UniProt Entries Research Articles

BFVD-a large repository of predicted viral protein structures.

The AlphaFold Protein Structure Database (AFDB) is the largest repository of accurately predicted structures with taxonomic labels. Despite providing predictions for over 214 million UniProt entries, the AFDB does not cover viral sequences, severely limiting their study. To address this, we created the Big Fantastic Virus Database (BFVD), a repository of 351 242 protein structures predicted by applying ColabFold to the viral sequence representatives of the UniRef30 clusters. By utilizing homology searches across two petabases of assembled sequencing data, we improved 36% of these structure predictions beyond ColabFold's initial results. BFVD holds a unique repertoire of protein structures as over 62% of its entries show no or low structural similarity to existing repositories. We demonstrate how a substantial fraction of bacteriophage proteins, which remained unannotated based on their sequences, can be matched with similar structures from BFVD. In that, BFVD is on par with the AFDB, while holding nearly three orders of magnitude fewer structures. BFVD is an important virus-specific expansion to protein structure repositories, offering new opportunities to advance viral research. BFVD can be freely downloaded at bfvd.steineggerlab.workers.dev and queried using Foldseek and UniProt labels at bfvd.foldseek.com.

GO2Sum: generating human-readable functional summary of proteins from GO terms

Understanding the biological functions of proteins is of fundamental importance in modern biology. To represent a function of proteins, Gene Ontology (GO), a controlled vocabulary, is frequently used, because it is easy to handle by computer programs avoiding open-ended text interpretation. Particularly, the majority of current protein function prediction methods rely on GO terms. However, the extensive list of GO terms that describe a protein function can pose challenges for biologists when it comes to interpretation. In response to this issue, we developed GO2Sum (Gene Ontology terms Summarizer), a model that takes a set of GO terms as input and generates a human-readable summary using the T5 large language model. GO2Sum was developed by fine-tuning T5 on GO term assignments and free-text function descriptions for UniProt entries, enabling it to recreate function descriptions by concatenating GO term descriptions. Our results demonstrated that GO2Sum significantly outperforms the original T5 model that was trained on the entire web corpus in generating Function, Subunit Structure, and Pathway paragraphs for UniProt entries.

UniProt: the Universal Protein Knowledgebase in 2023.

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication we describe enhancements made to our data processing pipeline and to our website to adapt to an ever-increasing information content. The number of sequences in UniProtKB has risen to over 227 million and we are working towards including a reference proteome for each taxonomic group. We continue to extract detailed annotations from the literature to update or create reviewed entries, while unreviewed entries are supplemented with annotations provided by automated systems using a variety of machine-learning techniques. In addition, the scientific community continues their contributions of publications and annotations to UniProt entries of their interest. Finally, we describe our new website (https://www.uniprot.org/), designed to enhance our users' experience and make our data easily accessible to the research community. This interface includes access to AlphaFold structures for more than 85% of all entries as well as improved visualisations for subcellular localisation of proteins.

HProteome-BSite: predicted binding sites and ligands in human 3D proteome.

Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.

TAGOPSIN: collating taxa-specific gene and protein functional and structural information

BackgroundThe wealth of biological information available nowadays in public databases has triggered an unprecedented rise in multi-database search and data retrieval for obtaining detailed information about key functional and structural entities. This concerns investigations ranging from gene or genome analysis to protein structural analysis. However, the retrieval of interconnected data from a number of different databases is very often done repeatedly in an unsystematic way.ResultsHere, we present TAxonomy, Gene, Ontology, Protein, Structure INtegrated (TAGOPSIN), a command line program written in Java for rapid and systematic retrieval of select data from seven of the most popular public biological databases relevant to comparative genomics and protein structure studies. The program allows a user to retrieve organism-centred data and assemble them in a single data warehouse which constitutes a useful resource for several biological applications. TAGOPSIN was tested with a number of organisms encompassing eukaryotes, prokaryotes and viruses. For example, it successfully integrated data for about 17,000 UniProt entries of Homo sapiens and 21 UniProt entries of human coronavirus.ConclusionTAGOPSIN demonstrates efficient data integration whereby manipulation of interconnected data is more convenient than doing multi-database queries. The program facilitates for instance interspecific comparative analyses of protein-coding genes in a molecular evolutionary study, or identification of taxa-specific protein domains and three-dimensional structures. TAGOPSIN is available as a JAR file at https://github.com/ebundhoo/TAGOPSIN and is released under the GNU General Public License.

UniProt: the universal protein knowledgebase in 2021.

The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Characterization and Quantitative Determination of a Diverse Group of Bacillus subtilis subsp. subtilis NCIB 3610 Antibacterial Peptides.

Five antibacterial peptides produced by Bacillus subtilis NCIB 3610 were purified, quantified, characterized, and identified in the present study. Cell-free extracts were subjected to three purification protocols employing ammonium sulfate or organic solvent precipitation and their combination, followed by ion-exchange chromatography, solid-phase extraction, and preparative high-performance liquid chromatography (HPLC). The combined ammonium sulfate and organic solvent precipitation extraction protocol presented the best results for peptide purification. In the five fractions that presented antimicrobial activity, antibacterial peptides were quantified by the turbidometric method and by HPLC using nisin for external calibration, with the second providing more accurate results. All peptides were pH- and temperature-resistant and their sensitivity to proteases treatment indicated their proteinic nature. The five peptides were subjected to microwave-assisted acid hydrolysis (MAAH) and following derivatization were analyzed using norleucine as the internal standard, to determine their amino acid content. The identification of the isolated peptides using the UniProt and PubChem databases indicated that the four peptides correspond to UniProt entries of the bacteriocins Subtilosin-A (Q1W152) Subtilosin-SbOX (H6D9P4), Ericin B (Q93GH3), Subtilin (P10946), and the fifth to the non-ribosomal antibacterial lipopeptide surfactin (CID:443592). The amino acid content determination and computational analyses, applied in the present work on the antimicrobial peptides of B. subtilis, proved an efficient screening and quantification method of bacteriocins that could potentially be applied in other bacterial strains. The constructed phylogenetic trees heterogeneity observed across the five peptides investigated might be indicative of competitive advantage of the strain.

PiNET: a versatile web platform for downstream analysis and visualization of proteomics data.

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

1H, 13C, and 15N Backbone assignments of the human brain and acute leukemia cytoplasmic (BAALC) protein

The brain and acute leukemia cytoplasmic (BAALC; UniProt entry Q8WXS3) is a 180-residue-long human protein having six known isoforms. BAALC is expressed in either hematopoietic or neuroectodermal cells and its specific function is still to be revealed. However, as a presumably membrane-anchored protein at the cytoplasmic side it is speculated that BAALC exerts its function at the postsynaptic densities of certain neurons and might play a role in developing cytogenetically normal acute myeloid leukemia (CN-AML) when it is highly overexpressed by myeloid or lymphoid progenitor cells. In order to better understand the physiological role of BAALC and to provide the basis for a further molecular characterization of BAALC, we report here the 1H, 13C, and 15N resonance assignments for the backbone nuclei of its longest hematopoietic isoform (isoform 1). In addition, we present a 1HN and 15NH chemical shift comparison of BAALC with its shortest, neuroectodermal isoform (isoform 6) which shows only minor changes in the 1H and 15N chemical shifts.

Biophysical Techniques for Distinguishing Ligand Binding Modes in Cytochrome P450 Monooxygenases.

The cytochrome P450 superfamily of heme monooxygenases catalyzes important chemical reactions across nature. The changes in the optical spectra of these enzymes, induced by the addition of substrates or inhibitors, are critical for assessing how these molecules bind to the P450, enhancing or inhibiting the catalytic cycle. Here we use the bacterial CYP199A4 enzyme (Uniprot entry Q2IUO2), from Rhodopseudomonas palustris HaA2, and a range of substituted benzoic acids to investigate different binding modes. 4-Methoxybenzoic acid elicits an archetypal type I spectral response due to a ≥95% switch from the low- to high-spin state with concomitant dissociation of the sixth aqua ligand. 4-(Pyridin-3-yl)- and 4-(pyridin-2-yl)benzoic acid induced different type II ultraviolet-visible (UV-vis) spectral responses in CYP199A4. The former induced a greater red shift in the Soret wavelength (424 nm vs 422 nm) along with a larger overall absorbance change and other differences in the α-, β-, and δ-bands. There were also variations in the ferrous UV-vis spectra of these two substrate-bound forms with a spectrum indicative of Fe-N bond formation with 4-(pyridin-3-yl)benzoic acid. The crystal structures of CYP199A4, with the pyridinyl compounds bound, revealed that while the nitrogen of 4-(pyridin-3-yl)benzoic acid is coordinated to the heme, with 4-(pyridin-2-yl)benzoic acid an aqua ligand remains. Continuous wave and pulse electron paramagnetic resonance data in frozen solution revealed that the substrates are bound in the active site in a form consistent with the crystal structures. The redox potential of each CYP199A4-substrate combination was measured, allowing correlation among binding modes, spectroscopic properties, and the observed biochemical activity.

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.

Understanding the Structure and Function of the DcaP Channel from Acinetobacter baumannii using MD Simulations

DcaP is putative dicarboxylate specific channel, located in the outer membrane of the pathogen Acinetobacter baumannii. The X-ray crystal structure reveals that DcaP is the first trimeric channel identified in Acinetobacter baumannii and could play an important role in substrate and antibiotic permeation. To characterize the permeation properties of this channel, we have carried out the applied field MD simulations in the presence of ions (KCl), substrates (phthalic acid, succinic acid) and a β-lactam antibiotic (sulbactam). Additionally, free energy calculation have been carried out using metadynamics simulations to identify the lowest energy permeation path along the 2D free energy surfaces and the most prominent residues during translocation. These simulations clearly suggest that the DcaP channel is involved in the permeation of these solutes and results are complemented with electrophysiology experiments. Furthermore, the crystal structure reveals that DcaP have an extended N-terminus domain in the periplasmic space, which is presumably known to form the coiled-coil structure (uniprot entry: A0A0B9X9I7). As the N-terminus domain was not resolved in the crystal structure, we have predicted the structure using an extensive modeling approach. Moreover, the simulations and electrophysiology experiments suggest that the N-terminus might play an important role in the formation of a stable trimer. Overall, we have built the so far unknown structure-function relationship for the DcaP channel which could help in designing next generation antibiotics efficiently permeating through this channel.

Identification of bacteriocins secreted by the probiotic Lactococcus lactis following microwave-assisted acid hydrolysis (MAAH), amino acid content analysis, and bioinformatics.

A novel, generally applicable method of identifying peptides using HPLC, microwave-assisted acid hydrolysis (MAAH), and bioinformatics is described. Method validation was performed on bacteriocins-antibacterial peptides produced by probiotic bacteria-using nine different bacteriocin isolates secreted by the probiotic Lactococcus lactis. Calibration curves were constructed for 23 amino acid PTH derivatives, and analysis was performed using norleucine as the internal standard. Validation of amino acid analysis performed in the range 2.5-100nmol/mL indicated excellent method linearity, while the LODs ranged from 0.17 to 2.88nmol/mL and the LOQs from 0.51 to 8.75nmol/mL. The MAAH method was developed by irradiating nisaplin for various durations at 700W, with 7min providing the best results. The amino acid content of each sample was estimated following the application of MAAH to ten different samples. The bacteriocins in our samples were identified using the UniProt database. Eight of nine peptides were identified as UniProt entries: nisin A (P13068), nisin Z (P29559), I4DSZ9, OB7236, P36499, OB7237, A0A0M7BH60, and T2C9F0. The phylogenetic tree was constructed for nisin A and nisin Z using the multiple sequence aligning tool Clustal Ω. The identified nisin types presented excellent correlation with their ModBase-predicted structures. The present method gives true, precise, and rapid results, and requires only standard technical equipment. Our results suggest that the present approach can facilitate the discovery of novel bacteriocins and provide useful information on not only the amino acid contents of peptides but also the evolution of protein biology. Graphical abstract Identification of eight bacteriocins secreted by the probiotic L. lactis, following microwave assisted acid hydrolysis (MAAH), amino acid content analysis of each sample with HPLC-DAD and bioinformatics analysis using Uniprot, Clustal Ω and ModBase.

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology.

It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.

The differential ability of asparagine and glutamine in promoting the closed/active enzyme conformation rationalizes the Wolinella succinogenes L-asparaginase substrate specificity

Many side effects of current FDA-approved L-asparaginases have been related to their secondary L-glutaminase activity. The Wolinella succinogenes L-asparaginase (WoA) has been reported to be L-glutaminase free, suggesting it would have fewer side effects. Unexpectedly, the WoA variant with a proline at position 121 (WoA-P121) was found to have L-glutaminase activity in contrast to Uniprot entry P50286 (WoA-S121) that has a serine residue at this position. Towards understanding how this residue impacts the L-glutaminase property, kinetic analysis was coupled with crystal structure determination of these WoA variants. WoA-S121 was confirmed to have much lower L-glutaminase activity than WoA-P121, yet both showed comparable L-asparaginase activity. Structures of the WoA variants in complex with L-aspartic acid versus L-glutamic acid provide insights into their differential substrate selectivity. Structural analysis suggests a mechanism by which residue 121 impacts the conformation of the conserved tyrosine 27, a component of the catalytically-important flexible N-terminal loop. Surprisingly, we could fully model this loop in either its open or closed conformations, revealing the roles of specific residues of an evolutionary conserved motif among this L-asparaginase family. Together, this work showcases critical residues that influence the ability of the flexible N-terminal loop for adopting its active conformation, thereby effecting substrate specificity.

A novel plant enzyme with dual activity: an atypical Nudix hydrolase and a dipeptidyl peptidase III.

In a search for plant homologues of dipeptidyl peptidase III (DPP III) family, we found a predicted protein from the moss Physcomitrella patens (UniProt entry: A9TLP4), which shared 61% sequence identity with the Arabidopsis thaliana uncharacterized protein, designated Nudix hydrolase 3. Both proteins contained all conserved regions of the DPP III family, but instead of the characteristic hexapeptide HEXXGH zinc-binding motif, they possessed a pentapeptide HEXXH, and at the N-terminus, a Nudix box, a hallmark of Nudix hydrolases, known to act upon a variety of nucleoside diphosphate derivatives. To investigate their biochemical properties, we expressed heterologously and purified Physcomitrella (PpND) and Arabidopsis (AtND) protein. Both hydrolyzed, with comparable catalytic efficiency, the isopentenyl diphosphate (IPP), a universal precursor for the biosynthesis of isoprenoid compounds. In addition, PpND dephosphorylated four purine nucleotides (ADP, dGDP, dGTP, and 8-oxo-dATP) with strong preference for oxidized dATP. Furthermore, PpND and AtND showed DPP III activity against dipeptidyl-2-arylamide substrates, which they cleaved with different specificity. This is the first report of a dual activity enzyme, highly conserved in land plants, which catalyzes the hydrolysis of a peptide bond and of a phosphate bond, acting both as a dipeptidyl peptidase III and an atypical Nudix hydrolase.

EGenPub, a text mining system for extending computationally mapped bibliography for UniProt Knowledgebase by capturing centrality.

UniProt Knowledgebase (UniProtKB) is a publicly available database with access to a vast amount of protein sequence and functional information. To widen the scope of the publications associated with a protein entry, UniProt has introduced the computationally mapped additional bibliography section, which includes literature collected from external sources. In this article, we describe a text mining system, eGenPub, which selects articles that are ‘about’ specific proteins and allows automatic identification of additional bibliography for given UniProt protein entries. Focusing on plant proteins initially, eGenPub utilizes a gene normalization tool called pGenN, and a trained support vector machine model, which achieves a precision of 95.3%, to predict whether an article, based on its abstract, should be linked to a given UniProt entry. We have conducted a full-scale PubMed processing using eGenPub for eight common plant species. Altogether, 9025 articles are identified as relevant bibliography for 4752 UniProt entries, among which 5252 are additional papers not in the existing publication section. These newly computationally mapped additional bibliography via eGenPub is being integrated in the UniProt production pipeline, and can be accessed via the UniProtKB protein entry publication view.

Unipept web services for metaproteomics analysis

Unipept is an open source web application that is designed for metaproteomics analysis with a focus on interactive datavisualization. It is underpinned by a fast index built from UniProtKB and the NCBI taxonomy that enables quick retrieval of all UniProt entries in which a given tryptic peptide occurs. Unipept version 2.4 introduced web services that provide programmatic access to the metaproteomics analysis features. This enables integration of Unipept functionality in custom applications and data processing pipelines. The web services are freely available at http://api.unipept.ugent.be and are open sourced under the MIT license. Unipept@ugent.be Supplementary data are available at Bioinformatics online.

Protein profile of Beta vulgaris leaf apoplastic fluid and changes induced by Fe deficiency and Fe resupply.

The fluid collected by direct leaf centrifugation has been used to study the proteome of the sugar beet apoplastic fluid as well as the changes induced by Fe deficiency and Fe resupply to Fe-deficient plants in the protein profile. Plants were grown in Fe-sufficient and Fe-deficient conditions, and Fe resupply was carried out with 45 μM Fe(III)-EDTA for 24 h. Protein extracts of leaf apoplastic fluid were analyzed by two-dimensional isoelectric focusing-SDS-PAGE electrophoresis. Gel image analysis revealed 203 consistent spots, and proteins in 81% of them (164) were identified by nLC-MS/MS using a custom made reference repository of beet protein sequences. When redundant UniProt entries were deleted, a non-redundant leaf apoplastic proteome consisting of 109 proteins was obtained. TargetP and SecretomeP algorithms predicted that 63% of them were secretory proteins. Functional classification of the non-redundant proteins indicated that stress and defense, protein metabolism, cell wall and C metabolism accounted for approximately 75% of the identified proteome. The effects of Fe-deficiency on the leaf apoplast proteome were limited, with only five spots (2.5%) changing in relative abundance, thus suggesting that protein homeostasis in the leaf apoplast fluid is well-maintained upon Fe shortage. The identification of three chitinase isoforms among proteins increasing in relative abundance with Fe-deficiency suggests that one of the few effects of Fe deficiency in the leaf apoplast proteome includes cell wall modifications. Iron resupply to Fe deficient plants changed the relative abundance of 16 spots when compared to either Fe-sufficient or Fe-deficient samples. Proteins identified in these spots can be broadly classified as those responding to Fe-resupply, which included defense and cell wall related proteins, and non-responsive, which are mainly protein metabolism related proteins and whose changes in relative abundance followed the same trend as with Fe-deficiency.