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Results for “Integrated proteomics”

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At least 865 records · Page 48Linked to original sources

PubMed2003

Proteomic analysis reveals upregulation of calreticulin in murine dopaminergic neuronal cells after treatment with 6-hydroxydopamine.

We have utilized integrated technologies including separation of proteins by 2-dimensional (2-D) gel electrophoresis and identification of proteins by matrix assisted laser desorption/ionizing time of flight (MALDI-TOF) mass spectrometry to examine an array of proteins that are regulated following treatment with neurotoxin. In essence, total cellular lysates harvested from MN9D dopaminergic neuronal cells treated with 6-hydroxydopamine (6-OHDA) for various time periods were subjected to 2-D gel separation followed by an analysis of the protein spots separated. Among the several protein spots that appeared to be either up- or down-regulated following 6-OHDA treatment, MALDI-TOF mass spectrometry revealed that an ER chaperone protein, calreticulin, was upregulated in a time-dependent manner. 6-OHDA-mediated up-regulation of this protein spot was reversed to the untreated control level when MN9D cells were co-treated with a pan-caspase inhibitor or an anti-oxidant. Immunoblot analysis using anti-calreticulin antibody confirmed this phenomenon. Since accumulation of altered proteins may be relevant in Parkinson's disease, our data suggest that regulation of chaperone activity in dopaminergic neurons comprises an additional cellular response to death-inducing stimuli.

Amino Acid Sequence↗

PubMed2004

Proteomics techniques for cystic fibrosis research.

Numerous factors, other than mutations in the CFTR gene, affect the phenotypic variability of cystic fibrosis (CF). With a two-dimensional electrophoresis (2-DE) analysis of total protein expression profiles (proteomics) of CF versus non-CF cells it is possible to obtain an integrative picture of CF cellular alterations. Through this approach, proteins that interact differently with wild type- and mutant-CFTR can also be identified (interactomics). This can provide insight into CF pathophysiology as well as clues for novel therapeutic targets. Additionally, protein profiling can ultimately identify novel disease markers with the potential for a CF diagnosis not based on the analysis of CFTR gene.

Clinical Laboratory Techniques↗

PubMed2005

Sociomics! Using the IssueCrawler to map, monitor and engage with the global proteomics research network.

We invite comment upon an experiment to locate proteomics on the WWW using a software tool called the IssueCrawler. We call our research "sociomics" because, like the bioscience omics, it is a semi-automated, computerised approach to the global analysis of data, whose computerised results can be integrated towards the development of a new "systems sociology" approach to the study of society. Our findings are that proteomics on the web is a scale-free network whose nodes display considerable "dynamic range".

Cluster Analysis↗

PubMed2025

Proteomic profiling identifies miR-423-5p as a modulator of oncogenic metabolism in HCC.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a significant clinical challenge due to limited diagnostic and therapeutic options. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), play key roles in cancer biology. Our previous findings showed that miR-423-5p enhances anti-cancer effects on HCC patients treated with sorafenib by promoting autophagy. Here, we investigated the molecular mechanisms underlying miR-423-5p function through a comprehensive proteomic approach. METHODS: We generated an HCC cell line stably overexpressing miR-423-5p via lentiviral transduction. Total proteins were extracted from SNU-387 cells, enzymatically digested into peptides, and subsequently analysed by liquid chromatography-tandem mass spectrometry (LC-MS/M). Raw spectral data were processed and quantified using MaxQuant. Differentially expressed proteins (DEPs) were defined based on fold-change (|log2FC| ≥ 1) and false discovery rate (FDR < 0.05). The full proteomic dataset is available via the ProteomeXchange repository (identifier: PXD064869). Functional enrichment analysis of DEPs were performed using DAVID and Reactome. To assess clinical relevance, predicted and validated miR-423-5p targets were integrated with The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) dataset using GEPIA platform. Survival analyses were performed using the Kaplan-Meier method. RESULTS: Proteomic profiling identified 698 DEPs in miR-423-5p-overexpressing cells compared to controls with significant enrichment in metabolic pathways, related to purine/pyrimidine metabolism and gluconeogenesis. Integration with bioinformatic predictions and miRTarBase validation identified 43 DEPs as potential direct targets of miR-423-5p. Among these, seven proteins (ACACA, ANKRD52, DVL3, MCM5, MCM7, RRM2, SPNS1, and SRM) were significantly associated with patient prognosis in the TCGA-LIHC cohort. These targets were downregulated in miR-423-5p-overexpressing cells but upregulated in advanced-stage HCC tissues, suggesting a potential role for miR-423-5p in the regulation of HCC pathogenesis. Stage-specific expression analysis showed increased levels from stage I to III, followed by a decline at stage IV. Notably, we experimentally confirmed miR-423-5p-mediated suppression of MCM7, DVL3, IMPDH1, and SRM (SPEE), supporting their functional involvement in HCC progression. CONCLUSION: Overall, our findings support a tumour-suppressive role for miR-423-5p in HCC, mediated by modulation of metabolic pathways and suppression of oncogenic proteins. These results suggest that miR-423-5p and its downstream effectors may serve as promising biomarkers and potential therapeutic targets in HCC. HIGHLIGHTS: miR-423-5p acts as a tumor suppressor in HCC by targeting key nodes of pro-tumorigenic signalling. miR-423-5p significantly altered metabolic pathways, including purine/pyrimidine metabolism and gluconeogenesis. Seven miR-423-5p targets correlate with poor prognosis in TCGA-LIHC patients and are downregulated in miR-423-5p overexpressing HCC cells. miR-423-5p over-expression induces a significant downregulation of MCM7, DVL3, IMPDH1, SPEE in HCC cell models. miR-423-5p limits tumor metabolic plasticity, suggesting therapeutic potential.

MicroRNAs↗

PubMed2004

Protein network inference from multiple genomic data: a supervised approach.

MOTIVATION: An increasing number of observations support the hypothesis that most biological functions involve the interactions between many proteins, and that the complexity of living systems arises as a result of such interactions. In this context, the problem of inferring a global protein network for a given organism, using all available genomic data about the organism, is quickly becoming one of the main challenges in current computational biology. RESULTS: This paper presents a new method to infer protein networks from multiple types of genomic data. Based on a variant of kernel canonical correlation analysis, its originality is in the formalization of the protein network inference problem as a supervised learning problem, and in the integration of heterogeneous genomic data within this framework. We present promising results on the prediction of the protein network for the yeast Saccharomyces cerevisiae from four types of widely available data: gene expressions, protein interactions measured by yeast two-hybrid systems, protein localizations in the cell and protein phylogenetic profiles. The method is shown to outperform other unsupervised protein network inference methods. We finally conduct a comprehensive prediction of the protein network for all proteins of the yeast, which enables us to propose protein candidates for missing enzymes in a biosynthesis pathway. AVAILABILITY: Softwares are available upon request.

Artificial Intelligence↗

PubMed2026

Current landscape of Cys-OxiPTMs in plants: from hormone signaling to phenotypic control and their potential in sustainable agriculture.

The integration of environmental and developmental cues into coherent physiological responses is fundamental to plant survival. Reactive oxygen, nitrogen, and sulfur species (ROS/RNS/RSS) are now recognized as essential signaling molecules, not merely cytotoxic byproducts. Their specificity is largely achieved through reversible, site-specific cysteine oxidative post-translational modifications (Cys-OxiPTMs), which constitute a dynamic and sophisticated "redox code". This review provides a systematic synthesis of the current landscape of Cys-OxiPTMs in plants, bridging chemistry, hormone biology, agronomy, detection, and engineering. The chemical and enzymatic basis of major Cys-OxiPTMs is detailed, along with a discussion of how their spatiotemporal interplay orchestrates signaling specificity. A critical examination is then presented on how these modifications decode and integrate plant hormone signaling networks to regulate key agronomic traits. Cutting-edge proteomic technologies that have revolutionized the identification of redox-sensitive cysteines are also evaluated. Finally, forward-looking strategies to "write" the redox code are explored. By moving the field from descriptive cataloging to predictive "redox breeding", this review establishes a foundational framework for manipulating Cys-OxiPTMs to develop climate-resilient, high-yielding crops for sustainable agriculture.

Agronomic traits↗

PubMed2000

Genomics: from novel genes to new therapeutics in parasitology.

The advent of rapid DNA sequencing technologies is generating vast quantities of raw genomic information ranging from in-depth analysis of the expressed genes to complete sequencing of genomes at an increasing rate (bioinformatics). However, it is the functional characterisation of a specific gene product that is the key limiting factor for validation as targets for high throughput assay development. The challenge is to obtain the raw genomic information from parasites of economic importance and to effectively integrate broad technologies such as gene disruption and over-expression, DNA arrays, proteomics, antisense RNAs, with bioinformatics in a timely fashion to identify relevant biological targets. Screening of validated targets in a strategy that includes large numbers of chemistries with high diversity and predictive in vitro and in vivo assays should permit the successful identification of novel chemical entities with high specificity to the target parasite. It is proposed that this rational approach will permit the identification of new antiparasitic therapies able to surpass the current toxicological, environmental, and economic challenges of the marketplace.

Animals↗

PubMed2002

The pathology of brucellosis reflects the outcome of the battle between the host genome and the Brucella genome.

The successful co-existence of each Brucella spp. with its preferred host is the outcome of ancient co-evolutionary relationships and selection pressures that often result in a stalemate where the pathogen has evolved to survive within the biological systems of the host, and the host has evolved innate and acquired immune systems which allow controlled survival of infection by the pathogen, ultimately supporting the survival of the host-pathogen system. In general, Brucella spp. have evolved a similar fundamental pathogenesis of facultative intracellular parasitism though the predominant route of natural exposure varies from oropharynx to genital tract, as does the preferred tissue and cellular tropism, e.g. non-professional placental trophoblasts, fetal lung, professional macrophages of reticulendothelial system, and the male and female reproductive tracts. The morphogenesis of the pyogranulomatous lesions stimulated by Brucella reflects the nature of the persistent parasitism, i.e. genome versus genome. The question is, how can this perplexing array of survival mechanisms be unraveled? Fortunately, the integration of real-time image analysis, cell biology, genome-wide analysis, proteomics and bioinformatics holds the most promise ever for the global analysis of the Brucella infectious process and the host:pathogen interface leading to a clearer understanding of the interactions of these biological systems. These discoveries will be expected to provide a frameshift in rationales for interrupting and/or controlling brucellosis at host and/or pathogen levels.

Animals↗

PubMed2005

Identification of the hydrophobic glycoproteins of Caenorhabditis elegans.

Hydrophobic proteins such as integral membrane proteins are difficult to separate, and therefore to study, at a proteomics level. However, the Asn-linked (N-linked) carbohydrates (N-glycans) contained in membrane glycoproteins are important in differentiation, embryogenesis, inflammation, cancer and metastasis, and other vital cellular processes. Thus, the identification of these proteins and their sites of glycosylation in a well-characterized model organism is the first step toward understanding the mechanisms by which N-glycans and their associated proteins function in vivo. In this report, a proteomics method recently developed by our group was applied to identify 117 hydrophobic N-glycosylated proteins of Caenorhabditis elegans extracts by analysis of 195 glycopeptides containing 199 Asn-linked oligosaccharides. Most of the proteins identified are involved in cell adhesion, metabolism, or the transport of small molecules. In addition, there are 18 proteins for which no function is known or predictable by sequence homologies and two proteins which were previously predicted to exist only on the basis of genomic sequences in the C. elegans database. Because N-glycosylation is initiated in the lumen of the endoplasmic reticulum (ER), our data can be used to reassess the previously predicted subcellular localizations of these proteins. As well, the identification of N-glycosylation sites helps establish the membrane topology of the associated glycoproteins. Caenorhabditis elegans strains are presently available with mutations in 17 of the genes we have identified. The powerful genetic tools available for C. elegans can be used to make other strains with mutations in genes encoding N-glycosylated proteins and thereby determine N-glycan function.

Animals↗

PubMed2026

SpxA1 and SpxA2 function as a stoichiometry-dependent regulatory rheostat governing virulence gene expression in group A Streptococcus.

UNLABELLED: Group A Streptococcus (GAS) is a human-restricted pathogen whose global incidence has surged in the post-COVID era. The ability of GAS to shift from a colonizing to invasive phenotype depends on coordinated virulence gene regulation in response to host-derived signals. However, the mechanisms by which individual stress-sensing systems interact to reshape the virulence gene regulatory landscape remain incompletely understood. Here, we define the regulatory programs of two conserved transcriptional regulator paralogs, SpxA1 and SpxA2, using an integrated multi-omic approach combining RNA-seq, data-independent acquisition proteomics, NanoString-based transcriptional profiling across multiple host-relevant stress conditions, and chromatin immunoprecipitation with exonuclease treatment (ChIP-exo). RNA-seq revealed functionally distinct regulons with SpxA1 governing oxidative stress defense and SpxA2 coordinating virulence-associated gene expression linked to the CovRS two-component regulatory system. Proteomic analysis established SpxA2 as a ClpXP protease substrate in GAS and identified reciprocal paralog accumulation upon loss of either SpxA1 or SpxA2, consistent with compensatory transcriptional upregulation. NanoString profiling under bacitracin and human neutrophil peptide-1 challenge identified four gene modules with distinct stoichiometry-dependent and condition-dependent regulatory logic, revealing that the SpxA1/SpxA2 ratio rather than the activity of either paralog alone determines which transcriptional programs are engaged. ChIP-exo demonstrated that SpxA2 directly modulates CovR-DNA binding occupancy in a CovR-binding motif-dependent manner, simultaneously antagonizing CovR dimer binding at an extended (25 bp) CovR motif and facilitating CovR monomer binding at the canonical ATTARA motif. These findings establish the LiaFSR-SpxA2-CovRS axis as a cross-regulatory circuit through which GAS cell envelope stress sensing is directly transduced into coordinated virulence gene regulatory changes. IMPORTANCE: Group A Streptococcus (GAS) causes millions of infections annually, including a recent global surge in invasive disease. To survive in the human host, GAS must rapidly reprogram virulence gene expression in response to host-derived stresses. This study characterizes two conserved transcriptional regulators, SpxA1 and SpxA2, that govern this response through interaction with RNA polymerase to indirectly influence the DNA-binding activity of downstream transcription factors. We show that SpxA2, activated by a cell envelope stress-sensing system responding to human antimicrobial peptides, reshapes the binding of the master virulence regulator CovR in a promoter-specific manner, coupling cell envelope stress sensing to virulence gene regulation. The stoichiometric balance between SpxA1 and SpxA2 functions as a regulatory rheostat calibrating overall virulence gene regulatory tone, providing a framework for understanding how RNA polymerase-interacting regulators coordinate stress responses and virulence gene control across Gram-positive bacterial pathogens.

Streptococcus pyogenes↗

PubMedDate not supplied

[Nutritional genomics: toward a personalized diet].

Nutrigenomics is the application of high-throughput genomics tools to the study of diet-gene interactions in order to identify dietetic components having beneficial or detrimental health effects. Nutrition becomes indeed one of the environmental factors influencing gene expression. We can consider nutrigenomics as a multidisciplinary science that comes after the human genome characterization and that put the genomic techniques besides the biochemical and epidemiological aspects, with the aim to understand the etiologic aspects of chronic diseases such as cancer, type 2 diabetes mellitus (T2DM), obesity, cardiovascular diseases (CVD), metabolic syndrome, etc. Nutrigenomics is linked to nutrigenetics, which studies the genetic basis of the different individual response to the same nutritional stimulus. This phenomenon arises from gene polymorphism. As a consequence genes are important in determining a function, but nutrition is able to modify the degree of gene expression. These are however theories only at an early stage, but a perspective in the change of dietetic intervention is emerging. A really personalized diet will be a diet considering the nutritional status, the nutritional needs based on age, body composition, work and physical activities, but also considering the genotype. The integration of all these information and in particular the ones arising from genomic, proteomic and metabolomic analyses will be useful to define the "nutritional phenotype".

Diet↗

PubMedDate not supplied

STEM: a software tool for large-scale proteomic data analyses.

We describe the software, STEM (STrategic Extractor for Mascot's results), which efficiently processes large-scale mass spectrometry-based proteomics data. V (View)-mode evaluates the Mascot peptide identification dataset, removes unreliable candidates and redundant assignments, and integrates the results with key information in the experiment. C (Comparison)-mode compares peptide coverage among multiple datasets and displays proteins commonly/specifically found therein, and processes data for quantitative studies that utilize conventional isotope tags or tags having a smaller mass difference. STEM significantly improves throughput of proteomics study.

Algorithms↗

PubMed2026

Hepatic metabolic adaptation to endurance exercise: temporal and sex differences by multiomics integration and validation.

BACKGROUND: Although endurance exercise benefits liver health, sex-specific adaptive trajectories remain unclear. This study mapped dynamic liver adaptation in males and females during prolonged training and identified underlying molecular programs. METHODS: Using publicly available time-resolved liver multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we established a computational pipeline for differential analysis of transcriptomic, proteomic, phosphoproteomic, and metabolomic data with FDR correction, followed by FGSEA pathway enrichment. Kinase activities were inferred through ortholog mapping and PhosphoSitePlus. Cross-omics co-expression networks were constructed using WGCNA and topological overlap to link omics features with physiological phenotypes. For experimental validation, liver tissues were collected from endurance-trained Sprague-Dawley rats, and key nodes were confirmed by Western blotting, qRT-PCR, and immunofluorescence/immunohistochemical staining. Public scRNA-seq data were further integrated to map multi-omics signals to single-cell resolution and assess functional changes in specific cell types. RESULTS: The hepatic response to exercise stress was stage-specific, shifting from early transcriptional activation to later proteomic and metabolic remodeling. Multi-omics integration revealed distinct sex-associated adaptive trajectories: males were more strongly associated with energy metabolism, redox-related programs, and amino acid/organic acid catabolism, whereas females showed prominent membrane lipid remodeling, proteostasis -related programs, and mitochondrial/ribosomal translational features. Single-cell analysis showed that tissue remodeling occurred without major lineage turnover, instead involving altered communication among pre-existing cell communities. Validation of PPP1R3G identified a protein-dominant exercise-responsive marker, supporting the contribution of post-transcriptional or protein-level regulation. CONCLUSIONS: Hepatic adaptation to endurance stress follows a cross-omics evolutionary pattern with sex-specific reprogramming of energy supply and homeostatic maintenance. This time-resolved framework clarifies how exercise improves liver function and supports sex-oriented metabolic interventions and therapeutic target discovery.

Animals↗

PubMed2025

An integrative network approach for longitudinal stratification in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms resulting from the loss of dopamine-producing neurons in the brain. Currently, there is no cure for the disease which is in part due to the heterogeneity in patient symptoms, trajectories and manifestations. There is a known genetic component of PD and genomic datasets have helped to uncover some aspects of the disease. Understanding the longitudinal variability of PD is essential as it has been theorised that there are different triggers and underlying disease mechanisms at different points during disease progression. In this paper, we perform longitudinal and cross-sectional experiments to identify which data modalities or combinations of modalities are informative at different time points. We use clinical, genomic, and proteomic data from the Parkinson's Progression Markers Initiative. We validate the importance of flexible data integration by highlighting the varying combinations of data modalities for optimal stratification at different disease stages in idiopathic PD. We show there is a shared signal in the DNAm signatures of participants with a mutation in a causal gene of PD and participants with idiopathic PD. We also show that integration of SNPs and DNAm data modalities has potential for use as an early diagnostic tool for individuals with a genetic cause of PD.

Parkinson Disease↗

PubMed2025

ImmunoTar-integrative prioritization of cell surface targets for cancer immunotherapy.

MOTIVATION: Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field. RESULTS: To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies. AVAILABILITY AND IMPLEMENTATION: Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.

Humans↗

PubMed2026

Optimizing NGN2 Dosage Enhances the Neuronal Enrichment of iPSC-Derived Neuronal Cultures.

Proteomic analysis of highly neuron-enriched iPSC-derived cultures can provide valuable insights into the pathogenesis of neurological disease by minimizing confounding signals from non-neuronal populations. However, single-cell iPSC clones with stable NGN2 integration at the AAVS1 locus exhibit spontaneous loss of the mCherry reporter. In addition, following NGN2 induction, cultures frequently contain proliferative progenitor cells that compromise neuronal physical integrity. Here, we show that increased DNA methylation of the EF1-α promoter is associated with mCherry silencing. Importantly, reporter silencing does not affect iNeuron derivation, as evidenced by two lines of evidence. First, single-cell proteomic analysis indicated that mCherry silencing does not drive detectable proteomic divergence in undifferentiated iPSCs. Second, bulk proteomics and immunofluorescence analyses indicated that iPSC-derived neuronal cultures, whether expressing or lacking mCherry, resemble cortical glutamatergic neurons. Instead, the primary confounding factor in iNeuron generation was suboptimal neuronal conversion, which led to cell aggregates comprised of actively proliferating progenitor cells and astrocytes as the culture developed. We found that a 4-day period of NGN2 induction substantially increases neuronal maturation and the expression of neuronal-specific markers. Moreover, transient inhibition of Notch signaling is consistent with a reduction in progenitor cells. Finally, we observed that monoallelic NGN2 integration, rather than biallelic integration, preferentially increases expression of the CNS neuronal marker GPM6A over PNS markers (PRPH, POU4F1, ILS1).

Basic Helix-Loop-Helix Proteins↗

PubMed2002

Proteome analysis of nuclear matrix proteins during apoptotic chromatin condensation.

The nuclear matrix (NM) is considered a proteinaceous scaffold spatially organizing the interphase nucleus, the integrity of which is affected during apoptosis. Caspase-mediated degradation of NM proteins, such as nuclear lamins, precedes apoptotic chromatin condensation (ACC). Nevertheless, other NM proteins remain unaffected, which most likely maintain a remaining nuclear structure devoid of chromatin. We, therefore, screened various types of apoptotic cells for changes of the nuclear matrix proteome during the process of apoptotic ACC. Expectedly, we observed fundamental alterations of known chromatin-associated proteins, comprising both degradation and translocation to the cytosol. Importantly, a consistent set of abundant NM proteins, some (e.g. hNMP 200) of which displaying structural features, remained unaffected during apoptosis and might therefore represent constituents of an elementary scaffold. In addition, proteins involved in DNA replication and DNA repair were found accumulated in the NM fraction before cells became irreversibly committed to ACC, a time point characterized in detail by inhibitor studies with orthovanadate. In general, protein alterations of a consistent set of NM proteins (67 of which were identified), were reproducibly detectable in Fas-induced Jurkat cells, in UV-light treated U937 cells and also in staurosporine-treated HeLa cells. Our data indicate that substantial alterations of proteins linking chromatin to an elementary nuclear protein scaffold might play an intriguing role for the process of ACC.

Apoptosis↗

PubMed2003

Proteomic informatics: in silico methods lead to data management challenges.

Proteomics, which identifies proteins and analyzes their function in cells, is foreseen as the next challenge in biomedicine as diseases in the body are most easily recognized through the function of their proteins. Achieving this recognition is more difficult than pure gene analysis: it is estimated that 35,000 genes are present in human DNA, encoding more than 1 million proteins. A myriad of in vitro and in silico technologies now exist for studying proteins and their biological function. This review focuses on the vast array of in silico proteomic analysis methods, highlights public and commercial repositories for this data, and discusses the challenges associated with resolving and integrating the knowledge originating from this data.

Amino Acid Sequence↗