D190–D199 Nucleic Acids Research, 2017, Vol. 45, Database issue Published online 28 November 2016
doi: 10.1093/nar/gkw1107
InterPro in 2017––beyond protein family and domain
annotations
Robert D. Finn1,*
, Teresa K. Attwood2
, Patricia C. Babbitt3
, Alex Bateman1
, Peer Bork4
, Alan
J. Bridge5
, Hsin-Yu Chang1
, Zsuzsanna Doszt´anyi6
, Sara El-Gebali1
, Matthew Fraser1
,
Julian Gough7
, David Haft8
, Gemma L. Holliday3
, Hongzhan Huang9
, Xiaosong Huang10
,
Ivica Letunic11
, Rodrigo Lopez1
, Shennan Lu12
, Aron Marchler-Bauer12
, Huaiyu Mi10
,
Jaina Mistry1
, Darren A Natale13
, Marco Necci14
, Gift Nuka1
, Christine A. Orengo15
,
Youngmi Park1
, Sebastien Pesseat1
, Damiano Piovesan14
, Simon C. Potter1
, Neil
D. Rawlings1
, Nicole Redaschi5
, Lorna Richardson1
, Catherine Rivoire5
,
Amaia Sangrador-Vegas1
, Christian Sigrist5
, Ian Sillitoe15
, Ben Smithers7
,
Silvano Squizzato1
, Granger Sutton8
, Narmada Thanki12
, Paul D Thomas10
,
Silvio C. E. Tosatto14,16
, Cathy H. Wu9
, Ioannis Xenarios5
, Lai-Su Yeh13
, Siew-Yit Young1
and
Alex L. Mitchell1
1
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome
Campus, Hinxton, Cambridge CB10 1SD, UK, 2
School of Computer Science, University of Manchester, UK,
3
Department of Bioengineering & Therapeutic Sciences, University of California, San Francisco, CA 94143, USA,
4
European Molecular Biology Laboratory, Biocomputing, Meyerhofstasse 1, 69117 Heidelberg, Germany, 5
Swiss-Prot
Group, SIB Swiss Institute of Bioinformatics, CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland, 6
MTA-ELTE
Lend¨ulet Bioinformatics Research Group, Department of Biochemistry, E¨otv¨os Lor´and University, P´azm´any P´eter
s´et´any 1/c, Budapest, Hungary, 7
Computer Science department, University of Bristol, Woodland Road, Bristol BS8
1UB, UK, 8
Bioinformatics Department, J. Craig Venter Institute, 9714 Medical Center Drive, Rockville, MD 20850,
USA, 9
Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19711, USA,
10
Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA
90033, USA, 11
Biobyte Solutions GmbH, Bothestr. 142, 69126 Heidelberg, Germany, 12
National Center for
Biotechnology Information, National Library of Medicine, NIH Bldg, 38A, 8600 Rockville Pike, Bethesda, MD 20894,
USA, 13
Georgetown University Medical Center, 3300 Whitehaven St, NW, Washington, DC 20007, USA,
14
Department of Biomedical Sciences and CRIBI Biotech Center, University of Padua, via U. Bassi 58/b, 35131
Padua, Italy, 15
Structural and Molecular Biology, University College London, Darwin Building, London WC1E 6BT, UK
and 16
CNR Institute of Neuroscience, via U. Bassi 58/b, 35131 Padua, Italy
Received October 24, 2016; Editorial Decision October 25, 2016; Accepted October 27, 2016
ABSTRACT
InterPro (http://www.ebi.ac.uk/interpro/) is a freely
available database used to classify protein sequences
into families and to predict the presence
of important domains and sites. InterProScan is the
underlying software that allows both protein and nucleic
acid sequences to be searched against InterPro’s
predictive models, which are provided by its
member databases. Here, we report recent developments
with InterPro and its associated software, including
the addition of two new databases (SFLD and
CDD), and the functionality to include residue-level
annotation and prediction of intrinsic disorder. These
developments enrich the annotations provided by InterPro,
increase the overall number of residues annotated
and allow more specific functional inferences.
INTRODUCTION
In the post-genomic era, generation of biological sequence
data is no longer a scientific barrier; rather, data storage and
analysis have become the new bottlenecks in terms of cost
*To whom correspondence should be addressed. Tel: +44 1223 492 679; Fax: +44 1223 494 46; Email: rdf@ebi.ac.uk
C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Nucleic Acids Research, 2017, Vol. 45, Database issue D191
and time (1). With the potential to sequence entire genomes,
or to generate hundreds of millions of sequences from environmental
samples, the pace of generating sequence data
now outstrips the rate of experimental characterisation by
many orders of magnitude (2). Consequently, rapid, accurate
automatic functional annotation of large numbers of
sequences has become a major challenge.
The InterPro database aims to meet this challenge by integrating
diverse information about protein families, domains
and functional sites. Central to the resource are diagnostic
models (profile hidden Markov models (HMMs),
profiles, position-specific scoring matrices or regular expressions,
collectively known as ‘signatures’), against which protein
sequences can be searched to determine their potential
functions. The signatures are provided by 14 different
member databases: 12 of these are long-standing members
of the InterPro Consortium (CATH-Gene3D (3), HAMAP
(4), PANTHER (5), Pfam (6), PIRSF (7), PRINTS (8),
ProDom (9), PROSITE Patterns (10), PROSITE Profiles
(10), SMART (11), SUPERFAMILY (12) and TIGRFAMs
(13)); two are new members, the Conserved Domains
Database (CDD) (14) and Structure–Function Linkage
Database (SFLD) (15) having been added in 2016.
The source databases each have their own individual
biological focus, method of signature production, and/or
signature-match processing. The diversity of approaches
helps to ensure that annotations are as comprehensive as
possible. For example, related Pfam (profile HMM-based)
and Prosite Profiles entries often match subtly different sets
of proteins; united however, they match most, if not all,
known members of a protein family, while eliminating falsepositive
annotations. Furthermore, the different databases
offer complementary levels of protein classification, from
broad-level (e.g., a protein is a member of a superfamily) to
more fine-grained assignments (e.g. a protein is a member of
a specific family, or possesses a particular type of domain).
These different levels of granularity are used by InterPro to
produce a hierarchical classification system: one or moremember
database signatures are integrated into an InterPro
entry, and, where appropriate, relationships are highlighted
between different entries, identifying those that represent
smaller, functionally specific subsets of a broader entry.
Database curation
InterPro entries are classified into types (families, domains,
repeats or sites) depending on the biological entity they represent.
Family and domain entries are placed into distinct,
non-overlapping hierarchies: domain entries are able to occur
in the same hierarchy as other domains, but not within
the same hierarchy as family entries, and vice versa.
Entries are manually annotated with literaturereferenced
free-text descriptions, explaining the biological
information that may be inferred for proteins that match a
given signature. Where possible, each entry is also associated
with Gene Ontology (GO) (16) terms, which provide a
controlled vocabulary to describe protein function, cellular
localisation and involvement in wider biological pathways
and processes. The granularity of the member database
signatures (and hence InterPro entries) determines the
specificity of the functional annotation and GO terms that
can be assigned. For example, an InterPro entry representing
a small family of functionally conserved enzymes
that act on a single substrate, such as the glycerol kinases
(IPR005999), is annotated with more specific functional
information and terms from the GO hierarchy than an
entry representing a more diverse enzyme family acting on
a larger class of substrates, such as their parent InterPro
entry, the FGGY carbohydrate kinases (IPR000577) (see
Figure 1).
At each release of the database, InterPro entries are
checked, and updated where necessary, to ensure that the
annotations remain accurate. Updates to annotations are
typically made in response to changes in signature specificity
(e.g. if a signature has been rebuilt by a member
database to recognise a tighter functionally-related group
of proteins, or to match more distant homologues) or to improved
scientific understanding of the function of a protein
family (17).
InterPro entries are also automatically annotated with
cross-references to a range of relevant databases, including
the pathway databases ENZYME (18), MetaCyc (19), UniPathway
(20) and KEGG (21), and various 3D structure
databases (22,23).
Use of the resource
InterPro plays a major role in the analysis and annotation
of sequence data held in the UniProt Knowledgebase
(UniProtKB), the central hub of protein sequences
(24). Protein annotations derived from InterPro’s member
database signatures are calculated using the InterProScan
software package (25) on a monthly basis. These annotations
are then used by UniProtKB curators to help annotate
Swiss-Prot records and as input to the automated systems
that add annotation to UniProtKB/TrEMBL. InterPro’s
protein match information is also made available to
the public via XML files, and the database’s Web interfaces
and services, which can be searched with a protein
sequence, a UniProtKB protein identifier, an InterPro or
member database identifier, GO term, or free text.
In addition to its use in UniProtKB annotation, InterPro
is widely used by the scientific community. Its data feed
into a host of annotation pipelines, including ENSEMBL
(26), ENSEMBL Genomes (27), PDBe (28), BLAST2GO
(29), PhytoPath (30), the digenic diseases database - DIDA
(31), and the Endeavour candidate gene prioritisation server
(32). The InterProScan Web services are extensively used,
processing in excess of 40 million sequence searches per
month. InterPro’s data and analysis software are increasingly
used in the analysis of metagenomic data: in addition
to underpinning EMBL-EBI’s in-house EBI Metagenomics
resource (33), the MEGAN metagenomics analysis tool (34)
now has the ability to process InterPro-derived data and GO
terms.
New protein family member databases
The constituent databases of InterPro have remained fixed
for the last 7 years (see Figure 2), and have changed little
in the last decade. Since the last update paper (35),
we have been evaluating a range of different databases
D192 Nucleic Acids Research, 2017, Vol. 45, Database issue
Figure 1. Example of an InterPro family hierarchical relationship. The FGGY carbohydrate kinases entry (IPR000577) provides a parent to a series of
child entries that match smaller, more functionally-specific sets of proteins.
Figure 2. Timeline showing the member databases that have joined InterPro since version 1.0, released in 2000.
Nucleic Acids Research, 2017, Vol. 45, Database issue D193
that could enhance the information contained within InterPro,
in terms both of adding more comprehensive coverage
and in providing more fine-grained functional annotations
at the individual amino acid level. As a result, two
new databases have been added to the resource: the Conserved
Domains Database (CDD) and Structure–Function
Linkage Database (SFLD).
CDD is a manually curated protein annotation resource
representing domain footprints conserved in molecular evolution.
Each domain entry is modelled as a multiple sequence
alignment, which is also converted into a positionspecific
scoring matrix (PSSM) that allows fast identification
of conserved domains in protein sequences via RPSBLAST
(36). NCBI-curated domains use 3-dimensional
structure information to explicitly define the boundaries of
known conserved domains, and to provide comprehensive
and accurate annotation of protein sequences with the locations
and boundaries (footprints) of known conserved
domains, including location of conserved/functional sites.
CDD content also includes domain models imported from a
number of external source databases (Pfam, SMART, COG
(37), PRK (38) and TIGRFAMs). Only CDD’s own models
have been imported into InterPro, mainly owing to the fact
that Pfam, SMART and TIGRFAMs are already present in
InterPro. Within InterProScan, the RPS-BLAST is substituted
by a piece of software called ‘rpsbproc’, an amendment
to the standalone RPS-BLAST program, which allows
detailed CD-Search results, including domain superfamily
assignments and the predicted locations of conserved
sites to be reproduced locally. As well as being bundled
into InterProScan, the rpsbproc utility is available from
the CDD FTP site at ftp://ftp.ncbi.nih.gov/pub/mmdb/cdd.
This package ensures that InterPro can faithfully reproduce
the results from CDD, thus ensuring consistency of annotations
between the CDD and InterPro Web servers for the
same sequence.
SFLD is a manually-curated classification resource describing
structure-function relationships for functionally diverse
enzyme superfamilies. Members within a single superfamily
are derived from a common ancestor and mediate
a diverse set of related, yet distinct reactions. For example,
the enzymes within a superfamily may share activesite
features (such as residues in a nucleophile) associated
with conserved functional attributes (e.g. part of a reaction
mechanism or substrate binding). Consequently, in some
annotation resources, such superfamilies are grouped together
in a single entry (e.g. the Radical SAM entry in
Pfam, PF04055), but are inadequately subdivided into their
functional groups, leading to imperfect annotations.
To overcome annotation errors and enable transfer of
functional features, SFLD provides hierarchical annotations
at multiple levels. SFLD subdivides superfamilies into
subgroups based upon sequence information, then into
families of enzymes known to catalyze the same reaction
using the same mechanistic strategy. The family level of the
hierarchy defines variations in each set of active site residues
that distinguish that family’s particular reaction type from
other families in the superfamily (Figure 3).
To enable InterPro to perform SFLD annotations, the
two resources have developed an approach whereby SFLD
produces multiple sequence alignments representing the different
superfamilies, subgroups and families. Most of the
alignments, particularly those associated with subgroups
and families, are annotated with key catalytic residues that
are important to the chemical reaction performed by the sequences
found within that set. From these alignments, InterPro
builds a profile HMM library that is used to annotate
sequences. Significant sequence matches are then verified
against the key catalytic residues (if present), only those
sequences matching all residues being assigned to the family.
Thus, the profile HMMs and residue verification steps
act as a two-stage assignment criterion, enabling very finegrained
annotations to be made, at rates only marginally
slower than searching profiles alone.
Integration plans for CDD and SFLD
When a new member database is added to InterPro, the
database is added as an entire set of unintegrated signatures,
which are then manually annotated and added
to InterPro entries, as described above, by the curation
team. Thus, CDD and SFLD matches are now available
for all signatures, but require a large curation effort
to fully integrate each new resource within the InterPro
hierarchy. Until integrated, the annotations from
these databases are provided at the bottom of each protein
page, in the ‘Detailed signature matches’, under the
‘unintegrated signatures’ listing. They are provided in a
similar way in the InterProScan output. For each member
database, it is possible to get the complete listing of
signatures from the following pages: http://www.ebi.ac.uk/
interpro/member-database/ member-database , for example
http://www.ebi.ac.uk/interpro/member-database/CDD
for the CDD database.
As of InterPro release 58.0, CDD version 3.14 added 11
273 signatures, of which 1,005 have now been integrated into
InterPro over a period of 4 months. During this time, there
has been a dual approach to integrating CDD signatures:
(i) adding those that are directly equivalent to other member
database signatures that have already been included in
InterPro, as this can be done with minimal curation; (ii)
adding those that match sequence sets not covered by any
other member database, as these add coverage to InterPro.
SFLD, a smaller scale database with a more specific focus,
has been added to InterPro more recently. SFLD version 1.0
provided 480 signatures in total, 17 of which were integrated
over a 2-week period into the most recent InterPro 60.0 release.
Increasing the number of integrated signatures from
these databases will be a significant focus for InterPro in the
forthcoming year.
Both CDD and SFLD provide hierarchical classifications
(see Figure 3), but their hierarchies differ from each other
and from that of InterPro. In the case of CDD, the parent
signature in the hierarchy does not aim to match all of
the sequences matched by its children. Rather, CDD parent
entries often provide signatures aiming to cover the relatively
few family member sequences that are not matched
by the child signatures. As such, one of the most important
functions of the parent entry is to provide a root node
through which child signatures can be associated. This is
significantly different from the hierarchical classification
approach used in InterPro, where a parent level entry should
D194 Nucleic Acids Research, 2017, Vol. 45, Database issue
Figure 3. Examples of the CDD and SFLD hierarchies (A and B). (A) CDD models for related domains are organized hierarchically, reflecting major events
in the domain family’s molecular evolution and functional diversification. The hierarchy usually follows a tree structure obtained from (C) phylogenetic
analysis of multiply aligned sequences. The relationship between the CDD entries in panel A and the sequences in panel B is indicated by colour. The
top ‘parent’ entry (isoprenoid biosynthesis enzymes, Class 1 superfamily) would be less specific than the ‘leaf’ node entry (trans-isoprenyl diphosphate
synthase, head-to-head). (B) The corresponding superfamily, Isoprenoid Synthase Type I, from SFLD. The specificity relationships between the entries is
similarly arranged as in panel A. (D) SFLD network analysis graph showing the sequence identity relationships between the Isoprenoid Synthase Type I
superfamily members. The E-value threshold for the network is 1e-10 and sequences within nodes share 50% or more sequence identity, calculated using
CD-HIT. Note, figures C and D are visualizations from the respective source database and are not available from the InterPro website. These figures
demonstrate the different approaches for visualizing and defining relationships between families.
(if possible) provide coverage of all proteins matching its
child entries. As a result, CDD’s higher-level signatures will
not be integrated into InterPro. Instead, the aim will be to
identify signatures from other member databases that can
be substituted in their place, grouping the child entries to-
gether.
The SFLD hierarchy is closer to that provided by InterPro,
but with some important differences. Superfamilylevel
entries in SFLD tend to be based upon the common
amino acid core of a set of matching sequences. Descending
the hierarchy, the child signatures tend to increase in
length, pulling in more accessory domains, which provide
functional specificity. As a result, while most SFLD entries
correspond to InterPro family-type entries, some superfamilies
may appear more like domains (for example, where the
functional unit of a sequence family is represented only by
a single domain). InterPro aims to follow the SFLD hierarchy
as closely as possible, integrating SFLD entries as ‘type’
Family. Nevertheless, some discrepancies will inevitably occur
as we try to merge this hierarchy with InterPro’s hierarchy.
This is consistent with the current integration strategy
for member databases that define families and domains
slightly differently from InterPro.
Nucleic Acids Research, 2017, Vol. 45, Database issue D195
Updates to data content
InterPro is released publicly every 2 months, with a break
in production during August, in harmony with the UniProtKB
release cycle. With each InterPro release, one or more
of its member databases may have been updated, providing
a stream of new entries for integration into the resource.
There have been 12 public InterPro releases since the last
update paper, with an additional 5,158 signatures being integrated
into 3,462 new InterPro entries: 4,035 of these signatures
came from existing member databases, and a further
1123 from CDD and SFLD. The latest release (version 60.0)
contains 41 925 member database signatures integrated into
29 700 InterPro entries. The member database updates that
contributed to recent InterPro releases are shown in Table
1.
The InterPro coverage of sequences in UniProtKB (i.e.
the number of proteins with one or more InterPro annotations)
is calculated at each release. The signatures integrated
into InterPro 60.0 provide matches to 79.8% of the
sequences in UniProtKB release 2016 09 (see Table 2), compared
to 83.5% in release 48.0. GO terms assigned by the
InterPro2GO pipeline (which associates terms with proteins
based on their InterPro matches) are cross-referenced more
than 130 million times in UniProt 2016 09, representing annotation
for 42 million individual proteins. This compares
to 168 million terms for almost 50 million proteins in InterPro
release 48.0/UniProtKB 2014 07.
The reduction in InterPro’s coverage of UniProtKB since
our last report may seem counterintuitive, especially as the
number of sequences in UniProtKB has decreased (from a
peak of 90 million sequences during 2015, to the current
level of 71 million), while the number of InterPro entries and
associated GO terms has gone up (the latter increasing from
∼28 000 in release 48.0 to >32 000 in the current release).
However, the reduction in the number of records in UniProtKB
has been brought about as a redundancy removal effort,
aimed at eliminating close to identical proteomes that
are over-represented in the database. As part of this process,
UniProtKB records belonging to ∼15 000 redundant
bacterial proteomes were moved from UniProtKB into UniParc.
As InterPro provided high levels of coverage for these
proteomes, their removal meant that InterPro’s overall coverage
of UniProtKB has been disproportionately affected.
Increasing coverage of UniProtKB’s smaller, but more diverse,
sequence set will be an ongoing challenge for InterPro
and its member databases in the coming years.
Per residue annotations
We have been investigating ways to expand the scope of InterPro
annotations; specifically, to individual residues that
fall within a region defined by a signature. As signaturederived
matches are currently based on scores across the
entire matched region, the methods can often fail to discriminate
between functionally distinct groups. For example,
the InterPro entry for the calpain catalytic domain
(IPR001300) matches >6000 UniProtKB sequences. While
these are undoubtedly derived from a common ancestor,
in ∼2500 cases the active site residues have been mutated
to residues that are no longer capable of performing the
proteolytic reaction. Thus, many of the sequences are not
active peptidases, but are likely to perform different functions:
e.g. calpamodulin (also known as calpain 6) has
the catalytic Cys replaced by Lys, and is a microtubulestabilising
protein, particularly in embryonic muscle, where
it has been shown to suppress skeletal muscle differentiation
(39). One approach to achieve discrimination between
active and inactive forms is to have specific subfamily signatures,
e.g. the PANTHER subfamily model for calapin-6:
PTHR10183:SF355. However, this requires the production
and curation of many models, and there will always be cases
where it is hard or impossible to ensure that the signature is
capable of separating active and inactive forms, or active
forms with (subtly) different mechanisms of action. In such
cases, accurately annotating active site residues would help
separate active peptidases from inactive homologues.
The mechanism described above is exactly the one employed
by the SFLD database to identify specific groups
of proteins within entries. Thus, the integration of SFLD
into InterPro brings not only increased protein family coverage,
but also the annotation of thousands of important
residues. Other InterPro databases also provide residuelevel
annotations: CDD’s RPS-BLAST matrices are annotated
with a range of per-residue annotations, including
active sites, ligand-binding, protein-protein interactions
and nucleic acid-protein interactions; Pfam contains active
site annotations, based on matches to UniProtKB/SwissProt
sequences; PIRSF annotations can also be extended
to residues, using the PIRSR resource; and HAMAP and
PROSITE annotation rules provide external users and
the UniProt automatic annotation pipeline (UniRule) with
annotations for functionally important residues (single
residues as well as continuous and discontinuous motifs).
To enable the capture of this fine-grained information, we
have extended the InterPro data model to deal with perresidue
annotations. To date, these annotations have been
enabled in InterProScan for SFLD and CDD, with the aim
of adding other databases that also provide per-residue annotations
in future releases. These developments provide a
further tier to the annotations already provided by InterPro.
They will also underpin future opportunities to improve
annotation granularity. Specifically, the data will allow
families to be subdivided into more fine-grained functional
groups based on residue patterns, will allow specific
annotations to be provided for entries (e.g. identifying the
critical functional residues for a given catalytic domain),
and will enable the adoption of rule-based approaches (similar
to those used by HAMAP, PIRSR and PROSITE) for
the assignment of specific functional annotations, such as
GO terms.
Other sequence features annotated by InterPro
For a number of years, InterProScan has included the capability
to annotate signal peptides, transmembrane regions
and coiled-coils, drawing upon a suite of algorithms to
make these annotations (specifically, Coils v2.2, Phobius
v1.01, SignalP v4.1 and TMHMM v2.0 in the latest InterProScan
release). To complement these, we have integrated
a new tool called MobiDB Lite, which provides a consensus
prediction of long disordered regions.
D196 Nucleic Acids Research, 2017, Vol. 45, Database issue
Table 1. Member database release versions integrated into InterPro since release 48.0
InterPro release Member database update
49.0 PROSITE patterns (20.105), PROSITE profiles (20.105)
50.0 PIRSF (3.01)
51.0 TIGRFAMs (15.0), HAMAP (201502.04)
52.0 PROSITE patterns (20.113), PROSITE profiles (20.113)
53.0 Pfam (28.0)
54.0 PANTHER (10.0)
55.0 HAMAP (201511.02)
56.0 PROSITE patterns (20.119), PROSITE profiles (20.119)
57.0 Pfam (29.0), SMART (7.1)
58.0 CDD (1.0)*, HAMAP (201605.11)
59.0 Pfam (30.0), SFLD (1.0)*
60.0 MobiDB**
*New member databases.
**MobiDB is a new non-signature based database that has been integrated into InterPro to provide ID region annotations. See text for details.
Table 2. Coverage of the major sequence databases UniProtKB and UniParc (the non-redundant protein sequence archive) by InterPro signatures
Sequence database Number of proteins in database Number of proteins with one or more matches to InterPro
UniProtKB/Swiss-Prot 552 884 533 303 (96.5%)
UniProtKB/TrEMBL 70 656 157 56 310 112 (79.7%)
UniProtKB (total) 71 209 041 56 843 415 (79.8%)
UniParc 132 489 873 103 835 823 (78.4%)
Intrinsically disordered (ID) protein regions, which do
not adopt a single well-defined conformation in isolation,
rely on a highly flexible state or structural plasticity to
carry out their functions (40,41). While ID regions are
present in all three domains of cellular life, they often exhibit
very little evolutionary conservation and are hence
difficult to predict using signature based methods currently
employed by InterPro member databases. Information
about ID regions largely complements domain and
family annotations. At a closer look, ID encompasses different
phenomena and different predictors can capture complementary
aspects (42,43). MobiDB Lite combines different
predictors to generate a consensus focusing on long
disordered regions. Eight different algorithms (IUPredshort,
IUPred-long (44), GlobPlot (45), DisEMBL-465,
DisEMBL-HL (46), Espritz-DisProt, Espritz-NMR and
Espritz-X-ray (47)) were chosen for their speed and orthogonality
of approaches. The consensus is generated by evaluating
the agreement among predictors and smoothing short
disorder stretches. A strict agreement threshold of at least 5
out of 8 methods favors precision over inclusiveness and a
length cutoff (≥20 residues) helps to discriminate functional
disorder from ambiguous assignments. MobiDB lite annotations
have been enabled in InterProScan, and are available
to external users in the InterProScan 5.20–60.0 release, part
of InterPro release 60.0. Graphical representations of ID
are also to be implemented on InterPro’s Web interfaces (see
Figure 4). From each ID region, InterPro provides a link to
the MobiDB (43) page for the protein of interest where the
breakdown of the individual predictions and additional ID
annotation may be found. The MobiDB Lite consensus corresponds
to the ‘Long Disorder’ track in MobiDB.
DISCUSSION
Since its inception 17 years ago, InterPro has striven to provide
a comprehensive protein classification resource that
enables high-quality functional annotation of protein sequences.
It has met this aim in collaboration with its member
databases, which have provided an invaluable stream
of signatures for integration into the resource. As a result,
InterPro has grown significantly in terms of coverage and
function-annotation specificity, and has developed a substantial
worldwide user-base. During this time, most of the
member databases have evolved to adopt new algorithms
and include new data. For example, eight of the nine profile
HMM-based member database now use the significantly
faster version of HMMER, version 3.0 (with only SMART
using HMMER 2.0). Meanwhile, for calculating sequence
matches to HAMAP within InterProScan, the two teams
developed a heuristic (using a profile HMM trained on the
same alignment used to produce the HAMAP profile and
used as pre-filter search), to ensure continued scalability.
As indicated in Table 1, 10 of the 14 member databases
(CDD, HAMAP, PANTHER, Pfam, PIRSF, Prosite Patterns,
Prosite Profiles, SFLD, SMART, TIGRFAM) have
been added or updated at least once over the past two
years, with CATH-Gene3D awaiting update from version
3.5 to 4.1 in InterPro, demonstrating a steady increase
of new data and sustained curation effort from InterPro’s
member databases. Two new member databases CDD and
SFLD further increase coverage and extend the number
of resources that annotate discrete functional amino acid
residues; this functionality is now available in InterPro for
the first time. We will continue to work with our expert
member databases, to provide more per-residue annotations,
e.g. HAMAP and PROSITE both define annotations
dependent on features composed of multiple noncontiguous
residues.
Nucleic Acids Research, 2017, Vol. 45, Database issue D197
Figure 4. Integration of MobiDB Lite annotation within InterPro, enabling annotation of intrinsic disordered (ID) regions within proteins. Top - InterPro
annotations for the Human mediator of RNA polymerase II transcription subunit 1 protein (UniProtKB accession Q15648). Middle - Zoomed in view
of the consensus long range ID predictions provided by MobiDB Lite. InterPro only captures the consensus output for each sequence, but the graphical
representations of the ID regions link to the source website, MobiDB (bottom), where the individual predictions can be viewed.
This additional tier of annotations is a step change
for InterPro: it adds a feature that has long been absent
from the resource, and, alongside the intrinsic disorder
predictions from MobiDB Lite (to complement
membrane-topology and coiled-coil prediction), enables the
most richly-detailed, informative annotation of protein sequences
possible. The integration of similar residue-level
annotations from databases like Pfam and PIRSF, coupled
with highly specific subfamily-level annotations from resources
like PANTHER and PRINTS, extends this functionality
even further. Together, these advances will help improve
the annotation of proteins in databases like UniProtKB,
by adding more discriminatory power to automated
annotation systems like UniRule. InterPro’s Web interfaces
will be expanded to present this additional layer of annotation
to users, ensuring both added value and continued
usefulness of the resource for the scientific community.
FUNDING
BBSRC [BB/L024136/1, BB/N00521X/1]; Wellcome Trust
[108433/Z/15/Z]; National Sciences Foundation [DBI
1458808]; Intramural Research Program of the National Library
of Medicine at National Institutes of Health/DHHS;
COST Action [BM1405]; European Molecular Biology
Laboratories core funds. Funding for open access charge:
Research Councils UK.
Conflict of interest statement. None declared.
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