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A draft map of the human proteome

Nature volume 509, pages 575–581 (2014)Cite this article

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Abstract

The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

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Figure 1: Overview of the workflow and comparison of data with public repositories.
Figure 2: Landscape of the normal human proteome.
Figure 3: Isoform-specific expression.
Figure 4: Proteogenomic analysis.
Figure 5: Translation of pseudogenes and identification of novel N termini.

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Acknowledgements

We would like to acknowledge the National Development and Research Institutes for some of the tissues. We acknowledge the assistance of V. Sandhya, V. Puttamallesh, U. Guha and B. Cole for help with analysis of some of the samples. We thank L. Lane and B. Amos for their assistance with the list of missing genes. This work was supported by an NIH roadmap grant for Technology Centers of Networks and Pathways (U54GM103520), NCI’s Clinical Proteomic Tumor Analysis Consortium initiative (U24CA160036), a contract (HHSN268201000032C) from the National Heart, Lung and Blood Institute and the Sol Goldman Pancreatic Cancer Research Center. The authors acknowledge the joint participation by the Adrienne Helis Malvin Medical Research Foundation and the Diana Helis Henry Medical Research Foundation through its direct engagement in the continuous active conduct of medical research in conjunction with The Johns Hopkins Hospital and the Johns Hopkins University School of Medicine and the Foundation’s Parkinson’s Disease Programs. The analysis work was partially supported by the National Resource for Network Biology (P41GM103504). A.Mah., S.K.Sh., P.S. and T.S.K.P. are supported by DBT Program Support on Neuroproteomics (BT/01/COE/08/05) to IOB and NIMHANS. H.G. is a Wellcome Trust-DBT India Alliance Early Career Fellow. We thank Council of Scientific and Industrial Research, University Grants Commission and Department of Science and Technology, Government of India for research fellowships for S.M.P., R.S.N., A.R., M.K., G.J.S., S.C., P.R., J.S., S.S.M., D.S.K., S.R., S.K.Sr., K.K.D., Y.S., A.S., S.D.Y., N.S., S.A. and G.D.

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Author notes

  1. Candace L. Kerr

    Present address: Present address: Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.,

Authors and Affiliations

  1. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    Min-Sik Kim, Derese Getnet, Raghothama Chaerkady, Pamela Leal-Rojas, Samarjeet Prasad, Tai-Chung Huang, Jun Zhong, Xinyan Wu, Patrick G. Shaw, Donald Freed, Christopher J. Mitchell, Steven D. Leach & Akhilesh Pandey

  2. Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    Min-Sik Kim, Raghothama Chaerkady, Xinyan Wu, Muhammad S. Zahari & Akhilesh Pandey

  3. Institute of Bioinformatics, International Tech Park, Bangalore 560066, India,

    Sneha M. Pinto, Raja Sekhar Nirujogi, Srikanth S. Manda, Anil K. Madugundu, Dhanashree S. Kelkar, Joji K. Thomas, Babylakshmi Muthusamy, Praveen Kumar, Nandini A. Sahasrabuddhe, Lavanya Balakrishnan, Jayshree Advani, Bijesh George, Santosh Renuse, Lakshmi Dhevi N. Selvan, Arun H. Patil, Vishalakshi Nanjappa, Aneesha Radhakrishnan, Tejaswini Subbannayya, Rajesh Raju, Manish Kumar, Sreelakshmi K. Sreenivasamurthy, Arivusudar Marimuthu, Gajanan J. Sathe, Sandip Chavan, Keshava K. Datta, Yashwanth Subbannayya, Apeksha Sahu, Soujanya D. Yelamanchi, Savita Jayaram, Pavithra Rajagopalan, Jyoti Sharma, Krishna R. Murthy, Nazia Syed, Renu Goel, Aafaque A. Khan, Sartaj Ahmad, Gourav Dey, Aditi Chatterjee, Ravi Sirdeshmukh, T. S. Keshava Prasad, Harsha Gowda & Akhilesh Pandey

  4. Adrienne Helis Malvin Medical Research Foundation, New Orleans, 70130, Louisiana, USA

    Derese Getnet & Akhilesh Pandey

  5. The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada,

    Ruth Isserlin, Shobhit Jain & Gary D. Bader

  6. Department of Pathology, Universidad de La Frontera, Center of Genetic and Immunological Studies-Scientific and Technological Bioresource Nucleus, Temuco 4811230, Chile,

    Pamela Leal-Rojas

  7. School of Medicine, Imperial College London, South Kensington Campus, London SW7 2AZ, UK,

    Keshav Mudgal

  8. Department of Neurosurgery, Postgraduate Institute of Medical Education & Research, Chandigarh 160012, India,

    Kanchan K. Mukherjee

  9. Department of Internal Medicine Armed Forces Medical College, Pune 411040, India,

    Subramanian Shankar

  10. Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India,

    Anita Mahadevan & Susarla Krishna Shankar

  11. Human Brain Tissue Repository, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore 560029, India,

    Anita Mahadevan & Susarla Krishna Shankar

  12. Department of Chemical and Biomolecular Engineering and Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong,

    Henry Lam

  13. Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India,

    Parthasarathy Satishchandra

  14. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, 21224, Maryland, USA

    John T. Schroeder

  15. Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, 21231, Maryland, USA

    Anirban Maitra, Marc K. Halushka, Ralph H. Hruban, Christine A. Iacobuzio-Donahue & Akhilesh Pandey

  16. Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, 21231, Maryland, USA

    Anirban Maitra, Charles G. Drake, Ralph H. Hruban, Christine A. Iacobuzio-Donahue & Akhilesh Pandey

  17. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, 21231, Maryland, USA

    Steven D. Leach & Christine A. Iacobuzio-Donahue

  18. Departments of Immunology and Urology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, 21231, Maryland, USA

    Charles G. Drake

  19. Department of Obstetrics and Gynecology, Johns Hopkins University School of Medicine Baltimore, 21205, Maryland, USA

    Candace L. Kerr

  20. Diana Helis Henry Medical Research Foundation, New Orleans, 70130, Louisiana, USA

    Akhilesh Pandey

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Contributions

A.P., H.G., R.C., M.-S.K. designed the study; A.P., H.G., M.-S.K. managed the study; D.G., C.L.K., C.A.I.-D., K.R.M. collected human cells/tissues; M.-S.K., R.C., D.G. developed the pipeline of experiment and analysis; D.G., M.-S.K., S.M.P., K.M., R.C., S.R., J.Z., X.W., P.G.S., M.S.Z., T.-C.H. prepared peptide samples for LC-MS/MS; M.-S.K., R.S.N., S.M.P., R.C., D.S.K., S.R., G.J.S. performed LC-MS/MS; M.-S.K., S.M.P., S.P., S.S.M., C.J.M., J.A. and A.K.M. processed MS data and managed data; A.K.M., S.S.M., B.G., A.H.P., Y.S., M.-S.K. performed comparison analysis with PeptideAtlas, neXtProt and GPMDB; R.I., S.Jai., G.D.B. performed interaction and complex analysis; M.-S.K., S.M.P., S.S.M., P.K., A.K.M., N.A.S., R.S.N., L.B., L.D.N.S., D.S.K., V.N., A.R., T.S., M.K., S.K.Sr., G.D., A.Mar., R.R., S.C., K.K.D., A.S., S.D.Y., S.Jay., P.R., A.H.P., B.G., J.S., N.S., R.G., G.J.S., A.A.K., S.A., D.F., T.S.K.P., H.G., A.P. performed proteogenomic analysis; A.C., H.L., R.S., J.T.S., K.K.M., S.S., A.Mah., S.K.Sh., P.S., S.D.L., C.G.D., A.Mai., M.K.H., R.H.H., C.L.K., C.A.I.-D. assisted with analysis of the data; M.-S.K., S.M.P., T.-C.H., P.L.-R. performed western blot experiments; M.-S.K., J.K.T., A.K.M., B.M., S.P., S.M.P. designed the Human Proteome Map web portal; M.-S.K., A.K.M., J.K.T. generated selected reaction monitoring (SRM) database; M.-S.K., K.M., G.D., S.M.P., S.S.M. illustrated figures with help of other authors; A.P., M.-S.K., H.G. wrote the manuscript with inputs from other authors.

Corresponding authors

Correspondence to Harsha Gowda or Akhilesh Pandey.

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Competing interests

The authors declare no competing financial interests.

Additional information

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD000561.

Extended data figures and tables

Extended Data Figure 1 Summary of proteome analysis.

a, Mass error in parts per million for precursor ions of all identified peptides. b, Number of peptides detected per gene binned as shown. c, Distribution of sequence coverage of identified proteins. d–f, %FDR with a q value of <0.01 plotted against peptide length in number of amino acids, charge state of peptide ion and number of cleavage sites missed by enzyme. P values computed from two-tailed t-test are shown. Error bars indicate s.d. calculated from FDRs of multiple fetal samples. g, h, A comparison of peptides identified in this study with PeptideAtlas and GPMDB. i, Mass error in parts per million for precursor ions identified from proteogenomics analysis.

Extended Data Figure 2 Tissue-wise gene expression and housekeeping proteins.

a, A heat map shows a partial list of not well-characterized, hypothetical genes. b, The bulk of protein mass is contributed by only a small number of genes. Only 2,350 ‘housekeeping genes’ account for ∼75% of proteome mass. c, The number of cell/tissue types where a gene was observed was counted. Some genes were found to be specifically restricted in a few samples while others were observed in the majority of samples analysed. For example, 1,537 genes were detected only in one sample, and 2,350 genes were found in all samples. These latter genes can be defined as highly abundant ‘housekeeping proteins’. d, Distribution of genes in the RefSeq database based on the number of protein isoforms resulting from their annotated transcripts (left). Distribution of the transcripts with two or more protein isoforms annotated based on the number of isoform-specific or shared peptides (right). e, A representative example of sequence coverage of PSMB8 protein along with tissue distribution of all of its identified peptides and the MS/MS spectrum of one of the peptides is shown along with seven selected reaction monitoring (SRM) transitions.

Extended Data Figure 3 Western blot analysis of select tissue-restricted proteins.

a, Eight proteins showing tissue-restricted expression were tested using western blot analysis in 17 adult tissues. GAPDH was used as a loading control. b, Four proteins found to be expressed in a broad range of tissues, although bands that do not correspond to the expected molecular weight are also observed. CST, Cell Signalling Technology; SCB, Santa Cruz Biotechnology.

Extended Data Figure 4 Identification of novel genes/ORFs and translated non-coding RNAs.

a, An example of a novel ORF in an alternate reading frame located in the 3′ UTR of CHTF8 gene. The relative abundance of peptides from the CHTF8 protein and the protein encoded by the novel ORF is shown (bottom). b, An example of translated non-coding RNA (NR_027693.1) identified by searching 3-frame-translated transcript database. The MS/MS spectrum of one of the five identified peptides (LEVASSPPVSEAVPR) is shown along with a similar fragmentation pattern observed from the corresponding synthetic peptide.

Extended Data Figure 5 Human genome annotation through proteogenomic analysis using GeneSpring.

a, Four genome search specific peptides (GSSPs; red boxes) map to an upstream ORF (denoted as black hashes) located in 5′ UTR of the SLC35A4 gene (ORF shown as blue rectangle). b, GSSP mapping in the intergenic region between two RefSeq annotated genes NDUFv3 and PKNOX1. The ORF region is depicted in dotted lines of human endogenous retroviral element (HERV). c, GSSPs mapping to an annotated pseudogene MAGEB6P1, the alignments of parent gene and pseudogene are shown below the peptides.

Extended Data Figure 6 Frequency of nucleotides surrounding translational start sites.

a, Frequency of nucleotides at positions ranging from −5 to +1 surrounding the AUG codon for confirmed translational start sites. b, Frequency of nucleotides at positions ranging from −5 to +1 surrounding the AUG codon for novel translational start sites identified in this study.

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion and additional references. (PDF 106 kb)

Supplementary Data

This file contains Supplementary Data. (PDF 3594 kb)

Supplementary Table 1

This file contains a summary of results from proteogenomics analysis; a list of peptides indicating novel signal peptide cleavage sites; and a draft map of the human proteome. (XLSX 1178 kb)

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Kim, MS., Pinto, S., Getnet, D. et al. A draft map of the human proteome. Nature 509, 575–581 (2014). https://doi.org/10.1038/nature13302

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