Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project

Omenn, Gilbert S.,Lane, Lydie,Overall, Christopher M.,Corrales, Fernando J.,Schwenk, Jochen M.,Paik, Young-Ki,Van Eyk, Jennifer E.,Liu, Siqi,Snyder, Michael,Baker, Mark S.,Deutsch, Eric W. American Chemical Society 2018 Journal of proteome research Vol.17 No.12

<P>The Human Proteome Project (HPP) annually reports on progress throughout the field in credibly identifying and characterizing the human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2018-01-17, the baseline for this sixth annual HPP special issue of the <I>Journal of Proteome Research</I>, contains 17 470 PE1 proteins, 89% of all neXtProt predicted PE1-4 proteins, up from 17 008 in release 2017-01-23 and 13 975 in release 2012-02-24. Conversely, the number of neXtProt PE2,3,4 missing proteins has been reduced from 2949 to 2579 to 2186 over the past two years. Of the PE1 proteins, 16 092 are based on mass spectrometry results, and 1378 on other kinds of protein studies, notably protein-protein interaction findings. PeptideAtlas has 15 798 canonical proteins, up 625 over the past year, including 269 from SUMOylation studies. The largest reason for missing proteins is low abundance. Meanwhile, the Human Protein Atlas has released its Cell Atlas, Pathology Atlas, and updated Tissue Atlas, and is applying recommendations from the International Working Group on Antibody Validation. Finally, there is progress using the quantitative multiplex organ-specific popular proteins targeted proteomics approach in various disease categories.</P> [FIG OMISSION]</BR>

HUPO Brain Proteome Project: Summary of the pilot phase and introduction of a comprehensive data reprocessing strategy

Hamacher, Michael,Apweiler, Rolf,Arnold, Georg,Becker, Albert,Blü,ggel, Martin,Carrette, Odile,Colvis, Christine,Dunn, Michael J.,Frö,hlich, Thomas,Fountoulakis, Michael,van Hall, André WILEY-VCH Verlag 2006 Proteomics Vol.6 No.18

<P>The Human Proteome Organisation (HUPO) initiated several projects focusing on the proteome analysis of distinct human organs. The Brain Proteome Project (BPP) is the initiative dedicated to the brain, its development and correlated diseases. Two pilot studies have been performed aiming at the comparison of techniques, laboratories and approaches. With the help of the results gained, objective data submission, storage and reprocessing workflow have been established. The biological relevance of the data will be drawn from the inter-laboratory comparisons as well as from the re-calculation of all data sets submitted by the different groups. In the following, results of the single groups as well as the centralised reprocessing effort will be summarised and compared, showing the added value of this concerted work.</P>

Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project

Horvatovich, Pé,ter,Lundberg, Emma K.,Chen, Yu-Ju,Sung, Ting-Yi,He, Fuchu,Nice, Edouard C.,Goode, Robert J.,Yu, Simon,Ranganathan, Shoba,Baker, Mark S.,Domont, Gilberto B.,Velasquez, Erika,Li, D American Chemical Society 2015 Journal of Proteome Research Vol.14 No.9

<P>This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed “missing proteins”) in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP’s activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper’s content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (<uri xlink:href='http://c-hpp.webhosting.rug.nl/' xlink:type='simple'>http://c-hpp.webhosting.rug.nl/</uri>) and in the Supporting Information.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2015/jprobs.2015.14.issue-9/pr5013009/production/images/medium/pr-2014-013009_0005.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/pr5013009'>ACS Electronic Supporting Info</A></P>

Launching the C-HPP neXt-CP50 Pilot Project for Functional Characterization of Identified Proteins with No Known Function

Paik, Young-Ki,Lane, Lydie,Kawamura, Takeshi,Chen, Yu-Ju,Cho, Je-Yoel,LaBaer, Joshua,Yoo, Jong Shin,Domont, Gilberto,Corrales, Fernando,Omenn, Gilbert S.,Archakov, Alexander,Encarnació,n-Guevara American Chemical Society 2018 JOURNAL OF PROTEOME RESEARCH Vol.17 No.12

<P>An important goal of the Human Proteome Organization (HUPO) Chromosome-centric Human Proteome Project (C-HPP) is to correctly define the number of canonical proteins encoded by their cognate open reading frames on each chromosome in the human genome. When identified with high confidence of protein evidence (PE), such proteins are termed PE1 proteins in the online database resource, neXtProt. However, proteins that have not been identified unequivocally at the protein level but that have other evidence suggestive of their existence (PE2-4) are termed missing proteins (MPs). The number of MPs has been reduced from 5511 in 2012 to 2186 in 2018 (neXtProt 2018-01-17 release). Although the annotation of the human proteome has made significant progress, the “parts list” alone does not inform function. Indeed, 1937 proteins representing ∼10% of the human proteome have no function either annotated from experimental characterization or predicted by homology to other proteins. Specifically, these 1937 “dark proteins” of the so-called dark proteome are composed of 1260 functionally uncharacterized but identified PE1 proteins, designated as uPE1, plus 677 MPs from categories PE2-PE4, which also have no known or predicted function and are termed uMPs. At the HUPO-2017 Annual Meeting, the C-HPP officially adopted the uPE1 pilot initiative, with 14 participating international teams later committing to demonstrate the feasibility of the functional <U>c</U>haracterization of large numbers of dark <U>p</U>roteins (CP), starting first with 50 uPE1 proteins, in a stepwise chromosome-centric organizational manner. The second aim of the feasibility phase to <U>c</U>haracterize protein (CP) functions of 50 uPE1 proteins, termed the neXt-CP50 initiative, is to utilize a variety of approaches and workflows according to individual team expertise, interest, and resources so as to enable the C-HPP to recommend experimentally proven workflows to the proteome community within 3 years. The results from this pilot will not only be the cornerstone of a larger characterization initiative but also enhance understanding of the human proteome and integrated cellular networks for the discovery of new mechanisms of pathology, mechanistically informative biomarkers, and rational drug targets.</P> [FIG OMISSION]</BR>

Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0

Deutsch, Eric W.,Lane, Lydie,Overall, Christopher M.,Bandeira, Nuno,Baker, Mark S.,Pineau, Charles,Moritz, Robert L.,Corrales, Fernando,Orchard, Sandra,Van Eyk, Jennifer E.,Paik, Young-Ki,Weintraub, S American Chemical Society 2019 JOURNAL OF PROTEOME RESEARCH Vol.18 No.12

<P>The Human Proteome Organization’s (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.</P> [FIG OMISSION]</BR>

Next Generation Proteomic Pipeline for Chromosome-Based Proteomic Research Using NeXtProt and GENCODE Databases

Hwang, Heeyoun,Park, Gun Wook,Park, Ji Yeong,Lee, Hyun Kyoung,Lee, Ju Yeon,Jeong, Ji Eun,Park, Sung-Kyu Robin,Yates, John R.,Kwon, Kyung-Hoon,Park, Young Mok,Lee, Hyoung-Joo,Paik, Young-Ki,Kim, Jin Yo American Chemical Society 2017 Journal of proteome research Vol.16 No.12

<P>Human Proteome Project aims to map all human proteins including missing proteins as well as proteoforms with post translational modifications, alternative splicing variants (ASVs), and single amino acid variants (SAAVs). neXtProt and Ensemble databases are usually used to provide curated information on human coding genes. However, to find these proteoforms, we (Chr #11 team) first introduce a streamlined pipeline using customized and concatenated neXtProt and GENCODE originated from Ensemble, with controlled false discovery rate (FDR). Because of large sized databases used in this pipeline, we found more stringent FDR filtering (0.1% at the peptide level and 1% at the protein level) to claim novel findings, such as GENCODE ASVs and missing proteins, from human hippocampus data set (MSV000081385) and ProteomeXchange (PXD007166). Using our next generation proteomic pipeline (nextPP) with neXtProt and GENCODE databases, two missing proteins such as activity-regulated cytoskeleton-associated protein (ARC, Chr 8) and glutamate receptor ionotropic, kainite 5 (GRIK5, Chr 19) were additionally identified with two or more unique peptides from human brain tissues. Additionally, by applying the pipeline to human brain related data sets such as cortex (PXD000067 and PXD000561), spinal cord, and fetal brain (PXD000561), seven GENCODE ASVs such as ACTN4–012 (Chr.19), DPYSL2–005 (Chr.8), MPRIP-003 (Chr.17), NCAM1–013 (Chr.11), EPB41L1–017 (Chr.20), AGAP1–004 (Chr.2), and CPNE5–005 (Chr.6) were identified from two or more data sets. The identified peptides of GENCODE ASVs were mapped onto novel exon insertions, alternative translations at 5′-untranslated region, or novel protein coding sequence. Applying the pipeline to male reproductive organ related data sets, 52 GENCODE ASVs were identified from two testis (PXD000561 and PXD002179) and a spermatozoa (PXD003947) data sets. Four out of 52 GENCODE ASVs such as RAB11FIP5–008 (Chr. 2), RP13–347D8.7–001 (Chr. X), PRDX4–002 (Chr. X), and RP11–666A8.13–001 (Chr. 17) were identified in all of the three samples.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2017/jprobs.2017.16.issue-12/acs.jproteome.7b00223/production/images/medium/pr-2017-00223z_0006.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/pr7b00223'>ACS Electronic Supporting Info</A></P>

A Chromosome-centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17

Liu, Suli,Im, Hogune,Bairoch, Amos,Cristofanilli, Massimo,Chen, Rui,Deutsch, Eric W.,Dalton, Stephen,Fenyo, David,Fanayan, Susan,Gates, Chris,Gaudet, Pascale,Hincapie, Marina,Hanash, Samir,Kim, Hoguen American Chemical Society 2013 JOURNAL OF PROTEOME RESEARCH Vol.12 No.1

<P>We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 “missing” proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of “missing” proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2013/jprobs.2013.12.issue-1/pr300985j/production/images/medium/pr-2012-00985j_0009.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/pr300985j'>ACS Electronic Supporting Info</A></P>

Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project

Cho, Jin-Young,Lee, Hyoung-Joo,Jeong, Seul-Ki,Kim, Kwang-Youl,Kwon, Kyung-Hoon,Yoo, Jong Shin,Omenn, Gilbert S.,Baker, Mark S.,Hancock, William S.,Paik, Young-Ki American Chemical Society 2015 JOURNAL OF PROTEOME RESEARCH Vol.14 No.12

<P>Approximately 2.9 billion long base-pair human reference genome sequences are known to encode some 20 000 representative proteins. However, 3000 proteins, that is, ∼15% of all proteins, have no or very weak proteomic evidence and are still missing. Missing proteins may be present in rare samples in very low abundance or be only temporarily expressed, causing problems in their detection and protein profiling. In particular, some technical limitations cause missing proteins to remain unassigned. For example, current mass spectrometry techniques have high limits and error rates for the detection of complex biological samples. An insufficient proteome coverage in a reference sequence database and spectral library also raises major issues. Thus, the development of a better strategy that results in greater sensitivity and accuracy in the search for missing proteins is necessary. To this end, we used a new strategy, which combines a reference spectral library search and a simulated spectral library search, to identify missing proteins. We built the human iRefSPL, which contains the original human reference spectral library and additional peptide sequence-spectrum match entries from other species. We also constructed the human simSPL, which contains the simulated spectra of 173 907 human tryptic peptides determined by MassAnalyzer (version 2.3.1). To prove the enhanced analytical performance of the combination of the human iRefSPL and simSPL methods for the identification of missing proteins, we attempted to reanalyze the placental tissue data set (PXD000754). The data from each experiment were analyzed using PeptideProphet, and the results were combined using iProphet. For the quality control, we applied the class-specific false-discovery rate filtering method. All of the results were filtered at a false-discovery rate of <1% at the peptide and protein levels. The quality-controlled results were then cross-checked with the neXtProt DB (2014-09-19 release). The two spectral libraries, iRefSPL and simSPL, were designed to ensure no overlap of the proteome coverage. They were shown to be complementary to spectral library searching and significantly increased the number of matches. From this trial, 12 new missing proteins were identified that passed the following criterion: at least 2 peptides of 7 or more amino acids in length or one of 9 or more amino acids in length with one or more unique sequences. Thus, the iRefSPL and simSPL combination can be used to help identify peptides that have not been detected by conventional sequence database searches with improved sensitivity and a low error rate.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2015/jprobs.2015.14.issue-12/acs.jproteome.5b00578/production/images/medium/pr-2015-00578f_0008.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/pr5b00578'>ACS Electronic Supporting Info</A></P>

GenomewidePDB 2.0: A Newly Upgraded Versatile Proteogenomic Database for the Chromosome-Centric Human Proteome Project

Jeong, Seul-Ki,Hancock, William S.,Paik, Young-Ki American Chemical Society 2015 Journal of proteome research Vol.14 No.9

<P>Since the launch of the Chromosome-centric Human Proteome Project (C-HPP) in 2012, the number of “missing” proteins has fallen to 2932, down from ∼5932 since the number was first counted in 2011. We compared the characteristics of missing proteins with those of already annotated proteins with respect to transcriptional expression pattern and the time periods in which newly identified proteins were annotated. We learned that missing proteins commonly exhibit lower levels of transcriptional expression and less tissue-specific expression compared with already annotated proteins. This makes it more difficult to identify missing proteins as time goes on. One of the C-HPP goals is to identify alternative spliced product of proteins (ASPs), which are usually difficult to find by shot-gun proteomic methods due to their sequence similarities with the representative proteins. To resolve this problem, it may be necessary to use a targeted proteomics approach (e.g., selected and multiple reaction monitoring [S/MRM] assays) and an innovative bioinformatics platform that enables the selection of target peptides for rarely expressed missing proteins or ASPs. Given that the success of efforts to identify missing proteins may rely on more informative public databases, it was necessary to upgrade the available integrative databases. To this end, we attempted to improve the features and utility of GenomewidePDB by integrating transcriptomic information (e.g., alternatively spliced transcripts), annotated peptide information, and an advanced search interface that can find proteins of interest when applying a targeted proteomics strategy. This upgraded version of the database, GenomewidePDB 2.0, may not only expedite identification of the remaining missing proteins but also enhance the exchange of information among the proteome community. GenomewidePDB 2.0 is available publicly at <uri xlink:href='http://genomewidepdb.proteomix.org/' xlink:type='simple'>http://genomewidepdb.proteomix.org/</uri>.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2015/jprobs.2015.14.issue-9/acs.jproteome.5b00541/production/images/medium/pr-2015-00541m_0009.gif'></P>

Bioinformatics Annotation of Human Y Chromosome-Encoded Protein Pathways and Interactions

Rengaraj, Deivendran,Kwon, Woo-Sung,Pang, Myung-Geol American Chemical Society 2015 JOURNAL OF PROTEOME RESEARCH Vol.14 No.9

<P>We performed a comprehensive analysis of human Y chromosome-encoded proteins, their pathways, and their interactions using bioinformatics tools. From the NCBI annotation release 107 of human genome, we retrieved a total of 66 proteins encoded on Y chromosome. Most of the retrieved proteins were also matched with the proteins listed in the core databases of the Human Proteome Project including neXtProt, PeptideAtlas, and the Human Protein Atlas. When we examined the pathways of human Y-encoded proteins through KEGG database and Pathway Studio software, many of proteins fall into the categories related to cell signaling pathways. Using the STRING program, we found a total of 49 human Y-encoded proteins showing strong/medium interaction with each other. While using the Pathway studio software, we found that a total of 16 proteins interact with other chromosome-encoded proteins. In particular, the SRY protein interacted with 17 proteins encoded on other chromosomes. Additionally, we aligned the sequences of human Y-encoded proteins with the sequences of chimpanzee and mouse Y-encoded proteins using the NCBI BLAST program. This analysis resulted in a significant number of orthologous proteins between human, chimpanzee, and mouse. Collectively, our findings provide the scientific community with additional information on the human Y chromosome-encoded proteins.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jprobs/2015/jprobs.2015.14.issue-9/acs.jproteome.5b00491/production/images/medium/pr-2015-00491g_0007.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/pr5b00491'>ACS Electronic Supporting Info</A></P>