favor the detection of less abundant proteins. A 2-D reference map of spermatogonia in the pH range 4-9 was created, and protein entities fractionated in a pH 5-6 2-D gel were further processed for protein identification. A new set of 156 polypeptides was identified by peptide mass fingerprinting and tandem mass spectrometry. These polypeptides corresponded to 102 different proteins, which reflect the complexity of post-translational modifications. Seventy-nine of these proteins were identified for the first time in spermatogonia. All identified proteins were classified into functional groups. This work represents a first step toward the establishment of a systematic spermatogonia protein database.
Animals↗