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How many proteins on PDB have unknown function?

How many proteins on PDB have unknown function?


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I was wondering how many of the files in the Protein Data Bank (PDB) have unknown function. The only paper I can find from an internet search is this one from 2012, which I assume might be outdated. I'd welcome suggestions of how to find this information for myself.


The article cited in the question indicates that the authors searched the PDB with the term “unknown function”. There is nothing special about this - you just type in the standard search field and hit 'Go'. I conducted a search of this type myself:

http://www.rcsb.org/pdb/results/results.do?tabtoshow=Current&qrid=91CA3A5F

Which returned 4384 structures out of approx. 149,600 in the data bank.

Of course, it is evident from the first page of the results that the number of unique proteins is smaller than this because a single study may examine different forms of the same protein.

I admit that I was surprised to find that people had spent time and money determining the structure of so many proteins of unknown function, but it appears that there is at least one blanket initiative to determine the structures of bacterial proteins of unknown functions because of their roles as potential pathogens. The idea would seem to be that they even if their mechanism of pathogenicity is unknown they could still be targeted on the basis of their structure.


Ebola Virus Proteins

At the center of the virus, a nucleocapsid composed of several types of proteins protects the genome. The ebola nucleoprotein wraps around the RNA, creating a helical complex. The interaction between nucleoprotein subunits, however, is not as rigid as in other viruses such as tobacco mosaic virus, so ebola virus often shows a wavy structure. Once inside cells, the large "L" protein, which is an RNA-dependent RNA polymerase, creates many new copies of the RNA genome.

As with the other ebola proteins, the nucleocapsid proteins contain several flexibly-connected domains, so researchers have studied them in parts. The RNA-binding portion of nucleoprotein has been studied by cryoelectron microscopy (PDB entry 5z9w), and x-ray crystallography has been used to study other parts of the protein (PDB entry 4qb0). Several other nucleocapsid proteins, which assist with formation of the structure, have also been studied (PDB entries 3vne, 3fke and 2i8b.)

Moonlighting Proteins

Exploring the Structure

Ebola Glycoprotein and Antibodies (PDB entry 3csy)

Researchers are looking hard for ways to fight infection by ebola, both with drugs and with vaccines. The glycoprotein is the major target for vaccines, since it is on the surface of the virus and is accessible to antibodies. The structure shown here, PDB entry 3csy , includes neutralizing antibodies (in red and orange) from a person who survived infection by the virus. The antibodies bind to the underside of the glycoprotein, to a portion of the protein that is not usually masked by carbohydrates and that is essential for the process of fusion. Hopefully, vaccines will be able to elicit these types of antibodies in patients, protecting them from infection. To explore this structure in more detail, click on the image for an interactive JSmol.

Topics for Further Discussion

  1. Entry 3csy is thought to be the ebola glycoprotein before it binds to a cell surface. You can look at entry 2ebo to see a portion of the glycoprotein after it fuses with the cell.

Related PDB-101 Resources

References

  1. 5z9w: Y. Sugita, H. Matsunami, Y. Kawaoka, T. Noda & M. Wolf (2018) Cryo-EM structure of the Ebola virus nucleoprotein-RNA complex at 3.6 angstrom resolution. Nature 563, 137-140.
  2. 4qb0: P. J. Dziubanska, U. Derewenda, J. F. Ellena, D. A. Engle & Z. S. Derewenda (2014) The structure of the C-terminal domain of Zaire ebolavirus nucleoprotein. Acta Crystallographica Section D 70, 2420-2429.
  3. T. F. Booth, M. J. Rabb & D. R. Beniac (2013) How do filovirus filaments bend without breaking? Trends in Microbiology 21, 583-593.
  4. 4ldb, 4ldd: Z. A. Bornholdt, T. Noda, D. M. Abelson, P. Halfmann, M. R. Wood, Y. Kawaoka & E. O. Saphire (2013) Structural rearrangement of ebola virus VP40 begets multiple functions in the virus life cycle. Cell 154, 763-774.
  5. 3csy: J. E. Lee, M. L. Fusco, A. J. Hessell, W. B. Oswald, D. R. Burton & E. O Saphire (2008) Structure of the ebola virus glycoprotein bound to an antibody from a human survivor. Nature 454, 177-182.
  6. 3vne: A. P. P. Zhang, Z. A. Bornholdt, T. Liu, D. M. Abelson, D. E. Lee, S. Li, V. L. Woods & E. O. Saphire (2012) The ebola virus interferon antagonist VP24 directly binds STAT1 and has a novel, pyramidal fold. PLoS Pathogens 8: e1002550.
  7. 3fke: D. W. Leung, N. D. Ginder, D. B. Fulton, J. Nix, C. F. Basler, R. B. Honzatko & G. K. Amarasinghe (2009) Structure of the ebola VP35 interferon inhibitory domain. Proceedings of the National Academy of Science USA 106, 411-416.
  8. 2i8b: B. Hartlieb, T. Muziol, W. Weissenhorn & S. Becker (2007) Crystal structure of the C-terminal domain of ebola virus VP30 reveals a role in transcription and nucleocapsid association. Proceedings of the National Academy of Science USA 104, 624-629.
  9. 1h2c: F. X. Gomis-Ruth, A. Dessen, J. Timmins, A. Bracher, L. Kolesnikowa, S. Becker, H. D. Klenk & W. Weissenhorn (2003) The matrix protein VP40 from ebola virus octamerizes into pore-like structures with specific RNA binding properties. Structure 11, 423-433.

October 2014, David Goodsell

About PDB-101

PDB-101 helps teachers, students, and the general public explore the 3D world of proteins and nucleic acids. Learning about their diverse shapes and functions helps to understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease to biological energy.

Why PDB-101? Researchers around the globe make these 3D structures freely available at the Protein Data Bank (PDB) archive. PDB-101 builds introductory materials to help beginners get started in the subject ("101", as in an entry level course) as well as resources for extended learning.


GFP-like Proteins

Researchers have used these fluorescent proteins in many clever ways. For example, to study protein interactions, they can split GFP into two pieces and attach one piece to each protein. Then, if the two proteins get close to one another in the cell, the GFP will assemble and light up. PDB entry 4kf5 shows an example of a split GFP, in this case, used as a method to assist crystallization of proteins for structure determination. Two beta strand segments of GFP are fused to a protein of interest (in this case, sfCherry, colored red here), and a version of GFP is created that lacks these two strands (colored green). When the two engineered proteins are mixed together and interact with one another, the two portions of GFP assemble into a functionally fluorescent protein, with the cargo of sfCherry. To explore this engineered complex in more detail, click on the image for an interactive JSmol.

Topics for Further Discussion

  1. Structures for several proteins fused with GFP are available in the PDB archive, for instance, PDB entry 4anj has GFP fused with myosin.
  2. Be sure to look around the internet for micrograph images of cells with GFP-labeled proteins--for particularly beautiful images, try searching for "brainbow" or "fluoresence micrograph cytoskeleton"

Related PDB-101 Resources

References

  1. D. M. Chudakov, M. V. Matz, S. Lukyanov & K. A. Lukyanov (2010) Fluorescent proteins and their applications in imaging living cells and tissues. Physiological Reviews 90, 1103-1163.
  2. 4kf5: H. B. Nguyen, L. W. Hung, T. O. Yeates, T. C. Terwilliger & G. S. Waldo (2013) Split green fluorescent protein as a modular binding partner for protein crystallization. Acta Crystallographica Section D 69, 2513-2523.
  3. 4ar7: D. Von Stetten, M. Noirclerc-Savoye, J. Goedhart, T. W. J. J. Gadella & A. Royant (2012) Structure of a fluorescent protein from Aequorea victoria bearing the obligate- monomer mutation A206K. Acta Crystallographical Section F 68, 878.
  4. 2y0g: A. Royant & M. Noirclerc-Savoye (2011) Stabilizing role of glutamic acid 222 in the structure of enhanced green fluorescent protein. Journal of Structural Biology 174, 385-390.
  5. 3m24: O. M. Subach, V. N. Malashkevich, W. D. Zencheck, K. S. Morozova, K. D. Piatkevich, S. C. Almo & V. V. Verkhusha (2010) Structural characterization of acylimine-containing blue and red chromophores in mTagBFP and TagRFP fluorescent proteins. Chemistry & Biology 17, 333-341
  6. 2q57: G. D. Malo, L. J. Pouwels, M. Wang, A. Weichsel, W. R. Montfort, M. A. Rizzo, D. W. Piston & R. M. Wachter (2007) X-ray structure of Cerulean GFP: a tryptophan- based chromophore useful for fluorescence lifetime imaging. Biochemistry 46, 9865- 9873.
  7. 2h5o, 2h5q: X. Shu, N. C. Shaner, C. A. Yarbrough, R. Y. Tsien & S. J. Remington (2006) Novel chromophores and buried charges control color in mFruits. Biochemistry 45, 9639-9647.
  8. 1huy: O. Griesbeck, G. S. Baird, R E., Campbell, D. A. Zacharias & R. Y. Tsien (2001) Reducing the environmental sensitivity of yellow fluorescent protein. Journal of Biological Chemistry 276, 29188-29194.
  9. 1g7k: D. Yarbrough, R. M. Wachter, K. Kallio, M. V. Matz & S. J. Remington (2001) Refines crystal structure of DsRed, a red fluorescent protein from coral, at 2.0-A resolution. PNAS USA 98, 462-467.

About PDB-101

PDB-101 helps teachers, students, and the general public explore the 3D world of proteins and nucleic acids. Learning about their diverse shapes and functions helps to understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease to biological energy.

Why PDB-101? Researchers around the globe make these 3D structures freely available at the Protein Data Bank (PDB) archive. PDB-101 builds introductory materials to help beginners get started in the subject ("101", as in an entry level course) as well as resources for extended learning.


Researchers describe protein previously unknown in biology

Ball-and-stick model of part of activated pig aconitase centered on (4Fe4S) cluster bound to cysteine-385, -448, -451, after PDB 7ACN. Credit: wikimedia commons

University of Georgia researchers have discovered a new way that iron is stored in microorganisms, a finding that provides new insights into the fundamental nature of how biological systems work. The research was recently published in the journal Nature Communications.

Iron, a metal that is required by all living organisms, is usually stored with oxygen inside a cell in a complex within a large protein known as ferritin. Researchers have now discovered a new type of protein, known as IssA, that stores iron with sulfur, instead of oxygen, in the form of an iron-sulrfur polymer known as thioferrate.

"This iron-sulfur polymer has been made previously in a test-tube but this is the first time thioferrate has been identified in a biological system," said Michael W. Adams, lead author and Distinguished Research Professor in the department of biochemistry and molecular biology. "In addition, this single type of protein, IssA, self-assembles into extremely large complexes or nanoparticles that can be more than 20-times the size of ferritin. The IssA nanoparticles are so large that they are visible inside whole cells using a microscope."

Researchers also discovered that this new protein plays a role not only in the storage of iron, but also in the assembly of proteins that contain iron-sulfur clusters.

"This work provides new insights into how microorganisms can store iron and also sulfur, and how single proteins can self-assemble into nanoparticles," said Adams. "It also gives a new perspective on how iron-sulfur clusters are synthesized in biological systems."

"Iron sulfur cluster-containing proteins are ubiquitous in biology where the clusters are used to catalyze chemical reactions or to transport electrons, for example, during respiration," he added. "In doing this research, we were interested in elucidating the function and biosynthesis of iron-sulfur clusters."

In the lab, the team grew microorganisms on a large scale, purified them and then were able to characterize a variety of iron-sulfur proteins and enzymes.

"From our genetic analyses of the organism we knew that IssA was a major protein in the cell, and during our biochemical analyses we noticed IssA due to its extremely large size. Its high abundance and large size made it quite easy to purify," he said. "With the purified protein we could apply various analytical, spectroscopic and microscopic techniques and that led us to conclude that IssA was a nanoparticle and contained thioferrate, a iron-sulfur polymer not previously seen in biology. With the pure IssA protein we could also generate antibodies, and this enabled us to visualize IssA in whole cells of the microorganism as a large complex within the cell."

While research of this nature provides fundamental knowledge about how biological systems work, the research could one day be used to engineer nanoparticles for medical or other applications.

"Nanoparticles are used in many medical and electronic applications, although they are typically made of inorganic components," he said. "Engineering protein nanoparticles might be possible if we could understand the properties of IssA that enable it to assemble into nanoparticle-like structures. It is also possible that nanoparticles built on the IssA protein but containing other inorganic materials could have applications."


PDB50

PDB50 will mark an important milestone in the history of structural biology. In 1971, the structural biology community established the single worldwide archive for macromolecular structure data &mdash the Protein Data Bank (PDB). From its inception, the PDB has embraced a culture of open access, leading to its widespread use by the research community. PDB data are used by hundreds of data resources and millions of users exploring fundamental biology, energy and biomedicine.

Structural biology and structural bioinformatics have had an enormous impact on our understanding of the mechanism and function of biological macromolecules. The PDB acts as a custodian for all these data, representing a repository of the vast majority of the achievements and milestones of the structural biology community. The archive is managed by the Worldwide Protein Data Bank consortium (wwPDB) of partner sites in Asia, Europe and America.

This celebration of the 50th anniversary of the founding of the Protein Data Bank as the first open access digital data resource in biology will include presentations from speakers from around the world who have made tremendous advances in structural biology and bioinformatics. Students and postdoctoral fellows are especially encouraged to attend and will be eligible for poster prizes.

Early and late stage career scientists are encouraged to submit an abstract for poster presentation during the symposium.

The online sessions will take place between 11 a.m. &ndash 4:30 p.m. EDT each day.

The event will be recorded and made available to registered participants after the meeting.

Important dates

Speakers

Eddy Arnold

  • Rutgers, The State University of New Jersey
  • Using HIV-1 reverse transcriptase structures to guide anti-AIDS drug discovery

Helen M. Berman

  • Rutgers, The State University of New Jersey
  • University of Southern California
  • The evolution of the Protein Data Bank as a community resource

Thomas L. Blundell

  • University of Cambridge
  • A personal history of five decades of structural biology and the PDB: From the X-ray structure of 2-Zinc insulin hexamer in 1970 to Cryo-EM structures of DNA-PK from DNA repair in 2020

Alexandre M. J. J. Bonvin

  • Utrecht University
  • Solving 3D puzzles by integrative modelling using PDB structures

Stephen K. Burley

  • Rutgers, The State University of New Jersey
  • University of California, San Diego
  • Impact of structural biologists and fifty years of Protein Data Bank operations on drug discovery and development

Wah Chiu

Johann Deisenhofer

  • University of Texas Southwestern Medical Center
  • 50 years of PDB &mdash from crazy idea to treasure

Juli Feigon

  • University of California, Los Angeles
  • Structural biology of telomerase

Angela M. Gronenborn

  • University of Pittsburgh
  • Integrated BioNMR &mdash getting by with a little help from my friends

Jennifer L. Martin

  • University of Wollongong
  • Science, crystallography, reflections: A journey with the PDB over 35 years

Stephen L. Mayo

  • California Institute of Technology
  • Antibody small molecule conjugates with computationally designed target binding synergy

Zihe Rao

  • ShanghaiTech University
  • Tsinghua University
  • Structural insight into SARS-CoV-2 replication and transcription complex (RTC)

Hao Wu

  • Harvard Medical School
  • Boston Children's Hospital
  • "Speck"tacular inflammasomes: structures of supramolecular complexes in innate immunity

Organizers

  • Celia Schiffer, University of Massachusetts Medical School
  • Helen M. Berman, Rutgers, The State University of New Jersey RCSB PDB
  • Stephen K. Burley, Rutgers, The State University of New Jersey RCSB PDB
  • Jeffrey C. Hoch, University of Connecticut BMRB
  • Gerard J. Kleywegt, European Bioinformatics Institute PDBe
  • Genji Kurisu, Osaka University PDBj
  • John L. Markley, University of Wisconsin&ndashMadison BMRB
  • Sameer Velankar, European Bioinformatics Institute PDBe
  • Christine Zardecki, Rutgers, The State University of New Jersey RCSB PDB

Acknowledgement: Illustration by David S. Goodsell, The Scripps Research Institute. doi: 10.2210/rcsb_pdb/goodsell-gallery-003

This illustration shows a cross-section through the blood, with blood serum in the upper half and a red blood cell in the lower half. In the serum, look for Y-shaped antibodies, long thin fibrinogen molecules (in light red) and many small albumin proteins. The large UFO-shaped objects are low density lipoprotein and the six-armed protein is complement C1. The red blood cell is filled with hemoglobin, in red. The cell membrane, in purple, is braced on the inner surface by long spectrin chains connected at one end to a small segment of actin filament.

Journals


How many proteins on PDB have unknown function? - Biology

The primary types and functions of proteins are listed in Table 1.

Table 1. Protein Types and Functions
Type Examples Functions
Digestive Enzymes Amylase, lipase, pepsin, trypsin Help in digestion of food by catabolizing nutrients into monomeric units
Transport Hemoglobin, albumin Carry substances in the blood or lymph throughout the body
Structural Actin, tubulin, keratin Construct different structures, like the cytoskeleton
Hormones Insulin, thyroxine Coordinate the activity of different body systems
Defense Immunoglobulins Protect the body from foreign pathogens
Contractile Actin, myosin Effect muscle contraction
Storage Legume storage proteins, egg white (albumin) Provide nourishment in early development of the embryo and the seedling

Two special and common types of proteins are enzymes and hormones. Enzymes, which are produced by living cells, are catalysts in biochemical reactions (like digestion) and are usually complex or conjugated proteins. Each enzyme is specific for the substrate (a reactant that binds to an enzyme) it acts on. The enzyme may help in breakdown, rearrangement, or synthesis reactions. Enzymes that break down their substrates are called catabolic enzymes, enzymes that build more complex molecules from their substrates are called anabolic enzymes, and enzymes that affect the rate of reaction are called catalytic enzymes. It should be noted that all enzymes increase the rate of reaction and, therefore, are considered to be organic catalysts. An example of an enzyme is salivary amylase, which hydrolyzes its substrate amylose, a component of starch.

Hormones are chemical-signaling molecules, usually small proteins or steroids, secreted by endocrine cells that act to control or regulate specific physiological processes, including growth, development, metabolism, and reproduction. For example, insulin is a protein hormone that helps to regulate the blood glucose level.

Proteins have different shapes and molecular weights some proteins are globular in shape whereas others are fibrous in nature. For example, hemoglobin is a globular protein, but collagen, found in our skin, is a fibrous protein. Protein shape is critical to its function, and this shape is maintained by many different types of chemical bonds. Changes in temperature, pH, and exposure to chemicals may lead to permanent changes in the shape of the protein, leading to loss of function, known as denaturation. Different arrangements of the same 20 types of amino acids comprise all proteins. Two rare new amino acids were discovered recently (selenocystein and pirrolysine), and additional new discoveries may be added to the list.

In Summary: Function of Proteins

Proteins are a class of macromolecules that perform a diverse range of functions for the cell. They help in metabolism by providing structural support and by acting as enzymes, carriers, or hormones. The building blocks of proteins (monomers) are amino acids. Each amino acid has a central carbon that is linked to an amino group, a carboxyl group, a hydrogen atom, and an R group or side chain. There are 20 commonly occurring amino acids, each of which differs in the R group. Each amino acid is linked to its neighbors by a peptide bond. A long chain of amino acids is known as a polypeptide.

Proteins are organized at four levels: primary, secondary, tertiary, and (optional) quaternary. The primary structure is the unique sequence of amino acids. The local folding of the polypeptide to form structures such as the α helix and β-pleated sheet constitutes the secondary structure. The overall three-dimensional structure is the tertiary structure. When two or more polypeptides combine to form the complete protein structure, the configuration is known as the quaternary structure of a protein. Protein shape and function are intricately linked any change in shape caused by changes in temperature or pH may lead to protein denaturation and a loss in function.


Author Summary

Genome sequencing has led to the discovery of many new gene products, proteins. These discoveries hold tremendous potential for totally new approaches to the diagnosis and treatment of disease. To realize this potential, one important step is to understand the function of the thousands of proteins whose function is currently unknown. One of these proteins of unknown function is human DJ-1, a protein that appears to play a protective role against Parkinson and other neurodegenerative diseases. Here we present a computational approach to the classification by function of DJ-1 and its family members. Eight DJ-1 family members, all with similar 3-D structure, are analyzed. Three different probable functional classes emerge from this analysis on six of the family members, all with a simple calculation.

Citation: Wei Y, Ringe D, Wilson MA, Ondrechen MJ (2007) Identification of Functional Subclasses in the DJ-1 Superfamily Proteins. PLoS Comput Biol 3(1): e15. https://doi.org/10.1371/journal.pcbi.0030010

Editor: Luhua Lai, Peking University, China

Received: September 11, 2006 Accepted: December 7, 2006 Published: January 26, 2007

Copyright: © 2007 Wei at al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The support of US National Science Foundation grant MCB-0517292 is gratefully acknowledged.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: PDB, Protein Data Bank THEMATICS, theoretical microscopic titration curves


Sequence-based classification of proteins

The first hurdle for any functional annotation process is to define 'function'. If the protein is an enzyme, then simply using the EC numbering scheme (see Box 1) can be useful. In general however, the problem is multi-dimensional: a protein can have a molecular function, a cellular role, and be part of a functional complex or pathway (these are the distinctions used in the Gene Ontology (GO see Box 1) [6]). Furthermore, certain aspects of molecular function can be illustrated by multiple descriptive levels (for example, the coarse 'enzyme' category versus a more specific 'protease' assignment). Even the more detailed definition would not reveal the cellular role of the protein (apoptosis, metabolism, blood coagulation, and so on).

Most function-prediction methods, both sequence and structure based, rely on inferring relationships between proteins that permit the transfer of functional annotations and binding specificities from one to the other. A notable challenge here is deciphering the connection between the detected similarities (structural or in sequence) and the actual level of functional relatedness. Function is often associated with domains, and another problem is the identification of functional domains from sequence alone. The accuracy of current methods for predicting domain boundaries is not yet completely satisfactory. Several methods provide reliable predictions if a structural template for the protein is available, but when this is not the case, one is left with the problem of whether the experimental annotation used for the inference refers to the same domain for which the sequence similarity/motif is established [7].

The function of a protein can also be inferred from its evolutionary relationship with proteins of known function, provided that the relationship is properly inspected. Orthologous proteins in different species most often share function, but paralogy (that is, divergence following duplication of the original gene) does not guarantee common function. Distinguishing between orthology and paralogy can be attempted on the basis of observed sequence-similarity patterns, by analyzing the specific conservation pattern of residues responsible for function in the family, or on the basis of the protein structure (either experimentally determined or modeled). In all cases, this requires the clustering of proteins into evolutionary families, which can be achieved using similarity-detection tools such as BLAST [8] or profiling tools based on multiple sequence alignments, for example, PSI-BLAST [9]. Several available resources provide pre-compiled family assignments for proteins on a genomic scale, based only on their sequence. Resources can be subdivided into those that consider full-length sequences and those based on domains or motifs that map to certain sub-sequences. In both cases, the degree of granularity of the classification is important, as this is related to the level of functional features that a group of proteins is expected to share.

A resource that classifies full-length proteins is PIRSF [10], in which a set of rules is applied to define primary and curated clusters that are also based on textual (protein names, literature) and parent-child relationships. These clusters (named superfamilies) are further divided into those with full-length similarity (that is, common domain architecture) and those sharing an ancestral domain. PIRSF covers more than two-thirds of the protein sequence space.

Studying proteins at a domain level allows more accurate functional inference [11] and is useful for predicting the function of novel domain combinations that possibly give rise to new protein functions [12]. In this type of resource, a family of domains is represented as a multiple sequence alignment, which is embodied in a statistical family signature profile (for example, CDD [13] and PROSITE [14]) or a profile-hidden Markov model (for example, Pfam [15] and SMART [16]), collectively referred to here as profiles. Pfam, a prototype for such collections, currently contains more than 9,000 family profiles and covers roughly 70-74% of UniProt sequences, capturing about half of their amino acids [17]. About 40-45% of Pfam families are associated with known structures, whereas 20-25% are currently uncharacterized. Other resources, for example CDD, use externally defined profiles to provide rapid assignments to sequence queries, using a BLAST-like engine to speed up searches.

Profile-based methods and resources differ significantly in their level of automation, their degree of manual curation, and the level of independence from complementary resources used in the classification. Combination of these resources provides a more comprehensive coverage, as reflected by InterPro [18], a repository of protein families integrating signatures from more than 10 member resources, currently covering nearly 75% of UniProt sequences. InterPro also includes Gene3d [19] and SUPERFAMILY [20], which provide sequence profiles corresponding to the structural classification of folds by CATH [21] and SCOP [22], respectively. A resource exploiting the multiplicity of essentially complete genome sequences is COG (Clusters of Orthologous Groups), an evolutionary classification that uses comparative genomics principles, such as phyletic profiles [23] (see Box 1), to identify the presence of orthologs, and group them accordingly.

A notable shortcoming of the methods described above is that they require definition of a threshold similarity for separating families from each other. An alternative approach to defining clusters is the construction of a tree representation that can provide a hierarchical view. Resources in this category include ProtoNet [24], CluSTr [25] and SYSTERS [26]. They are based on sequence similarities detected by an all-against-all sequence comparison, so that any level of evolutionary granularity can be inspected, from closely related subfamilies to more distant relationships.

Approaches that do not rely solely on supervised annotation of family profiles include ProDom [27], which collects putative domain profiles using known sequence domains as query sequences for iterative PSI-BLAST searches [9]. EVEREST [28] is a fully automatic unsupervised method that identifies recurrent conserved regions on the basis of local sequence similarities and iterative profile searches.

The accuracy of sequence-based methods is affected by the type and amount of information on the specific protein family but, overall, they seem to be reasonably accurate. Their success rate has been shown to be greater than 70% when tested on a limited dataset (all structures solved by the Midwest Center for Structural Genomics during the first five years of the Protein Structure Initiative) [29].


References

Kim, S. C. et al. Substrate and functional diversity of lysine acetylation revealed by a proteomics survey. Mol. Cell 23, 607–618 (2006). The first proteomic study to report the widespread existence of acetylation in human (HeLa) and mouse cells.

Choudhary, C. et al. Lysine acetylation targets protein complexes and co-regulates major cellular functions. Science 325, 834–840 (2009). A large-scale, high-resolution proteomic screen that identified over 3,500 acetylated Lys sites in human cells.

Choudhary, C., Weinert, B. T., Nishida, Y., Verdin, E. & Mann, M. The growing landscape of lysine acetylation links metabolism and cell signalling. Nat. Rev. Mol. Cell Biol. 15, 536–550 (2014).

Verdin, E. & Ott, M. 50 years of protein acetylation: from gene regulation to epigenetics, metabolism and beyond. Nat. Rev. Mol. Cell Biol. 16, 258–264 (2015).

Bannister, A. J. & Kouzarides, T. Regulation of chromatin by histone modifications. Cell Res. 21, 381–395 (2011).

Glozak, M. A. & Seto, E. Histone deacetylases and cancer. Oncogene 26, 5420–5432 (2007).

Zhao, D., Li, F. L., Cheng, Z. L. & Lei, Q. Y. Impact of acetylation on tumor metabolism. Mol. Cell. Oncol. 1, e963452 (2014).

Falkenberg, K. J. & Johnstone, R. W. Histone deacetylases and their inhibitors in cancer, neurological diseases and immune disorders. Nat. Rev. Drug Discov. 13, 673–691 (2014).

Haynes, S. R. et al. The bromodomain: a conserved sequence found in human, Drosophila and yeast proteins. Nucleic Acids Res. 20, 2603 (1992). The first report of the BRD motif, which speculates that it constitutes a protein–protein interaction domain.

Li, Y. et al. AF9 YEATS domain links histone acetylation to DOT1L-mediated H3K79 methylation. Cell 159, 558–571 (2014).

Li, Y. et al. Molecular coupling of histone crotonylation and active transcription by AF9 YEATS domain. Mol. Cell 62, 181–193 (2016).

Andrews, F. H. et al. The Taf14 YEATS domain is a reader of histone crotonylation. Nat. Chem. Biol. 12, 396–398 (2016).

Kim, M. S. et al. A draft map of the human proteome. Nature 509, 575–581 (2014).

Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–584 (2014).

Muller, S., Filippakopoulos, P. & Knapp, S. Bromodomains as therapeutic targets. Expert Rev. Mol. Med. 13, e29 (2011).

Belkina, A. C. & Denis, G. V. BET domain co-regulators in obesity, inflammation and cancer. Nat. Rev. Cancer 12, 465–477 (2012).

Shi, J. & Vakoc, C. R. The mechanisms behind the therapeutic activity of BET bromodomain inhibition. Mol. Cell 54, 728–736 (2014).

Wang, C. Y. & Filippakopoulos, P. Beating the odds: BETs in disease. Trends Biochem. Sci. 40, 468–479 (2015).

Filippakopoulos, P. & Knapp, S. Targeting bromodomains: epigenetic readers of lysine acetylation. Nat. Rev. Drug Discov. 13, 337–356 (2014).

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Using protein-protein interactions to annotate protein function

In addition to identifying disease-associated mutations, PrePPI can also be used to gain a better understanding of the cellular processes in which a gene or protein is involved. Similarly to how the Honig Lab took the BLAST concept and applied it to protein structure, they have also begun using PrePPI within a gene set enrichment analysis (GSEA) framework.

Using PrePPI for gene set enrichment analysis. To infer the function of a particular protein, Q, the Honig Lab places all proteins in the human proteome, li, in a list and sorts them according to the interaction likelihood ratio between li and Q. They then search for gene sets associated with a given Gene Ontology annotation that is enriched among the high-scoring interactors of Q. In the example, Gene Set 1 would be enriched whereas Gene Sets 2 and 3 would not be, since the proteins in those sets are either evenly distributed throughout the ranked list or clustered with proteins that are unlikely to interact with Q. The paper reports on top ranked gene sets found for BRCA1 and PEX2. (Image courtesy of eLife.)

For each protein, they queried PrePPI to construct a list of the proteins whose scores make them most likely to interact with it, and then, using GSEA, looked for the GO terms associated with each. They found that the top-ranked gene sets that PrePPI predicted accurately reflected their function, as documented in a resource called the Molecular Signatures Database (mSigDB). Moreover, through the automatic computational method made possible by PrePPI, they predicted the functions of approximately 2,000 additional proteins whose functions were previously unknown.

Honig cautions that the interactions and functions predicted using PrePPI should not necessarily be assumed as fact. Nevertheless, his lab’s tests so far indicate that they are largely reliable. “PrePPI is based on statistical analysis and not experiment, which is really the gold standard,” he explains. “What we’re ultimately trying to do with these methods is to generate hypotheses that can be cross-referenced with other computational and experimental methods. We’re excited because the number of interactions that PrePPI finds is unprecedented in scope, and so our hope is that it will help systems biologists and other biomedical researchers who do not typically look at structure to be able to incorporate information about this essential layer of activity into their investigations."

Related publications

Garzón JI, Deng L, Murray D, Shapira S, Petrey D, Honig B. A computational interactome and functional annotation for the human proteome. Elife. 2016 Oct 225. pii: e18715.

Westphalen CB, Takemoto Y, Tanaka T, Macchini M, Jiang Z, Renz BW, Chen X, Ormanns S, Nagar K, Tailor Y, May R, Cho Y, Asfaha S, Worthley DL, Hayakawa Y, Urbanska AM, Quante M, Reichert M, Broyde J, Subramaniam PS, Remotti H, Su GH, Rustgi AK, Friedman RA, Honig B, Califano A, Houchen CW, Olive KP, Wang TC. Dclk1 defines quiescent pancreatic progenitors that promote injury-induced regeneration and tumorigenesis. Cell Stem Cell. 2016 Apr 718(4):441-55.

Chen TS, Petrey D, Garzon JI, Honig B. Predicting peptide-mediated interactions on a genome-wide scale. PLoS Comput Biol. 2015 May 411(5):e1004248.

Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, Maniatis T, Califano A, Honig B. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature. 2012 Oct 25490(7421):556-60.


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