=Paper=
{{Paper
|id=Vol-3726/paper4
|storemode=property
|title=Modelling the EGFR through the Protein Conformation Ontology
|pdfUrl=https://ceur-ws.org/Vol-3726/paper4.pdf
|volume=Vol-3726
|authors=Giacomo De Colle,Morgan Mitchell,Alexander D. Diehl
|dblpUrl=https://dblp.org/rec/conf/sewebmeda/ColleMD24
}}
==Modelling the EGFR through the Protein Conformation Ontology==
https://ceur-ws.org/Vol-3726/paper4.pdf
Modelling the EGFR through the Protein Conformation
Ontology⋆
Giacomo De Colle∗ 1,3, Morgan Mitchell2, Alexander D. Diehl2,3
1 Department of Philosophy, University at Buffalo, Buffalo (NY), US
2 Department of Biomedical Informatics, University at Buffalo, Buffalo (NY), US
3 National Center for Ontological Research
Abstract
The study of the spatial configuration of the structures of a protein, also known as protein
conformation, is pivotal to the understanding of the functioning and activation patterns of
proteins, as well as their relations with the surrounding environment. In order to facilitate data-
driven research on protein conformations, we present the Protein Conformation Ontology, an
ongoing project which provides a structured vocabulary of terms used to represent protein
conformations and related conformational changes at different levels of granularity. To the aim
of testing the capabilities of the ontology, we adopted as a test case the conformational changes
related to the activation of the epidermal growth factor receptor. In this paper, we discuss our
initial results in modelling two different models of the epidermal growth factor receptor.
Keywords
Work-in-progress, Protein Conformation Ontology (PRC), biomedical ontologies, protein
conformations, Basic Formal Ontology (BFO))1
1. Introduction
The spatial configuration of a protein is a key element to our understanding of its
functioning. The same protein, depending on the different environmental conditions it is
situated in, can fold into many different types of structures, also known as protein
conformations [1]. Differences in folding patterns and conformational changes of a protein
can bring it to be involved in radically different types of processes. Protein conformations
can be classified at different levels of granularity called the secondary, tertiary, and
quaternary levels. At the secondary level, protein structures identify hydrogen bonding
between atoms in the same polypeptide backbone. Tertiary structures are more complex
SeWebMeDA-2024: 7th International Workshop on Semantic Web Solutions for Large-scale Biomedical Data
Analytics, May 26, 2024, Hersonissos, Greece
∗ Corresponding author.
gdecolle@buffalo.edu (G. De Colle); addiehl@buffalo.edu (A. D. Diehl); mm528@buffalo.edu (M. Mitchell)
0000-0002-3600-6506 (G. De Colle); 0000-0001-9990-8331 (A. D. Diehl); 0000-0002-9318-128X (M.
Mitchell)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
and are identified with the way in which one polypeptide chain disposes itself in three-
dimensional space when side chains interact with one another. Quaternary structures are
created when two or more polypeptide chains connect [2].
Ontologies are controlled vocabularies developed for allowing consistent semantic
interoperability of large and fragmented datasets [3]. This paper presents developments in
creating the Protein Conformation Ontology (PRC), the first attempt at building an ontology
that is able to represent all types of protein conformations, their changes over time and their
relation to protein functioning. The PRC can be used to build representations of secondary,
tertiary, and quaternary structures, thus enabling querying and comparison of protein
databases on the basis of protein conformation. We present the current state of
development of the PRC and focuses on modelling the epidermal growth factor receptor
(EGFR) as a use case for the ontology.
A survey of BioPortal [4] and of the ontologies included in the OBO Foundry [5] reveal
that existing ontologies representing protein structures or conformations include the
Protein Ontology (PRO), the Sequence Ontology (SO), the Semanticscience Integrated
Ontology (SIO) and the Physico-Chemical Methods and Properties ontology (FIX).
Nevertheless, all these efforts only represent certain aspects of protein structure and cannot
be used as comprehensive tools to model conformational changes. PRO, for example, cannot
be used to represent secondary and tertiary protein structure, although it does represent
proteoforms, i.e. protein isoforms and post-translational modifications that represent
variants in the structure of a given protein [6].
SO on the other hand represents protein structure at the secondary structure level, but
cannot be used to model protein conformations at an higher level of complexity. Moreover,
the scope of SO is limited to continuous sequences of amino acids, thus effectively
disregarding representation of secondary structures that include discontinuous regions [7].
Some common protein secondary conformations are also included in SIO and FIX, but both
ontologies have a relatively narrow scope and lack many terms, especially in the realm of
tertiary and quaternary protein conformation [8].
The scope of the ontologies described above is then too narrow to fully represent the full
variety of protein conformations, as well as changes in protein conformations, and warrants
the creation of an ontology devoted to this particular domain. In this paper we present first
results in developing the PRC, an ontology which intends to address this issue. The aim of
the PRC is not only to represent protein conformations as physical structure or as
structured material entities. Rather, the PRC represents conformational changes, the way in
which these conformational changes take place, the way in which they are triggered and the
way in which they are ordered in time. For this reason, the PRC identifies protein
conformations as dispositions to adopt a certain structure. Such dispositions are realized in
a process of conformational change, where the protein adopts the corresponding to the
protein conformation.
2. Methods
The development of the PRC originated from the need to represent the conformational
changes involved in the formation of protein aggregates in neurological diseases, such as
the formation of prions associated with Creutzfeldt-Jakob disease [9]. PRC was built using
the Stanford Center for Biomedical Informatics Research tool, Protégé (version 5.6.1) [10].
The HermiT 1.4.3.456 [11] reasoner was used to check the ontology for logical consistency.
The PRC adopts the Basic Formal Ontology (BFO) as a top-level architecture. Moreover, the
PRC imports terms from the Gene Ontology (GO), the Protein Ontology (PRO) and the
Relation Ontology (RO) [3 6 12 13]. SO served as a valuable source of information about
protein secondary structures, but SO in ambiguous as to whether its classes represent
information content entities or material entities, and as such is not compliant with the BFO.
Many PRC terms are based on SO terms, but have definitions rewritten to emphasize that
the conformations represented are types of dispositions. Reference to the original terms in
SO was given by using the annotation properties ‘definition source’ from the Information
Artifact Ontology (IAO) and skos:closematch. It should be noted that PRC includes many
more secondary structure classes than SO because of our more thorough curation and
representation of secondary structures formed from discontinuous sequence regions, and
we will propose matching classes in SO for those that are conformant with SO’s
representation approach (which allows for only continuous sequences of amino acids).
PRC is developed with the aim of supporting and complying with FAIR data standards
practices [14]. When possible, resources imported by other ontologies have been used as
described in the paragraph above in order to allow for reuse of data, and they have been
referenced by providing metadata by using, for example, skos:closematch. The aim of the
project is to develop the ontology in compliance with OBO Foundry principles [15], and to
submit a future version of the ontology for acceptance to the OBO community, thus allowing
for better findability of the ontology. At the moment, the latest version of the PRC ontology
can be found at the following GitHub page:
https://github.com/Buffalo-Ontology-Group/Protein-Conformation-Ontology
The development process of the PRC began with the identification of a class of entities of
interest for research in the biomedical domain, i.e. protein conformations, and the
consideration of use cases where the ontological representation of these entities would
have been useful. BFO was adopted as a top-level ontology from the beginning of the
development process, and the PRC team decided to use the BFO class “disposition” to
represent how conformations are created by processes which change the material structure
of the proteins they inhere in. In this way, protein conformations can be natively connected
to the functioning of proteins which they are responsible for. During the development
process, we created models of use cases selected from scientific literature, which we then
evaluated and used as a guide to introduce or modify the initial top-level structure we
developed out of BFO. This paper focuses on one of such use-cases. A more complete
presentation of the development of the PRC will be submitted elsewhere and include other
use cases that we have explored, such as representing the conformational changes that the
voltage-gated sodium channel [16] is involved in and the conformational changes involved
in the formation of protein aggregates implicated causally in various neurological diseases.
3. Results
The PRC currently contains 206 PRC-prefixed classes, which include terms representing
secondary, tertiary, and quaternary conformation dispositions, as well as terms
representing conformational changes, structural qualities, and material entities such as
protein complexes and amino acid chains. Currently, the PRC includes many terms
representing secondary structures, such as ‘beta helix’, that refer to conformations
commonly found in many different protein complexes. Tertiary and quaternary structures,
on the other hand, are very specific to particular protein complexes, and cannot all be
represented in a single ontology. To test the capabilities of the ontology to represent tertiary
and quaternary structures, we chose to represent certain specific protein complexes and
their conformational changes over time and in different conditions.
The use-case we present in this paper as a test case for the PRC is the epidermal growth
factor receptor (EGFR). The EGFR is a transmembrane protein responsible for regulation of
a series of events such as coordination of cell growth, differentiation, and migration and
which serves a role in epithelial development [17]. Other members of the EGFR protein
superfamily, which share structural similarities with the EGFR, have roles in other parts of
the body, such as cardiac development [17 18]. Errors in the activation of the EGFR are
closely related to the formation of tumors [19]. Representing EGFR is a formidable test case
for the PRC, given that the EGFR is involved in a very complex series of conformational
changes including dimerization. Building an ontological model of the EGFR is then an
excellent test case for any ontology that aims at representing conformations and
conformational changes over time.
The EGFR is a tyrosine kinase receptor (RTK) which activates a process of
phosphorylation that regulates cell production. The mechanism through which this process
is activated is of particular interest for a structural study of proteins. The EGFR is activated
through binding with one of its ligands, usually the epidermal growth factor (EGF), and
through a complex process of conformational changes, that in some cases involve ligand-
induced dimerization with another EGFR monomer [17 18 19].
Two models based on a variety of experimental data and simulations have been
developed to represent the mechanism through which the EGFR activates. In one of the
models, called “ligand-induced dimerization model”, two EGFR subunits dimerize after
binding to EGF. In the other model, called “rotation model”, EGFR exists as a preformed
dimer, and is merely activated after ligand binding occurs. Despite the rotation model
having received stronger experimental confirmation in recent years, consensus has not
been reached on whether the model should entirely replace the ligand-induced one [20 21].
We thus decided to include the ligand-induced and rotation models in our representation,
not only because scientific consensus has not yet been reached, but also because both
models may reflect reality, in that some EGFR subunits may be pre-dimerized and some not
[22].
The EGFR comprises an external region that is divided into four domains, includes a
transmembrane domain and a juxtamembrane domain, that connects the external domains
to the tyrosine kinase domain (TKD) to a disordered carboxyl tail [18]. Each of these parts
is described by a ‘material entity’ class in the PRC, along with the secondary structures it is
composed of.
The process through which the EGFR activates its TKD, according to the ligand-induced
dimerization model, is the following: first of all, one EGF monomer binds to an EGFR
monomer, between the external domains 1 and 3 [17]. This causes domain 3 to rotate of
roughly 120 degrees closer to domain 1, and the whole external part of the EGFR to undergo
a radical conformational change. A beta hairpin called “dimerization arm”, part of external
domain 2, is now pointing outwards and can be used to bind to another EGFR that has also
undergone the same sequence of changes. The two dimerization arms must pass close to
each other and bind reciprocally to a site on the external domain 2 of the opposite EGFR
monomer, resulting in the formation of the external EGFR dimer [17].
Figure 1: intended design pattern for PRC classes. Blue represents processes, green
represents objects, orange represents dispositions. Processes realize dispositions, which
inhere in objects.
As a consequence, other parts of the two EGFR monomers also bind. In particular, the
intracellular TKD subunits dimerize and create an asymmetric TKD dimer. This dimer is
formed through the rotation of an alpha helix in one TKD, called the alpha C helix, that
interacts with an active site in the opposite TKD. As a result, the two TKDs, one assuming
the role of an activator and the other the role of a receiver, will trans-phosphorylate each
other.
These two processes of dimerization have been the main focus of our ontological
representation. They represent a complex series of conformational changes that two
different protein monomers undergo together in order to build a common quaternary
structure. All of the entities described above have been represented in the ontology in the
form of material entities for the parts of the EGFR, dispositions for the conformations of the
EGFR, roles and processes of conformational changes. Processes in particular have been
also axiomatized in order to automate reasoning, especially regarding their temporal
ordering.
For example, the class “epidermal growth factor receptor ligand binding” has been made
equivalent to
“'protein binding'
and (realizes some
'epidermal growth factor receptor with bound ligand conformation')
and (precedes some
'epidermal growth factor receptor activation')”.
When needed, new ad-hoc classes were created to reflect this different process ordering,
as well as corresponding new disposition classes and relative axioms. When possible,
classes from the ligand-induced dimerization model were reused, for example to represent
the material entities involved in the constitution of the EGFR. Similarly, two different classes
are introduced to represent the ligand-induced and rotation-induced processes of
activation, and each have different processes as parts.
Figure 2: Process classes used for representing the two models of the EGFR in the
PRC and their relationship with GO terms.
4. Discussion
The EGFR test case was successfully completed. The complex conformational changes
that the protein complex is involved in are all represented in the PRC and properly
axiomatized. Moreover, two different models for the EGFR functioning were also
successfully represented and can now be coherently compared and used for querying
purposes. Many subclasses of tertiary and quaternary structure were created to represent
the structure of the EGFR protein complex: its disposition to connect with an EGFR ligand,
its disposition to form a dimer with another EGFR monomer, its disposition to activate by
rotating its dimerization arm, and so on. Further work in representing the EGFR will include
linking the conformational changes in the ontology with other biological processes such as
phosphorylation and extending our ontological representation of the normal forms of EGFR
to include how pathological mutations alter its conformation and activation. Changes in the
conformational activations of the EGFR might be related with its mis-activation in cases
where the EGFR is involved with the formation of cancer [18]. Representing this process
ontologically would provide an extremely helpful application of the PRC.
The PRC is aimed at representing protein conformations, how they are created as a result
of various biological processes and how they inhere in different material entities. Allowing
for querying of information about protein structures can also be beneficial a series of data-
driven areas of research. For example, systems biology investigates biological functions
based on the interactions between different parts of the whole organism. The ELIXIR
platform has recently become a focal point for data-driven investigation in the field of
systems biology [22]. Conformational changes depend on features of the biological
environment that their bearers are situated in, and this is interestingly also the case for the
EGFR [23]. Moreover, being able to differentiate between the two possible EGFR models on
the basis of surrounding environmental features would reveal an extremely useful use case
for an EGFR ontological representation.
5. Conclusion
The PRC is an ontology which adopts BFO as a top-level architecture and which aims at
representing protein conformation, the processes they are involved in, and the material
entities they depend on. The current development of the PRC includes 206 classes which
represent secondary, tertiary, and quaternary structures. To test the capabilities of the PRC
in representing protein conformations, we have modeled the EGFR, its dimerization
process, and its activation, includes representing the two competing models of the EGFR in
a coherent way. Being able to represent the complex series of conformational changes that
interest the EGFR at different levels of granularity, the PRC proves to be the first successful
ontology for modeling protein conformations and their changes.
Acknowledgements
The authors wish to acknowledge the helpful discussions with Lauren Wishnie, Darren
Natale, and William Duncan.
References
[1] Anfinsen CB. The formation and stabilization of protein structure. Biochemical Journal
128(4) (1972) 737-49. doi: 10.1042/bj1280737.
[2] Rehman I, Farooq M, Botelho S. "Biochemistry, Secondary Protein Structure".
StatPearls. Treasure Island (FL), 2022.
[3] Arp R, Smith B, Spear AD. "Building Ontologies with Basic Formal Ontology", The MIT
Press, 2015.
[4] Whetzel PL, Noy NF, Shah NH, et al. "BioPortal: enhanced functionality via new Web
services from the National Center for Biomedical Ontology to access and use ontologies
in software applications". Nucleic Acids Res 39 (2011).
[5] Jackson R, Matentzoglu N, Overton JA, et al. "OBO Foundry in 2021: operationalizing
open data principles to evaluate ontologies". Database (Oxford) (2021). doi:
10.1093/database/baab069.
[6] Natale DA, Arighi CN, Blake JA, et al. "Protein Ontology (PRO): enhancing and scaling up
the representation of protein entities". Nucleic Acids Res 45 (2017). doi:
10.1093/nar/gkw1075.
[7] Eilbeck K, Lewis SE, Mungall CJ, et al. "The Sequence Ontology: a tool for the unification
of genome annotations. Genome Biology" 6(5):R44 (2005). doi: 10.1186/gb-2005-6-5-
r44.
[8] Dumontier M, Baker CJ, Baran J, et al. "The Semanticscience Integrated Ontology (SIO)
for biomedical research and knowledge discovery". J Biomed Semantics 5(1) (2014).
doi: 10.1186/2041-1480-5-14.
[9] Liu, F., Lü, W., & Liu, L. (2024). “New implications for prion diseases therapy and
prophylaxis”. Frontiers in molecular neuroscience, 17, 1324702.
https://doi.org/10.3389/fnmol.2024.1324702
[10] Noy NF, Crubezy M, Fergerson RW, et al. "Protégé-2000: an open-source ontology-
development and knowledge-acquisition environment". AMIA Annu Symp Proc (2003).
[11] Glimm B, Horrocks I, Motik B, Stoilos G, Wang Z. "HermiT: An OWL 2 Reasoner". Journal
of Automated Reasoning 53(3) (2014) 245-69. doi: 10.1007/s10817-014-9305-1.
[12] Consortium TGO. "The Gene Ontology Resource: 20 years and still GOing strong".
Nucleic Acids Res 47(D1):D330-d38 (2014). doi: 10.1093/nar/gky1055.
[13] Smith B, Ceusters W, Klagges B, et al. "Relations in biomedical ontologies". Genome Biol
6(5):R46 (2005). doi: 10.1186/gb-2005-6-5-r46.
[14] Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., Appleton, G., Axton, M., Baak, A.,
Blomberg, N., Boiten, J., Bonino da Silva Santos, L., Bourne, P.E., Bouwman, J., Brookes, A.J.,
Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S.C., Evelo, C.T., Finkers, R., González-
Beltrán, A.N., Gray, A.J., Groth, P., Goble, C.A., Grethe, J.S., Heringa, J., ’t Hoen, P.A., Hooft,
R.W., Kuhn, T., Kok, R.G., Kok, J.N., Lusher, S.J., Martone, M.E., Mons, A., Packer, A.L., Persson,
B., Rocca-Serra, P., Roos, M., van Schaik, R.C., Sansone, S., Schultes, E.A., Sengstag, T., Slater,
T., Strawn, G.O., Swertz, M.A., Thompson, M., van der Lei, J., van Mulligen, E.M., Velterop, J.,
Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., & Mons, B. (2016). “The FAIR
Guiding Principles for scientific data management and stewardship. Scientific Data, 3”.
[15] Smith, B., Ashburner, M., Rosse, C., Bard, J.B., Bug, W.J., Ceusters, W., Goldberg, L.J., Eilbeck,
K., Ireland, A., Mungall, C.J., Leontis, N.B., Rocca-Serra, P., Ruttenberg, A., Sansone, S.,
Scheuermann, R.H., Shah, N.H., Whetzel, P.L., & Lewis, S.E. (2007). “The OBO Foundry:
coordinated evolution of ontologies to support biomedical data integration”. Nature
Biotechnology, 25, 1251-1255.
[16] Catterall WA. "Voltage-gated sodium channels at 60: structure, function and
pathophysiology". The Journal of Physiology 590(11) (2012) 2577-89. doi:
10.1113/jphysiol.2011.224204.
[17] Ferguson KM. "Structure-based view of epidermal growth factor receptor regulation".
Annu Rev Biophys. 37 (2008) 353-73. doi:
10.1146/annurev.biophys.37.032807.125829. PMID: 18573086; PMCID:
PMC2745238.
[18] Lemmon MA, Schlessinger J, Ferguson KM. "The EGFR family: not so prototypical
receptor tyrosine kinases". Cold Spring Harb Perspect Biol. 2014 Apr 1;6(4):a020768.
doi: 10.1101/cshperspect.a020768. PMID: 24691965; PMCID: PMC3970421.
[19] Zhang X, Gureasko J, Shen K, Cole PA, Kuriyan J. "An allosteric mechanism for activation
of the kinase domain of epidermal growth factor receptor". Cell. 125(6) (2006) 1137-
49. doi: 10.1016/j.cell.2006.05.013. PMID: 16777603.
[20] Purba ER, Saita EI, Maruyama IN. "Activation of the EGF Receptor by Ligand Binding
and Oncogenic Mutations: The "Rotation Model"". Cells. 6(2) (2017) 13 doi:
10.3390/cells6020013. PMID: 28574446; PMCID: PMC5492017.
[21] Zanetti-Domingues LC, Korovesis D, Needham SR, Tynan CJ, Sagawa S, Roberts SK,
Kuzmanic A, Ortiz-Zapater E, Jain P, Roovers RC, Lajevardipour A, van Bergen En
Henegouwen PMP, Santis G, Clayton AHA, Clarke DT, Gervasio FL, Shan Y, Shaw DE,
Rolfe DJ, Parker PJ, Martin-Fernandez ML. "The architecture of EGFR's basal complexes
reveals autoinhibition mechanisms in dimers and oligomers". Nat Commun. 9(1):4325.
(2018) doi: 10.1038/s41467-018-06632-0. PMID: 30337523; PMCID: PMC6193980.
[22] Arkhipov A, Shan Y, Das R, Endres NF, Eastwood MP, Wemmer DE, Kuriyan J, Shaw DE.
"Architecture and membrane interactions of the EGF receptor". Cell 31;152(3) (2013)
557-69. doi: 10.1016/j.cell.2012.12.030. PMID: 23374350; PMCID: PMC3680629
[23] Martins Dos Santos V, Anton M, Szomolay B, et al. "Systems Biology in ELIXIR:
modelling in the spotlight". F1000Research 2022;11:1265 doi:
10.12688/f1000research.126734.1.
[24] Lelimousin M, Limongelli V, Sansom MS. “Conformational Changes in the Epidermal
Growth Factor Receptor: Role of the Transmembrane Domain Investigated by Coarse-
Grained MetaDynamics Free Energy Calculations”. J Am Chem Soc. 138(33) (2016)
10611-22. doi: 10.1021/jacs.6b05602. Epub 2016 Aug 11. PMID: 27459426; PMCID:
PMC5010359.