=Paper=
{{Paper
|id=None
|storemode=property
|title=wKinMut: An integrated tool for the analysis and interpretation of mutations in human protein kinases
|pdfUrl=https://ceur-ws.org/Vol-916/Paper3.pdf
|volume=Vol-916
|dblpUrl=https://dblp.org/rec/conf/eccb/IzarzugazaVPV12
}}
==wKinMut: An integrated tool for the analysis and interpretation of mutations in human protein kinases==
https://ceur-ws.org/Vol-916/Paper3.pdf
wKinMut: An integrated tool for the analysis and interpretation of
mutations in human protein kinases
1,2, 1 1 1,
Jose MG Izarzugaza *, Miguel Vazquez , Angela del Pozo and Alfonso Valencia *
1
Spanish National Cancer Research Centre (CNIO). Structural Biology and BioComputing
Programme. C/Melchor Fernandez Almagro, 3. E-28029 Madrid (Spain)
2
Center for Biological Sequence Analysis, Department of Systems Biolology, Technical University of
Denmark, 2800 Lyngby, Denmark
* To whom correspondence should be addressed: JMGI: jmgonzalez@cnio.es, AV: avalencia@cnio.es
ABSTRACT
Protein kinases are involved in relevant physiological functions and a broad number of mutations in
this superfamily have been reported in the literature to affect protein function and stability.
Unfortunately, the exploration of the consequences on the phenotypes of each individual mutation
remains a considerable challenge. wKinMut facilitates the exploration of the information available
about individual mutations by integrating prediction approaches with the automatic extraction of
information from the literature (text mining) and several state‐of‐the‐art databases.
The wKinMut web‐server offers direct prediction of the potential pathogenicity of the mutations
from a number of methods, including our recently developed prediction method based on the
combination of information from a range of diverse sources, including physicochemical properties
and functional annotations from FireDB and Swissprot and kinase‐specific characteristics such as the
membership to specific kinase groups, the annotation with disease‐associated GO terms or the
occurrence of the mutation in PFAM domains, and the relevance of the residues in determining
kinase subfamily specificity from S3Det. This predictor yields interesting results that compare
favourably with other methods in the field when applied to protein kinases.
Together with the predictions, wKinMut offers a number of integrated services for the analysis of
mutations. These include: the classification of the kinase, information about associations of the
kinase with other proteins extracted from iHop, the mapping of the mutations onto PDB structures,
pathogenicity records from a number of databases and the classification of mutations in large‐scale
cancer studies. Importantly, wKinMut is connected with the SNP2L system that extracts mentions of
mutations directly from the literature, and therefore increases the possibilities of finding interesting
functional information associated to the studied mutations.
wKinMut has been used during the last year for the analysis of the consequences of mutations in the
context of a number of cancer genome projects, including the recent analysis of Chronic
Lymphocytic Leukemia cases and is publicly available at http://wkinmut.bioinfo.cnio.es.
INTRODUCTION
Current high-throughput resequencing screenings (1, 2, 3) represent a powerful set of techniques to
discover large numbers of mutations. Of these, only a small fraction are causally implicated in disease
onset and therefore, separating the wheat from the chaff is still a major challenge (4). The
interpretation of the overwhelming wealth of data also represents an issue in other fields, such as
protein function prediction (5). For a small subset of the new mutations discovered, experimental
information regarding the relationship between the mutation and the underlying biochemical
mechanism is known. However, there is no information for the remaining mutations. The intensive
requirement of resources makes it unfeasible to experimentally test the association of all these
mutations to disease, and to characterize their functional effects. Nevertheless, this problem is very
amenable to in silico predictors (4, 6, 7). Different approaches are currently available to predict the
probability of a newly discovered mutation being implicated in disease. Some methods identify crucial
positions in a given protein and derive generalized rules to predict the pathogenicity of mutations.
Other methods assume that evolutionarily conserved protein residues are important for protein
structure, folding and function, whereby mutations in these residues are considered deleterious (8).
Variations on this principle lead to methods that predict deleterious mutations by evaluating changes
in evolutionarily conserved PFAM motifs (9). A number of systems use protein structures to
characterize substitutions that significantly destabilize the folded state. There are also methods that
integrate prior knowledge in the form of both sequence and structure-related features from a set of
experimentally characterized mutations to train automatic machine-learning systems. These systems
can infer the pathogenicity of new mutations based on the cases evaluated. Albeit similar in purpose,
very different machine-learning methods can be implemented. Among them, probably the most
popular ones are: rule-based systems (10, 11, 12), decision trees (13), random forests (14, 15),
neural networks (16, 17), Bayesian methods (18) and SVMs (19, 20, 21, 22, 23). Recently, some
meta approaches that combine different methodologies have been implemented. For example,
Condel (24) integrates five of the most widely employed computational tools for detecting pathogenic
single nucleotide variations. Predictors can also be classified according to their scope. Most of the
predictors are generally applicable to amino acid sequences from any protein family, while a few of
them include properties that apply only to a given protein family of interest; i.e. protein kinase specific
predictors (20, 23). These family-related features bring discriminative information that justifies the
development of specialized predictors.
A broad number of mutations in the protein kinase superfamily have been reported in the literature (25)
and a subset of them is known to disrupt protein structure and function (26). For some cases, since
human protein kinases are involved in a plethora of physiological functions, this disruption can be
causally associated to disease (27). Still, the majority of protein kinase mutations are tolerated without
apparent significant effects (28, 29).
In previous publications, we have discussed the preferential distribution of germline pathogenic
deviations (30) and driver somatic mutations (31) with respect to regions of functional and structural
importance. Here we present, wKinMut, an integrated web-service for the collection of information
from multiple sources and for the prediction of the pathogenicity of mutations by combining several
prediction approaches. The objective of wKinMut is to provide a one-stop resource for the analysis
and interpretation of the consequences of mutations in the protein kinase superfamily.
DESCRIPTION AND IMPLEMENTATION
wKinMut represents the first resource to provide an integrated tool for the analysis and interpretation
of the consequences of mutations in the protein kinase superfamily. The main objective of wKinMut is
to aid computational biologists and clinicians to prioritize pathogenic mutations and to understand the
mechanisms by which some mutations lead to disease, and particularly, to cancer.
The tool presented here, incorporates information retrieval and prediction approaches and displays
information from diverse sources. First, it simplifies the collection of information about the mutations,
such as the classification, domain architecture, functional annotations and plausible interaction
partners of the kinase. Furthermore, kinase mutations are analyzed in their structural context and
mentions in dedicated databases, genotyping studies and the literature suggesting an implication in
disease are also presented. Second, wKinMut estimates the theoretical pathogenicity of kinase
mutations with three different approaches, including our newly developed kinase-specific method,
KinMut (23), based on the evaluation of a wide set of sequence-derived features that describe each
independent mutation. The affected domain and kinase group, diverse functional annotations, residue
physicochemical properties and relevance of the mutated residues in determining subfamily specificity
are considered.
Although wKinMut’s web interface was designed to be simple and user-friendly, we have included a
tutorial and the corresponding help page that will guide the reader through the different steps in the
process, including the submission of mutations and the interpretation of the different result views.
wKinMut has been implemented mostly using the Ruby programming language. The functionality is
implemented as a workflow accessible through a REST interface that can render the results either in
JSON format or HTML. The later constitutes the interface described in this document. Some of data
resources that support this system, such as gene descriptions or iHOP interactions, are queried
remotely through the internet as demanded; but are then cached to improve subsequent access. The
server incorporates some additional caching schemes to improve performance in the back-end, by
persisting the job results, and in the web interface, by caching the HTML.
WEB INTERFACE
Step 1: Submission of mutations for analysis
The input to wKinMut are non-synonymous mutations in the protein kinase superfamily. The web-
service accepts several input formats, the simplest of which encodes the Uniprot/Swissprot accession
number, the wild type residue, the position and the mutated residue. It is interesting to highlight at this
point that non-standard amino acids and truncating mutations will be excluded from the analysis. An
example of this format would be a mutation from Glycine to Alanine in position 719 of the human
epidermal growth factor receptor, that will be encoded as P00533_G719A. In the following sections,
we will use this example to guide the reader through the different result views (Figure 1). Multiple
mutations can be submitted at a time, either as a plain text file or directly via the applications form, the
sample dataset provided as part of wKinMut’s documentation can be used as a formatting guide.
Step 2: Interpretation of the consequences of the mutations.
The first output the user will get right after submitting the mutations is a summary page with useful
information about the requested mutations (Figure 1, panel a). It includes a description of the proteins
in Uniprot, the membership to kinase groups in the classification in KinBase (32,33) and the
estimation of the pathogenicity of mutations attending to our kinase-specific predictor of pathogenicity,
KinMut (23). The prediction of the pathogenicity will be discussed in detail in a forthcoming section,
nevertheless we decided to include this information at this step as a guide to prioritize mutations. It
might be interesting to point out here that users interested only in the results from KinMut, can find a
link to the predictions in this summary page that can be accessed programmatically. The scope of
wKinMut goes beyond providing raw prediction of pathogenicity from KinMut, the web-service’s main
goal is to aid computational biologists and clinicians to understand and to interpret the consequences
of kinase mutations. Hence, information complementary to KinMut predictions, is provided. In the
summary table, the ‘View’ link in the right-most ‘Details’ column (Figure 1, panel b) will redirect the
user to another page containing this complementary information, which includes: the values of the
features used for classification, PFAM domains affected by the mutation, protein-protein interaction
information extracted from the literature with iHop (34), mentions of the mutations in the literature
automatically mined with SNP2L (25,35), and existing records of the mutations in other dedicated
databases. This additional information is intended to provide the basic background to help to
understand and interpret the consequences of the mutations. Each individual piece of information will
be discussed thoroughly in the following sections.
General information about the protein/gene: Information under the ‘gene/protein’ tab (Figure 1, panel
c) focuses on information shared by all mutations in the same kinase. Background information such
as the gene name, the formal description in Uniprot and the classification in KinBase (32,33) of the
kinase is provided. In addition, the system provides the Gene Ontology terms with which the kinase
has been annotated in each of the independent sub-ontologies (namely Molecular Function, Cellular
Compartment and Biological Process). This information provides clues to unveil the function of the
kinase and it is used by KinMut to calculate the likeness of the protein (and subsequently the mutation)
to play a role in disease.
PFAM domains: In a previous publication (23) we demonstrated that mutations occurring in certain
domains such as the Tyrosine kinase domain (PKinase Tyr, according to PFAM) are more likely to
cause disease. This is coherent with the assumption that the function of some domains is more
important that the function of others. In wKinMut, this information is contained in the ‘PFAM domains’
tab (Figure 1, panel d), which displays the domain (or domains, in some cases) where the mutation is
occurring and the alignment used by PFAM as seed to generate the domain family. The alignment is
evaluated in terms of sequence conservation. Under the assumption that conserved regions have
been preserved by evolution, this information can help the user to identify important regions in the
structure of the domain.
Mapping the mutations onto structures: To understand the consequences of mutations might have in
protein stability and function it is sometimes useful to study the mutations in their structural contexts.
However, mapping mutations from sequences to structures is not always trivial (36). Under the
‘Structures‘ tab, wKinMut enables the visualization of the mutation mapped to all available structures.
(Figure 1, panel e). In addition, the versatility of the Jmol applet implemented in wKinMut allows
advanced users to improve the visualization and to design their own experiments.
Prediction of the pathogenicity: In wKinMut the theoretical pathogenicity of mutations is assessed by
three independent methods, namely SIFT (8), MutationAssessor (10) and KinMut (23). This
information is displayed in the ‘Pathogenicity’ tab (Figure 1, panel f). SIFT (8) predicts whether non-
synonymous mutations are prone to affect protein function. This prediction is based on the degree of
conservation of the residues in sequence alignments derived from closely related sequences. A
threshold value of 0.05 is used to determine that mutations are likely to be pathogenic.
MutationAssessor (10) is a predictor of the functional impact of amino acid mutations in proteins. The
evolutionary conservation of the affected residues is evaluated in a set of homologous sequences.
KinMut (23) is a kinase-specific predictor of the pathogenicity of mutations. It relies in a machine-
learning approach (SVM) to evaluate a number of sequence-derived features that describe kinase
mutations from different perspectives, including: a) at the gene level, the membership to a Kinbase
group and Gene Ontology terms. b) at the domain level, the occurrence of the mutation inside a
PFAM domain, and c) at the residue level, several properties including amino acid type, functional
annotations from Swissprot and FireDB (37), specificity-determining positions, etc. SVM scores
greater than -0.5 indicate that the mutation is very likely pathogenic. The values of these features are
also displayed in this section of the web-service to aid to interpret the predictions. Please, refer to the
original publications for information on the individual characteristics, capabilities and validation of
each predictor.
Mutations in databases: The wealth of knowledge provided by current research is usually stored in
databases. A number of them store information about mutations from diverse perspectives. In
wKinMut (Figure 1, panel g) we collect information from four different sources (namely the Uniprot
Variant Pages (38), KinMutBase (39), SAAPdb (26) and COSMIC (40)) in an attempt to cover all
aspects of protein kinase mutation. The information displayed includes information about the
structural consequences of mutations, experiments associating mutations with a certain disease, or
the proof that a mutation has been observed in a cancer sample.
Automatic extraction of mutations from the literature: Unfortunately, the databases referred in the
previous section do not contain all current knowledge about mutations. Even in the cases where a
database record exists, the knowledgebase cannot always store all contextual information. The
context is sometimes very important for the correct interpretation of the predictions: experimental
conditions, patients’ habits and clinical histories, etcetera. wKinMut provides pointers to mentions of
the mutations in the literature under the ‘Literature’ tab (Figure 1, panel h). We extract this information
automatically using our in-house text mining approach,SNP2L (25). In brief, SNP2L is a literature
mining pipeline for the automatic extraction and disambiguation of singlepoint mutation mentions from
both abstracts as well as full text articles, followed by a sequence validation check to link mutations to
their corresponding kinase protein sequences.
Automatic determination of interaction partners: wKinMut integrates Protein-Protein Interactions (PPI)
gathered from iHOP in the homonymous tab (Figure 1, panel i). Briefly, iHOP is a powerful text mining
system to automatically extract protein protein interactions from PubMed abstracts. To relate the
interaction information with its context, the sentences including the interaction mentions are also
provided.
OVERVIEW AND FUTURE PERSPECTIVES
wKinMut facilitates the exploration of the information available about individual mutations by
integrating prediction approaches with the automatic extraction of information from the literature (text
mining) and several currently available databases. wKinMut works as an open accessible web server.
The system offers direct prediction of the potential pathogenicity of the mutations from a number of
methods, including our recently developed prediction method based on the combination of information
from a range of diverse sources with a machine learning system (23). The features used by our new
prediction system include: general physicochemical properties, annotations of known functional sites
from FireDB and Swissprot and kinase-specific characteristics such as membership to a specific
group of kinases, annotations of disease associations extracted from GO terms and mapping of
PFAM domains, and relevance of the residues for the differences between groups of kinases. In
addition to the predictions, wKinMut offers a number of integrated complementary services that help
to understand the consequences and the mechanism of the mutations. These services include the
classification of the kinase, information about associations of the kinase with other proteins extracted
directly extracted from the Medline abstracts, the mutations on the corresponding protein structures,
and possible relations with pathogenicity recorded in disease-variation databases and from large-
scale cancer studies. An important component of wKinMut is the access to information about the
mutations extracted directly from the literature. This information is important for the contextualization
of the consequences of the mutations. wKinMut uses our previously developed SNP2L (25), that has
been shown to provide a substantial addition to the information provided by public databases and
repositories..
In summary, we think that wKinMut constitutes a powerful one-stop shop for the study of the potential
pathogenic potential of mutations in protein kinases. As such, wKinMut will be of interest for
bioinformaticians and computational biologists that can use the information provided by the server
programmatically as part of their own analysis pipelines, and it can be also useful to biologists and
clinicians who can browse and explore punctual information easily from the provided interface. We
have used wKinMut during the past year for the analysis of the consequences of mutations in the
context of a number of personalized cancer genome projects (see 41), including the recent analysis of
Chronic Lymphocytic Leukemia cases (42, 43).
A further development of the presented system would consider the analysis of the downstream
consequences of mutations in relation to potential and known post-translational modifications and
their interelations (see 44,45). We are interested in extending wKinMut capabilities to the analysis of
the combined effect of mutations in pathways and signalling networks in where kinases are essential
components..
wKinMut is publicly available at http://wkinmut.bioinfo.cnio.es
ACKNOWLEDGEMENTS
The authors thank the members of the Structural Biology and Biocomputing Programme (CNIO),
especially A. Rausell, D. Juan, I. Ezkurdia and T. Pons, for interesting discussion and comments on
this manuscript. This research was supported by OpenPhacts European project (115191-2) and
Spanish Ministry of Science and Innovation project BIO2007-6685
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Cell 150, 413–425, July 20, 2012
TABLE AND FIGURES LEGENDS
Figure 1. Summary of the different result pages in wKinMut: Example of a Gly-719-Ala mutation in the
human epidermal growth factor receptor.