Editing Proteome

{{short description|Set of proteins that can be expressed by a genome, cell, tissue, or organism}}
[[Image:Metabolomics schema.png|thumb|400px|General schema showing the relationships of the [[genome]], [[transcriptome]], proteome, and [[metabolome]] ([[lipidome]]).]]

A '''proteome''' is the entire set of [[protein]]s that is, or can be, expressed by a [[genome]], cell, tissue, or organism at a certain time. It is the set of expressed proteins in a given type of cell or organism, at a given time, under defined conditions. [[Proteomics]] is the study of the proteome.

==Types of proteomes==
While proteome generally refers to the proteome of an organism, multicellular organisms may have very different proteomes in different cells, hence it is important to distinguish proteomes in cells and organisms.

A '''cellular proteome''' is the collection of proteins found in a particular [[cell (biology)|cell]] type under a particular set of environmental conditions such as exposure to [[hormone|hormone stimulation]].

It can also be useful to consider an organism's '''complete proteome''', which can be conceptualized as the complete set of proteins from all of the various cellular proteomes. This is very roughly the protein equivalent of the [[genome]].

The term ''proteome'' has also been used to refer to the collection of proteins in certain '''sub-cellular systems''', such as organelles. For instance, the [[Mitochondrion|mitochondrial]] proteome may consist of more than 3000 distinct proteins.<ref>{{cite journal |last1=Johnson |first1=D. T. |last2=Harris |first2=R. A. |last3=French |first3=S. |last4=Blair |first4=P. V. |last5=You |first5=J. |last6=Bemis |first6=K. G. |last7=Wang |first7=M. |last8=Balaban |first8=R. S. |date=2006 |title=Tissue heterogeneity of the mammalian mitochondrial proteome |url=https://doi.org/10.1152/ajpcell.00108.2006 |journal=American Journal of Physiology. Cell Physiology |volume=292 |issue=2 |pages=c689–c697 | pmid=16928776 | doi=10.1152/ajpcell.00108.2006|s2cid=24412700 }}</ref><ref>{{Cite journal|last1=Morgenstern|first1=Marcel|last2=Stiller|first2=Sebastian B.|last3=Lübbert|first3=Philipp|last4=Peikert|first4=Christian D.|last5=Dannenmaier|first5=Stefan|last6=Drepper|first6=Friedel|last7=Weill|first7=Uri|last8=Höß|first8=Philipp|last9=Feuerstein|first9=Reinhild|last10=Gebert|first10=Michael|last11=Bohnert|first11=Maria|date=June 2017|title=Definition of a High-Confidence Mitochondrial Proteome at Quantitative Scale|url= |journal=Cell Reports|volume=19|issue=13|pages=2836–2852|doi=10.1016/j.celrep.2017.06.014|issn=2211-1247|pmc=5494306|pmid=28658629}}</ref><ref>{{Cite journal|last=Gómez-Serrano|first=M|date=November 2018|title=Mitoproteomics: Tackling Mitochondrial Dysfunction in Human Disease.|journal=Oxid Med Cell Longev|volume=2018|pages=1435934|pmid=30533169|pmc=6250043|doi=10.1155/2018/1435934|doi-access=free}}</ref>

The proteins in a '''virus''' can be called a ''[[Viral evolution#Origins|viral proteome]]''. Usually viral proteomes are predicted from the viral genome<ref>{{Cite journal|last=Uetz|first=P.|date=2004-10-15|title=From ORFeomes to Protein Interaction Maps in Viruses|journal=Genome Research|language=en|volume=14|issue=10b|pages=2029–2033|doi=10.1101/gr.2583304|pmid=15489322|issn=1088-9051|doi-access=free}}</ref> but some attempts have been made to determine all the proteins expressed from a virus genome, i.e. the viral proteome.<ref>{{Cite journal|last1=Maxwell|first1=Karen L.|last2=Frappier|first2=Lori|date=June 2007|title=Viral proteomics|journal=Microbiology and Molecular Biology Reviews |volume=71|issue=2|pages=398–411|doi=10.1128/MMBR.00042-06|issn=1092-2172|pmc=1899879|pmid=17554050}}</ref> More often, however, virus proteomics analyzes the changes of host proteins upon virus infection, so that in effect ''two'' proteomes (of virus and its host) are studied.<ref>{{Cite journal|last1=Viswanathan|first1=Kasinath|last2=Früh|first2=Klaus|date=December 2007|title=Viral proteomics: global evaluation of viruses and their interaction with the host|url=https://pubmed.ncbi.nlm.nih.gov/18067418|journal=Expert Review of Proteomics|volume=4|issue=6|pages=815–829|doi=10.1586/14789450.4.6.815|issn=1744-8387|pmid=18067418|s2cid=25742649}}</ref>

== Importance in cancer ==
[[File:Protein Patterns and Diagnosis.jpg|thumb|The proteome can be used to determine the presence of different types of cancers.]]

The proteome can be used in order to comparatively analyze different [[cancer cell]] lines. Proteomic studies have been used in order to identify the likelihood of [[metastasis]] in [[bladder cancer]] cell lines KK47 and YTS1 and were found to have 36 unregulated and 74 down regulated proteins.<ref>{{Cite journal|last1=Yang|first1=Ganglong|last2=Xu|first2=Zhipeng|last3=Lu|first3=Wei|last4=Li|first4=Xiang|last5=Sun|first5=Chengwen|last6=Guo|first6=Jia|last7=Xue|first7=Peng|last8=Guan|first8=Feng|date=2015-07-31|title=Quantitative Analysis of Differential Proteome Expression in Bladder Cancer vs. Normal Bladder Cells Using SILAC Method|journal=PLOS ONE|volume=10|issue=7|pages=e0134727|doi=10.1371/journal.pone.0134727|pmid=26230496|pmc=4521931|issn=1932-6203|bibcode=2015PLoSO..1034727Y|doi-access=free}}</ref> The differences in protein expression can help identify novel cancer signaling mechanisms.

[[Biomarker]]s of cancer have been found by [[mass spectrometry]] based proteomic analyses. The use of proteomics or the study of the proteome is a step forward in personalized medicine to tailor drug cocktails to the patient's specific proteomic and genomic profile.<ref>{{Cite journal|last1=An|first1=Yao|last2=Zhou|first2=Li|last3=Huang|first3=Zhao|last4=Nice|first4=Edouard C.|last5=Zhang|first5=Haiyuan|last6=Huang|first6=Canhua|date=2019-05-04|title=Molecular insights into cancer drug resistance from a proteomics perspective|journal=Expert Review of Proteomics|volume=16|issue=5|pages=413–429|doi=10.1080/14789450.2019.1601561|issn=1478-9450|pmid=30925852|s2cid=88474614}}</ref> The analysis of [[ovarian cancer]] cell lines showed that putative [[biomarkers]] for ovarian cancer include "α-enolase (ENOA), [[EF-Tu|elongation factor Tu]], mitochondrial (EFTU), [[Glyceraldehyde 3-phosphate dehydrogenase|glyceraldehyde-3-phosphate dehydrogenase (G3P)]], stress-70 protein, mitochondrial (GRP75), [[Apolipoprotein A1|apolipoprotein A-1 (APOA1)]], peroxiredoxin (PRDX2) and [[Annexin A1|annexin A (ANXA)]]".<ref>{{Cite journal|last1=Cruz|first1=Isa N.|last2=Coley|first2=Helen M.|last3=Kramer|first3=Holger B.|last4=Madhuri|first4=Thumuluru Kavitah|last5=Safuwan|first5=Nur a. M.|last6=Angelino|first6=Ana Rita|last7=Yang|first7=Min|date=2017-01-01|title=Proteomics Analysis of Ovarian Cancer Cell Lines and Tissues Reveals Drug Resistance-associated Proteins|url=http://cgp.iiarjournals.org/content/14/1/35|journal= Cancer Genomics & Proteomics|language=en|volume=14|issue=1|pages=35–51|issn=1109-6535|pmid=28031236|pmc=5267499|doi=10.21873/cgp.20017}}</ref>

Comparative proteomic analyses of 11 cell lines demonstrated the similarity between the metabolic processes of each cell line; 11,731 proteins were completely identified from this study. Housekeeping proteins tend to show greater variability between cell lines.<ref>{{Cite journal|last1=Geiger|first1=Tamar|last2=Wehner|first2=Anja|last3=Schaab|first3=Christoph|last4=Cox|first4=Juergen|last5=Mann|first5=Matthias|date=March 2012|title=Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins|journal=Molecular & Cellular Proteomics |volume=11|issue=3|pages=M111.014050|doi=10.1074/mcp.M111.014050|doi-access=free |issn=1535-9476|pmc=3316730|pmid=22278370}}</ref>

Resistance to certain cancer drugs is still not well understood. Proteomic analysis has been used in order to identify proteins that may have anti-cancer drug properties, specifically for the [[colon cancer]] drug [[irinotecan]].<ref>{{Cite journal|last1=Peng|first1=Xing-Chen|last2=Gong|first2=Feng-Ming|last3=Wei|first3=Meng|last4=Chen|first4=Xi|last5=Chen|first5=Ye|last6=Cheng|first6=Ke|last7=Gao|first7=Feng|last8=Xu|first8=Feng|last9=Bi|first9=Feng|last10=Liu|first10=Ji-Yan|date=December 2010|title=Proteomic analysis of cell lines to identify the irinotecan resistance proteins|journal=Journal of Biosciences|language=en|volume=35|issue=4|pages=557–564|doi=10.1007/s12038-010-0064-9|pmid=21289438|s2cid=6082637|issn=0250-5991}}</ref> Studies of [[adenocarcinoma]] cell line LoVo demonstrated that 8 proteins were unregulated and 7 proteins were down-regulated. Proteins that showed a differential expression were involved in processes such as [[Transcription (biology)|transcription]], [[apoptosis]] and [[cell proliferation]]/[[Cellular differentiation|differentiation]] among others.

== The proteome in bacterial systems ==
Proteomic analyses have been performed in different kinds of bacteria to assess their metabolic reactions to different conditions. For example, in bacteria such as ''[[Clostridium]]'' and ''[[Bacillus]]'', proteomic analyses were used in order to investigate how different proteins help each of these bacteria spores germinate after a prolonged period of dormancy.<ref>{{Cite journal|last1=Chen|first1=Yan|last2=Barat|first2=Bidisha|last3=Ray|first3=W. Keith|last4=Helm|first4=Richard F.|last5=Melville|first5=Stephen B.|last6=Popham|first6=David L.|date=2019-03-15|title=Membrane Proteomes and Ion Transporters in Bacillus anthracis and Bacillus subtilis Dormant and Germinating Spores|journal=Journal of Bacteriology|language=en|volume=201|issue=6|doi=10.1128/JB.00662-18|issn=0021-9193|pmid=30602489|pmc=6398275}}</ref> In order to better understand how to properly eliminate spores, proteomic analysis must be performed.

==History==
[[Marc Wilkins (geneticist)|Marc Wilkins]] coined the term ''proteome'' <ref>{{cite journal|last=Wilkins|first=Marc|date=Dec 2009|title=Proteomics data mining|journal=Expert Review of Proteomics|volume=6|issue=6|pages=599–603|location = [[England]]| pmid = 19929606|doi = 10.1586/epr.09.81|s2cid=207211912}}</ref> in 1994 in a symposium on "2D Electrophoresis: from protein maps to genomes" held in Siena in Italy. It appeared in print in 1995,<ref>{{cite journal|vauthors=Wasinger VC, Cordwell SJ, Cerpa-Poljak A, Yan JX, Gooley AA, Wilkins MR, Duncan MW, Harris R, Williams KL, Humphery-Smith I | journal=Electrophoresis| year=1995|title= Progress with gene-product mapping of the Mollicutes: Mycoplasma genitalium| pmid = 7498152| volume=16| pages=1090–94| issue=1| doi=10.1002/elps.11501601185| s2cid=9269742}}</ref> with the publication of part of his PhD thesis. Wilkins used the term to describe the entire complement of [[protein]]s expressed by a genome, cell, tissue or organism.

==Size and contents==
The genomes of '''[[virus]]es''' and '''[[prokaryote]]s''' encode a relatively well-defined proteome as each protein can be predicted with high confidence, based on its [[open reading frame]] (in viruses ranging from ~3 to ~1000, in bacteria ranging from about 500 proteins to about 10,000).<ref>{{cite journal|last1=Kozlowski|first1=LP|date=26 October 2016|title=Proteome-''pI'': proteome isoelectric point database|journal=Nucleic Acids Research|volume=45|issue=D1|pages=D1112–D1116|doi=10.1093/nar/gkw978|pmc=5210655|pmid=27789699}}</ref> However, most [[Gene prediction|protein prediction]] algorithms use certain cut-offs, such as 50 or 100 amino acids, so small proteins are often missed by such predictions.<ref>{{Cite journal|last=Leslie|first=Mitch|date=2019-10-18|title=Outsize impact|url=https://www.science.org/doi/10.1126/science.366.6463.296|journal=Science|language=en|volume=366|issue=6463|pages=296–299|doi=10.1126/science.366.6463.296|pmid=31624194|bibcode=2019Sci...366..296L|s2cid=204774732 |issn=0036-8075}}</ref> In [[eukaryota|'''eukaryotes''']] this becomes much more complicated as more than one [[protein]] can be produced from most [[gene]]s due to [[alternative splicing]] (e.g. human genome encodes about 20,000 proteins, but some estimates predicted 92,179 proteins{{citation needed|date=January 2019}} out of which 71,173 are splicing variants{{citation needed|date=January 2019}}).<ref>{{cite journal|title=UniProt: a hub for protein information|journal=Nucleic Acids Research|volume=43|issue=D1|year=2014|pages=D204–D212|issn=0305-1048|doi=10.1093/nar/gku989|pmid=25348405|pmc=4384041 |last1=Uniprot |first1=Consortium }}</ref>

'''Association of proteome size with DNA repair capability'''

The concept of “proteomic constraint” is that [[DNA repair]] capacity is positively correlated with the information content of a [[genome]], which, in turn, is approximately related to the size of the proteome.<ref name = Acosta2015>Acosta S, Carela M, Garcia-Gonzalez A, Gines M, Vicens L, Cruet R, Massey SE. DNA Repair Is Associated with Information Content in Bacteria, Archaea, and DNA Viruses. J Hered. 2015 Sep-Oct;106(5):644-59. doi: 10.1093/jhered/esv055. Epub 2015 Aug 29. PMID: 26320243</ref>  In [[bacteria]], [[archaea]] and [[DNA virus]]es, DNA repair capability is positively related to genome information content and to genome size.<ref name = Acosta2015/>    “Proteomic constraint” proposes that modulators of mutation rates such as DNA repair genes are subject to selection pressure proportional to the amount of information in a genome.<ref name = Acosta2015/>

'''Proteoforms'''. There are different factors that can add variability to proteins. SAPs (single amino acid polymorphisms) and non-synonymous single nucleotide polymorphisms (nsSNPs) can lead to different "proteoforms"<ref name=":0">{{Cite journal|last1=Aebersold|first1=Ruedi|last2=Agar|first2=Jeffrey N|last3=Amster|first3=I Jonathan|last4=Baker|first4=Mark S|last5=Bertozzi|first5=Carolyn R|last6=Boja|first6=Emily S|last7=Costello|first7=Catherine E|last8=Cravatt|first8=Benjamin F|last9=Fenselau|first9=Catherine|last10=Garcia|first10=Benjamin A|last11=Ge|first11=Ying|date=March 2018|title=How many human proteoforms are there?|url= |journal=Nature Chemical Biology|language=en|volume=14|issue=3|pages=206–214|doi=10.1038/nchembio.2576|issn=1552-4450|pmc=5837046|pmid=29443976|hdl=1721.1/120977}}</ref> or "proteomorphs". Recent estimates have found ~135,000 validated nonsynonymous cSNPs currently housed within SwissProt. In dbSNP, there are 4.7 million candidate cSNPs, yet only ~670,000 cSNPs have been validated in the 1,000-genomes set as nonsynonymous cSNPs that change the identity of an amino acid in a protein.<ref name=":0" />

'''Dark proteome'''. The term [[dark proteome]] coined by Perdigão and colleagues, defines regions of proteins that have no detectable [[sequence homology]] to other proteins of known [[protein tertiary structure|three-dimensional structure]] and therefore cannot be [[homology modeling|modeled by homology]]. For 546,000 Swiss-Prot proteins, 44–54% of the proteome in [[eukaryote]]s and viruses was found to be "dark", compared with only ~14% in [[archaea]] and [[bacteria]].<ref>{{cite journal | last1 = Perdigão | first1 = Nelson | display-authors=etal | year = 2015 | title = Unexpected features of the dark proteome | journal = PNAS | volume = 112 | issue = 52| pages = 15898–15903 | doi = 10.1073/pnas.1508380112 | pmid=26578815 | pmc=4702990| bibcode = 2015PNAS..11215898P | doi-access = free }}</ref>

'''Human proteome'''. Currently, several projects aim to map the human proteome, including the [http://www.humanproteomemap.org/index.php Human Proteome Map], [https://www.proteomicsdb.org/ ProteomicsDB], [https://www.isoform.io isoform.io], and [https://www.hupo.org/human-proteome-project The Human Proteome Project (HPP)]. Much like the [[human genome project]], these projects seek to find and collect evidence for all predicted protein coding genes in the human genome. The Human Proteome Map currently (October 2020) claims 17,294 proteins and ProteomicsDB 15,479, using different criteria. On October 16, 2020, the HPP published a high-stringency blueprint <ref>{{cite journal|last=Adhikari|first=S|title=A high-stringency blueprint of the human proteome|journal=Nature Communications|date=October 2020|volume=11|issue=1|page=5301|doi=10.1038/s41467-020-19045-9|pmid=33067450|pmc=7568584|bibcode=2020NatCo..11.5301A}}</ref> covering more than 90% of the predicted protein coding genes. Proteins are identified from a wide range of fetal and adult tissues and cell types, including [[hematopoietic stem cells|hematopoietic cells]].

==Methods to study the proteome==
[[File:2D gel color coding.jpg|thumb|This image shows a two-dimensional gel with color-coded proteins. This is a way to visualize proteins based on their mass and isoelectric point.]]
{{Main|Proteomics}}

Analyzing proteins proves to be more difficult than analyzing nucleic acid sequences. While there are only 4 nucleotides that make up DNA, there are at least [[Proteinogenic amino acids|20 different amino acids that can make up a protein.]] Additionally, there is currently no known [[high throughput biology|high throughput]] technology to make copies of a single protein. Numerous methods are available to study proteins, sets of proteins, or the whole proteome. In fact, proteins are often studied indirectly, e.g. using computational methods and analyses of genomes. Only a few examples are given below.

===Separation techniques and electrophoresis===
[[Proteomics]], the study of the proteome, has largely been practiced through the separation of proteins by [[two dimensional gel electrophoresis]].  In the first dimension, the proteins are separated by [[isoelectric focusing]], which resolves proteins on the basis of charge.  In the second dimension, proteins are separated by [[Molecular mass|molecular weight]] using [[SDS-PAGE]].  The gel is [[Staining|stained]] with [[Coomassie brilliant blue]] or [[Silver staining|silver]] to visualize the proteins.  Spots on the gel are proteins that have migrated to specific locations.

===Mass spectrometry===
[[Image:ThermoScientificOrbitrapElite.JPG|thumb|An Orbitrap [[mass spectrometer]] commonly used in proteomics]]
{{main|Protein mass spectrometry|Mass spectrometry}}

[[Mass spectrometry]] is one of the key methods to study the proteome.<ref>{{cite journal|last=Altelaar|first=AF|author2=Munoz, J |author3=Heck, AJ |title=Next-generation proteomics: towards an integrative view of proteome dynamics.|journal=Nature Reviews Genetics|date=January 2013|volume=14|issue=1|pages=35–48|pmid=23207911|doi=10.1038/nrg3356|s2cid=10248311}}</ref> Some important mass spectrometry methods include Orbitrap Mass Spectrometry, [[MALDI]] (Matrix Assisted Laser Desorption/Ionization), and [[Electrospray ionization|ESI (Electrospray Ionization).]] [[Peptide mass fingerprinting]] identifies a protein by cleaving it into short peptides and then deduces the protein's identity by matching the observed peptide masses against a [[sequence database]].  [[Tandem mass spectrometry]], on the other hand, can get sequence information from individual peptides by isolating them, colliding them with a non-reactive gas, and then cataloguing the fragment [[Ion (physics)|ion]]s produced.<ref>{{cite journal |title=Mass-Spectrometry-Based Draft of the Human Proteome |url=https://www.jpt.com/literature/Mass-Spectrometry-Based-Draft-of-the-Human-Proteome |journal=[[Nature (journal)|Nature]] |volume=509 |issue=7502 |pages=582–7 |bibcode=2014Natur.509..582W |last1=Wilhelm |first1=Mathias |last2=Schlegl |first2=Judith |last3=Hahne |first3=Hannes |last4=Gholami |first4=Amin Moghaddas |last5=Lieberenz |first5=Marcus |last6=Savitski |first6=Mikhail M. |last7=Ziegler |first7=Emanuel |last8=Butzmann |first8=Lars |last9=Gessulat |first9=Siegfried |last10=Marx |first10=Harald |last11=Mathieson |first11=Toby |last12=Lemeer |first12=Simone |last13=Schnatbaum |first13=Karsten |last14=Reimer |first14=Ulf |last15=Wenschuh |first15=Holger |last16=Mollenhauer |first16=Martin |last17=Slotta-Huspenina |first17=Julia |last18=Boese |first18=Joos-Hendrik |last19=Bantscheff |first19=Marcus |last20=Gerstmair |first20=Anja |last21=Faerber |first21=Franz |last22=Kuster |first22=Bernhard |year=2014 |doi=10.1038/nature13319 |pmid=24870543 |s2cid=4467721 |access-date=2016-09-29 |archive-date=2018-08-20 |archive-url=https://web.archive.org/web/20180820005653/https://www.jpt.com/literature/Mass-Spectrometry-Based-Draft-of-the-Human-Proteome |url-status=dead }}</ref>

In May 2014, a draft map of the human proteome was published in ''[[Nature (journal)|Nature]]''.<ref>{{cite journal|last1=Kim|first1=Min-Sik|title=A draft map of the human proteome|journal=Nature|volume=509|doi=10.1038/nature13302|pmid=24870542|issue=7502|date=May 2014|pages=575–81|display-authors=etal|pmc=4403737|bibcode=2014Natur.509..575K}}</ref> This map was generated using high-resolution Fourier-transform mass spectrometry. This study profiled 30 histologically normal human samples resulting in the identification of proteins coded by 17,294 genes. This accounts for around 84% of the total annotated protein-coding genes.

=== Chromatography ===
Liquid [[chromatography]] is an important tool in the study of the proteome. It allows for very sensitive separation of different kinds of proteins based on their affinity for a matrix. Some newer methods for the separation and identification of proteins include the use of monolithic capillary columns, high temperature chromatography and capillary electrochromatography.<ref>{{Cite journal|last1=Shi|first1=Yang|last2=Xiang|first2=Rong|last3=Horváth|first3=Csaba|last4=Wilkins|first4=James A.|date=2004-10-22|title=The role of liquid chromatography in proteomics|journal=Journal of Chromatography A|series=Bioanalytical Chemistry: Perspectives and Recent Advances with Recognition of Barry L. Karger|volume=1053|issue=1|pages=27–36|doi=10.1016/j.chroma.2004.07.044|pmid=15543969|issn=0021-9673}}</ref>

=== Blotting ===
[[Western blot]]ting can be used in order to quantify the abundance of certain proteins. By using antibodies specific to the protein of interest, it is possible to probe for the presence of specific proteins from a mixture of proteins.

=== Protein complementation assays and interaction screens ===
[[Protein-fragment complementation assay]]s are often used to detect [[protein–protein interaction]]s. The [[Two-hybrid screening|yeast two-hybrid assay]] is the most popular of them but there are numerous variations, both used ''[[in vitro]]'' and ''[[in vivo]]''. Pull-down assays are a method to determine the protein binding partners of a given protein.<ref>{{Cite web|url=https://www.thermofisher.com/us/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/pull-down-assays.html|title=Pull-Down Assays - US|website=www.thermofisher.com|language=en|access-date=2019-12-05}}</ref>

=== Protein structure prediction ===
[[Protein structure prediction]] can be used to provide three-dimensional protein structure predictions of whole proteomes. In 2022, a large-scale collaboration between [[European Molecular Biology Laboratory|EMBL-EBI]] and [[DeepMind]] provided predicted structures for over 200 million proteins from across the tree of life.<ref>{{Cite journal |last=Callaway |first=Ewen |date=2022-07-28 |title='The entire protein universe': AI predicts shape of nearly every known protein |journal=Nature |language=en |volume=608 |issue=7921 |pages=15–16 |doi=10.1038/d41586-022-02083-2|pmid=35902752 |bibcode=2022Natur.608...15C |s2cid=251159714 |doi-access=free }}</ref> Smaller projects have also used protein structure prediction to help map the proteome of individual organisms, for example [https://www.isoform.io isoform.io] provides coverage of multiple protein isoforms for over 20,000 genes in the [[human genome]].<ref>{{Cite journal |last1=Sommer |first1=Markus J. |last2=Cha |first2=Sooyoung |last3=Varabyou |first3=Ales |last4=Rincon |first4=Natalia |last5=Park |first5=Sukhwan |last6=Minkin |first6=Ilia |last7=Pertea |first7=Mihaela |last8=Steinegger |first8=Martin |last9=Salzberg |first9=Steven L. |date=2022-12-15 |title=Structure-guided isoform identification for the human transcriptome |journal=eLife |volume=11 |pages=e82556 |language=en |doi=10.7554/eLife.82556|pmid=36519529 |pmc=9812405 |doi-access=free }}</ref>

== Protein databases ==
The [https://www.proteinatlas.org/ Human Protein Atlas] contains information about the human proteins in cells, tissues, and organs. All the data in the knowledge resource is open access to allow scientists both in academia and industry to freely access the data for exploration of the human proteome. The organization [https://elixir-europe.org ELIXIR] has selected the protein atlas as a core resource due to its fundamental importance for a wider life science community.

The [http://www.plasmaproteomedatabase.org Plasma Proteome database] {{Webarchive|url=https://web.archive.org/web/20210127090529/http://www.plasmaproteomedatabase.org/ |date=2021-01-27 }} contains information on 10,500 [[blood plasma]] proteins. Because the range in protein contents in plasma is very large, it is difficult to detect proteins that tend to be scarce when compared to abundant proteins. This is an analytical limit that may possibly be a barrier for the detections of proteins with ultra low concentrations.<ref name="Ponomarenko">{{Cite journal|last1=Ponomarenko|first1=Elena A.|last2=Poverennaya|first2=Ekaterina V.|last3=Ilgisonis|first3=Ekaterina V.|last4=Pyatnitskiy|first4=Mikhail A.|last5=Kopylov|first5=Arthur T.|last6=Zgoda|first6=Victor G.|last7=Lisitsa|first7=Andrey V.|last8=Archakov|first8=Alexander I.|date=2016|title=The Size of the Human Proteome: The Width and Depth|journal=International Journal of Analytical Chemistry|volume=2016|pages=7436849|doi=10.1155/2016/7436849|issn=1687-8760|pmc=4889822|pmid=27298622|doi-access=free}}</ref>

Databases such as [https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkz995/5625540?guestAccessKey=57d5366c-20f0-4a32-96b9-f20c8780442a neXtprot] and [https://www.uniprot.org/help/human_proteome UniProt] are central resources for human proteomic data.

== See also ==
* [[Metabolome]]
* [[Cytome]]
* [[Bioinformatics]]
* [[List of omics topics in biology]]
* [[Plant Proteome Database]]
* [[Transcriptome]]
* [[Interactome]]
* [[Human Proteome Project]]
* [[BioPlex]]
* [[Human Protein Atlas]]

==References==
{{Reflist}}

== External links ==
* [https://web.archive.org/web/20140312021627/http://pir.georgetown.edu/ PIR database] 
* [https://www.uniprot.org/ UniProt database]
* {{webarchive |url=http://webarchive.loc.gov/all/20110506030957/http%3A//pfam.sanger.ac.uk/ |title=Pfam database |date=2011-05-06}}

{{Protein topics}}

[[Category:Proteomics]]