The CATH domain structure database: new protocols
and classification levels give a more comprehensive
resource for exploring evolution
Lesley H. Greene, Tony E. Lewis, Sarah Addou, Alison Cuff*, Tim Dallman, Mark Dibley,
Oliver Redfern, Frances Pearl, Rekha Nambudiry, Adam Reid, Ian Sillitoe, Corin Yeats,
Janet M. Thornton1and Christine A. Orengo
Department of Biochemistry and Molecular Biology, University College London, Gower Street,
London WC1E 6BT, UK and1European Bioinformatics Institute, Hinxton Hall, Hinxton,
Cambridge CB 10 IRQ, UK
Received September 15, 2006; Revised October 23, 2006; Accepted October 24, 2006
We report the latest release (version 3.0) of the
CATH protein domain database (http://www.cathdb.
info). There has been a 20% increase in the
number of structural domains classified in CATH,
up to 86 151 domains. Release 3.0 comprises 1110
fold groups and 2147 homologous superfamilies.
To cope with the increases in diverse structural
homologues being determined by the structural
genomics initiatives, more sensitive methods have
been developed for identifying boundaries in multi-
domain proteins and for recognising homologues.
The CATH classification update is now being
driven by an integrated pipeline that links these
automated procedures with validation steps, that
have been made easier by the provision of infor-
mation rich web pages summarising comparison
scores and relevant links to external sites for each
domain being classified. An analysis of the pop-
ulation of domains in the CATH hierarchy and
several domain characteristics are presented for
version 3.0. We also report an update of the CATH
Dictionary of homologous structures (CATH-DHS)
which now contains multiple structural alignments,
consensus information and functional annotations
for 1459 well populated superfamilies in CATH.
CATH is directly linked to the Gene3D database
which is a projection of CATH structural data onto
?2 million sequences in completed genomes and
The numbers of new structures being deposited in the Protein
Data Bank (PDB) continues to grow at a considerable rate. In
addition, structures being targeted by world wide structural
genomics initiatives are more likely to be novel or only
very remotely related to domains previously classified in
CATH (1,2). Only 2% of structures currently solved by con-
ventional crystallography or NMR are likely to adopt novel
folds (see Figures 1 and 2). A higher proportion of new
folds are expected to be solved by structural genomics struc-
tures; indeed a recent study has already showed that to be the
case (1,2). Although the influx of more diverse structures and
subsequent analysis will inform our understanding of how
domains evolve, it has resulted in increasing lags between
the numbers of structures being deposited and classified in
CATH. In response to this situation we have significantly
improved our automated and manual protocols for domain
boundary assignment and homologue recognition.
Significant changes have been implemented in the CATH
classification protocol to achieve a more highly automated
system. A seamless flow of structures between the constituent
programs has been achieved by building a pipeline which
integrates web services for each major comparison stage in
the classification (see Figure 3). Secondly, completely auto-
matic decisions are now being made for new protein chains
with close relatives already assigned in the CATH database.
There are two situations that preclude the CATH update pro-
cess from being fully automated. We rely on expert manual
curation for particularly challenging protein domain bound-
ary assignments (DomChop stage) and also for classifications
of remote folds and homologues (HomCheck stage). These
two manual stages will remain an integral part of the system
*To whom correspondence should be addressed: Tel: +1 44 207 679 3890; Fax: +1 44 207 679 7193; Email: email@example.com
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
Lesley H. Greene, Department of Chemistry and Biochemistry, Old Dominion University, 4541 Hampton Boulevard Norfolk, VA 23529-0126, USA
? 2006 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Published online 29 November 2006 Nucleic Acids Research, 2007, Vol. 35, Database issueD291–D297
In this paper we report our ongoing development of the
automated procedures. These critical new features should
better enable CATH to keep pace with the PDB (3) and facili-
tate its development. Key statistics on the domain structure
populations and characteristics are also presented.
A REVISED CATH CLASSIFICATION
In order to provide information on the sequence diversity
between superfamily members, we have introduced additional
levels into the CATH hierarchy. The CATH hierarchal classi-
fication scheme now consists of nine levels. Class is derived
from secondary structure content and Architecture describes
the gross orientation of secondary structures, independent of
connectivity. The Topology level clusters structures into
fold groups according to their topological connections and
numbers of secondary structures. The Homologous superfami-
lies cluster proteins with highly similar structures, sequences
and/or functions (4,5). The new extension of the CATH clas-
sification system now includes five ‘SOLID’ sequence levels.
S, O, L, I further divides domains within the H-level using
multi-linkage clustering based on similarities in sequence
identity (35, 60, 95 and 100%) (see Table 1). The D-level
acts as a counter within the I-level and is appended to the clas-
sification hierarchy to ensure that every domain in CATH has
a unique CATHsolid identification code (see Table 1). Spe-
cific details on the nature of the SOLID-levels can be found
in the ‘General Information’ section of the CATH website,
http://www.cathdb.info. CATH only includes experimentally
determined protein structures with a 4 A˚resolution or better,
Figure 1. Annual decrease in the percentage of new structures classified in CATH which are observed to possess a novel fold. The raw data for years 1972–2005
was fit to a single exponential equation by nonlinear regression using Sigma Plot (SPSS, Version 9.0) and the fit is shown as a solid black line. The inset shows a
close-up of the raw data for new topologies over the years 1980–2005. For comparison, the numbers of structural domains solved each year and deposited in the
PDB and classified in CATH is depicted in the dashed line.
Figure 2. Annual proportion of protein structures deposited in the PDB which are classified in CATH, rejected or pending classification. The colour scheme
reflects different categories of PDB chains. Black: not accepted by the CATH criteria; Red: unprocessed chains; Dark green: cumulative count of all chains
processed in CATH release 2.6. Light green: cumulative count of all chains processed in CATH release 3.0.
D292Nucleic Acids Research, 2007, Vol. 35, Database issue
Figure 3. Flow diagram of the CATH classification pipeline. This schematic illustrates the processes involved in classifying newly determined structures in
CATH. The CATH update protocol workflow from new chain to assigned domain is split into two main processes; DomChop where chains are divided into
domains and HomCheck where domains are classified into homologous families. Grey boxes denote production of meta-data, red denotes algorithms, blue
denotes workflow decision, yellow denotes manual process. Definition of abbreviations and terms are as follows: NW, Needleman–Wunsch (23) sequence
alignment algorithm; HMM, hidden Markov model (11); ChopClose, program which determines domain boundaries based on sequence identity with domains in
CATH (Lewis T.E. et al. unpublished); DomChop, manual validation of domain boundary assignment; HomCheck, manual validation of homology assignment;
CATHEDRAL (4), structure comparison program.
Nucleic Acids Research, 2007, Vol. 35, Database issueD293
40 residues in length or longer and having 70% or more side
DOMAIN BOUNDARY ASSIGNMENTS
We have further improved our automated domain boundary
prediction method—CATHEDRAL (4). This is used to
search a newly determined multi-domain structure against a
library of representative structures from different fold groups
in the CATH database to recognise constituent domains.
CATHEDRAL performs an initial rapid secondary structure
comparison between structures using graph theory to identify
putative fold matches which are then more carefully aligned
using a slower, more accurate, dynamic programming
method. A new scoring scheme has been implemented
which combines information on the class of the domains
being compared, their sizes, similarity in the structural envi-
ronments and the number of equivalent residues. A support
vector machine is used to combine the different scores and
select the best fold match for each putative domain in a
new multi-domain protein.
Benchmarking against a set of 964 ‘difficult’ multi-domain
chains, whose 1593 constituent domains were remotely
related to folds in CATH (<35% sequence identity) and origi-
nated from 245 distinct fold groups and 462 superfamilies,
showed that 90% of domains within these chains could be
assigned to the correct fold group and for 78% of them, the
domains boundaries were within ±15 residues of boundaries
assigned by careful manual validation. Larger variations in
domain boundaries are often due to the fact that in many
families significant structural variation can occur during
evolution so that distant relatives vary considerably in size.
If no close relative has been classified in CATH, it is likely
that the only CATHEDRAL match will be to a relative
with significant structural embellishments thus making it
harder to determine the correct boundaries.
Since domain boundary assignment of remote homologues
is one of the most time consuming stages in the classification
we combine multiple information for each new structure on a
web page to guide manual curation. Pages display scores
from a range of algorithms which include structure based
methods: CATHEDRAL (4), SSAP (6,7), DETECTIVE (8),
PUU (9), DOMAK (10), sequence based methods such as
hidden Markov Models (HMMs) (11) and relevant literature.
These pages are now viewable for information on putative
boundaries for new multi-domain structures currently being
classified in CATH (e.g. http://www.cathdb.info/cgi-bin/
For protein chains which are closely related to chains that
are already chopped in CATH, an automated protocol
has been developed (ChopClose). ChopClose identifies any
previously chopped chains that have sufficiently high
sequence identity and overlap with the query chain. Using
SSAP (6), the query is aligned against each of these chains
in turn and in each case the domain boundaries are inherited
across the alignment. The process of inheriting the boundaries
often requires some adjustments to be made to account for
insertions, deletions or unresolved residues. If the inheritance
from one of the chains meets various criteria (SSAP score
>80, sequence identity >80%, RMSD <6.0 A˚, longest end
extension <10 residues etc) then the resulting boundaries
are used to chop the chain automatically—AutoChop. For
cases where ChopClose’s best result does not meet all the
criteria for automatic chopping, it is provided as support
information for a manual domain boundary assignment.
Refer to Figure 3 for the location of AutoChop/ChopClose
within the CATH update protocol.
NEW HOMOLOGUE RECOGNITION METHODS
We have assessed a number of HMM based protocols for
improving homologue recognition. A new protocol (Samosa),
exploiting models built using multiple structure alignments
to improve accuracy, gives some improvements in sensitivity
(4–5%). However, a protocol exploiting an 8-fold expanded
HMM library based on sequence relatives of structural dom-
ains, gives an increase of nearly 10% in sensitivity (12). In
addition, HMM–HMM-based approaches have been imple-
mented using the PRC protocol of Madera and co-workers
(http://supfam.org/PRC/). These allow recognition of extre-
by the structure comparison methods (discussed further
below). HMM based database scans developed for the CATH
For some very remotely related homologues, confidence in
an assignment can be improved by combining information
from multiple prediction methods. We have investigated the
benefits of using machine learning methods to do this auto-
matically. A neural network was trained using a dataset of
14 000 diverse homologues (<35% sequence identity) and
14 000 non-homologous pairs with data from different homo-
logue comparison methods including structure comparison
(CATHEDRAL, SSAP), sequence comparison
HMM), and information on functional similarity. The latter
was obtained by comparing EC classification codes between
close relatives of the distant homologues and using a seman-
tic similarity scoring scheme for comparing GO terms, based
on a method developed by Lord et al. (13). On a separate val-
idation set of 14 000 homologous pairs and 14 000 non-
homologous pairs 97% of the homologues can be recognised
at an error rate of <4%.
NEW UPDATE PROTOCOL
Previously, CATH data was generated using a group of inde-
pendent programs and flat files. Over the past two years we
have developed an update protocol for CATH that is driven
by a suite of programs with a central library and a Post-
greSQL database system. A classification pipeline has been
Table 1. CATH version 3.0 statistics
D294Nucleic Acids Research, 2007, Vol. 35, Database issue
established which links in a completely automated fashion the
different programs that analyse the sequences and structures
of both protein chains and domains. The CATH update pro-
tocol can essentially be divided into two parts, domain
boundary assignment and domain homology classification
(see Figure 3). The aim of the protocol is to minimise manual
assignment and provide as much support as possible when
manual validation is necessary.
Processing of both parts of the classification protocol are
similar, requiring related meta-data and the triggering of the
same automated algorithms. Methods include pairwise
sequence similarity comparisons and scans by other homo-
logue detection or fold recognition algorithms such as HMM-
scan and CATHEDRAL that provide data for either manual
or automated assignment. Many of the automated steps in
the protocol have been established as a web service and the
pipeline integrates both automated steps together with ’hold-
ing stages’ in which domains are held prior to processing
and await the completion of manual validation of predictions
Web pages to support manual validation
For each manual stage (domain boundary assignment—
DomChop and homologue recognition—HomCheck) in the
classification we have developed a suite of web pages bring-
ing together all available meta-data from prediction algo-
rithms (e.g. DBS, CATHEDRAL, HMMscan for DomChop,
CATHEDRAL, HMMscan, for HomCheck) and information
from the literature and from other family classifications
with relevant data (e.g. Pfam). For each protein or domain
shown on the pages, information on the statistical significance
of matches is presented. The web pages will shortly be made
viewable and will provide interim data on protein chains and
domains not fully classified in CATH for biologists interested
in any entries pending classification.
OVERVIEW OF THE CURRENT RELEASE
Assigning domain boundaries and relationships between pro-
tein structures is computationally challenging. Since the last
CATH release version (2.6), the number of domains in the
CATH database has increased by 20% in version 3.0 and
now totals 86 151. This is a more than 10-fold increase in
the number of domains classified in CATH since its creation.
Improvements in automation and also in the web based
resources used to aid manual validation, have allowed us to
increase the proportion of hard-to-classify structures pro-
cessed in CATH and this is reflected in a significant increase
in the proportion of new folds in the database—now more
than 1000. The detailed breakdown of numbers of domains
in the nine CATH levels is given in Table 1. We conducted
an analysis of the domains in version 3.0 and have derived
statistics for several fundamental features:
Percentage of new topologies
An analysis of the percentage of new folds arising since the
early 1970s to the present age is shown in Figure 1. The num-
bers of new folds has been decreasing over time with respect
to the number of new structures being deposited and it can be
seen that currently approximately 2% of new structures clas-
sified in CATH are observed to be novel folds. For compari-
son the number of domain structures solved over time is also
graphically represented in Figure 1.
Number of domains within a protein chain
Integral to the construction of the CATH database is designat-
ing domain boundaries. We conducted an analysis of the
number of chains versus number of domains in a chain. It
is interesting to note that 64% of all protein structures cur-
rently solved and classified in CATH are single domain
chains (data not shown). The next most prevalent are two
domain chains (27%) and following this we find that the num-
ber of chains containing three or more domains rapidly
decreases. The average size of the single domain chains is
159 residues in length.
The CATH Dictionary of Homologous Superfamilies
The CATH-DHS has also been recently updated. Data on
structural similarity and superfamily variability is presented
as a significant update to the Dictionary of Homologous
Superfamilies (DHS) web-resource (14). The DHS also pro-
vides functional annotations of domains within each H-level
(superfamily) in CATH v 2.5.1.
For each superfamily, pair-wise structural similarity scores
between relatives, measured by SSAP, are presented. The
DHS now contains 3307 multiple structural alignments for
are generated for all the relatives and also for subgroups of
structurally similar relatives and sequence similar relatives.
Alignments are performed using the residue-based CORA
algorithm (15) and presented both as CORAPLOTS (14) and
in the form of a 2DSEC diagram (16), alongside co-ordinate
data of the superposed structures in PDB format. Sequence
representations of the alignments are available to download
in FASTA format. In the CORAPLOT images of the multiple
alignment, residues in each domain are coloured according to
ligand binding and residue type. EquivSEC plots are also
shown that describe the variability in orientation and packing
between equivalent secondary structures (16).
To identify sequence relatives for CATH superfamilies,
sequences from UniProt (17) were scanned against HMMs
of all CATH domains (12). Homologous sequences were
identified as those hits with an E-value < 0.01 and a 60% res-
idue overlap with the CATH domain. This protocol recog-
nised over one million domain sequences in UniProt which
could be integrated in the CATH-DHS. The harvested
sequences in each superfamily were compared against other
relatives by BLAST (18) to determine the pair-wise sequence
identity, and then clustered at appropriate levels of sequence
identity (35 and 95%) using multi-linkage clustering.
Information and links to other functional databases ENZYME
(19), GO (Gene Ontology Consortium, 2000), KEGG (20),
COG (21), SWISSPROT (22) are also included by BLASTing
the sequences from each superfamily against sequences pro-
vided by these resources. Only 95% sequence identity hits,
with an 80% residue overlap which were used to annotate
Nucleic Acids Research, 2007, Vol. 35, Database issueD295
Recent analysis of structural and functional divergence in
highly populated CATH superfamilies (>5 structural relatives
with <35% sequence identity) has been undertaken using data
from the DHS. The 2DSEC algorithm was used to analyse
multiple structural alignments of families and identify highly
conserved structural cores and secondary structure embellish-
ments or decorations to the common core. In some large
superfamilies, extensive embellishments were observed out-
side the core, and although these secondary structure inser-
tions were frequently discontinuous in the protein chain,
they were often co-located in 3D space (16). In many
cases, manual inspection revealed that the embellishment
had aggregated to form a larger structural feature that was
modifying the active site of the domain or creating new sur-
faces for domain or protein interactions. Data collected in the
DHS clearly shows a relationship between structural diver-
gence within a superfamily, sequence divergence of this
superfamily amongst predicted domains in the genomes and
the number of distinct functional groups that can be identified
for the superfamily (see Figure 4).
LATERAL LINKS ACROSS THE CATH
HIERARCHY TO CAPTURE EVOLUTIONARY
DIVERGENCE AND EXPLORE THE
Our analysis of structural divergence in CATH superfamilies
(16) has revealed families where significant changes in the
structures had occurred, in some cases 5-fold differences in
the sizes of domains were identified and sometimes it was
apparent that the ‘folds’ of these very diverse relatives had
effectively changed. Therefore, in these superfamilies, more
than one fold group can be identified, effectively breaking
the hierarchical nature of the CATH classification which
implies that each relative within a C.A.T.H. homologous
superfamily should belong to the same C.A.T. fold group
In addition, an ‘all versus all’ HMM–HMM scan between
all superfamily representatives revealed several cases of
extremely remote homologues which had been classified
into separate superfamilies and yet match with significant
E-values. Structure comparison had failed to detect the rela-
tionship between these superfamilies because the structural
divergence of the relatives was so extreme, sometimes consti-
tuting a change in architecture as well as fold group. In these
cases homology was only suggested by the HMM-based
scans and then manually validated by considering functional
information and detailed evidence from literature. In order to
capture information on these distant homologies, links have
been created between the superfamilies both on our web
pages and in the CATH database. The data can now be
found as a link from the CATH homepage (http://www.
In the near future, we also plan to provide web pages pre-
senting cases of significant structural overlaps between super-
families or fold groups. For these cases we are not currently
able to find any additional evidence to support a distant
evolutionary relationship and these examples highlight the
recurrence of large structural motifs between some folds
and the existence of a structural continuum in some regions
of fold space.
ACCESSING CATH AND IMPROVEMENTS
TO THE SERVER
The CATH database can be accessed at http://www.cathdb.
info. The web interface may be browsed or alternatively
searched with PDB codes or CATH domain identifiers.
There is also a facility for keyword searches. With the version
3.0 release we now make the raw and processed data files
available which include for example CATH domain PDB
files, sequences, dssp files and they can be accessed through
the CATH database main page. The Gene3D resource can be
accessed through the CATH database or directly at http://
www.cathdb.info/Gene3D. The DHS can be accessed through
the CATH database or directly at http://www.cathdb.info/
We are grateful to Dr Janet Moloney, Dr Kanchan Phadwal,
Dr Azara Janmohamed and Ms Elisabeth Rideal for valuable
assistance with the domain boundary assignments and
classification of domains in CATH (version 3.0). We acknow-
and A. Cuff (MRC); M. Dibley (EU); T. Lewis (Wellcome
Trust); C. Yeats (Biosapiens under the EU Framework
Program 6); A. Reid and T. Dallman (BBSRC studentships);
O. Redfern (EPSRC studentship); S. Addou (studentship from
the Algerian government); R. Nambudiry (Argonne grant,
USA).FundingtopaytheOpenAccess publicationcharges for
this article was provided by EU.
Conflict of interest statement. None declared.
Figure 4. Relationship between sequence variability, structural variability
and functional diversity in CATH superfamilies. Structural variation in a
CATH superfamily as measured by the number of diverse structural
subgroups (SSAP score <80 between groups) is plotted against sequence
diversity as measured by the number of sequence diverse subfamilies in the
CATH-DHS (<35% sequence identity between groups). The colour of each
point reflects the number of functions identified in that superfamily using GO
as follows: white (0–25), yellow (26–50), red (51–100), maroon (101–200),
D296 Nucleic Acids Research, 2007, Vol. 35, Database issue
REFERENCES Download full-text
1. Todd,A.E., Marsden,R.L., Thornton,J.M. and Orengo,C.A. (2005)
Progress of structural genomics initiatives: an analysis of solved target
structures. J. Mol. Biol., 348, 1235–1260.
2. Chandonia,J.M. and Brenner,S.E. (2006) The impact of structural
genomics: expectations and outcomes. Science, 311, 347–351.
3. Berman,H.M., Westbrook,J., Feng,Z., Gilliland,G., Bhat,T.N.,
Weissig,H., Shindyalov,I.N. and Bourne,P.E. (2000) The Protein Data
Bank. Nucleic Acids Res., 28, 235–42.
4. Pearl,F.M., Bennett,C.F., Bray,J.E., Harrison,A.P., Martin,N.,
Shepherd,A., Sillitoe,I., Thornton,J. and Orengo,C.A. (2003) The
CATH database: an extended protein family resource for
structural and functional genomics. Nucleic Acids Res., 31, 452–455.
5. Pearl,F., Todd,A., Sillitoe,I., Dibley,M., Redfern,O., Lewis,T.,
Bennett,C., Marsden,R., Grant,A., Lee,D. et al. (2005) The CATH
domain structure database and related resources Gene3D and DHS
provide comprehensive domain family information for genome
analysis. Nucleic Acids Res., 33, 247–251.
6. Taylor,W.R. and Orengo,C.A. (1989) Protein structure alignment.
J. Mol. Biol., 208, 1–22.
7. Orengo,C.A. and Taylor,W.R. (1996) SSAP: sequential structure
alignment program for protein structure comparison. Methods
Enzymol., 266, 617–635.
8. Swindells,M.B. (1995) A procedure for detecting structural domains in
proteins. Protein Sci., 4, 103–12.
9. Holm,L. and Sander,C. (1994) Parser for protein folding units.
Proteins, 19, 256–268.
10. Siddiqui,A.S. and Barton,G.J. (1995) Continuous and discontinuous
domains: an algorithm for the automatic generation of reliable protein
domain definitions. Protein Sci., 4, 872–884.
11. Karplus,K., Barrett,C. and Hughey,R. (1998) Hidden Markov models
(HMMs) for detecting remote protein homologies. Bioinformatics, 14,
12. Sillitoe,I., Dibley,M., Bray,J., Addou,S. and Orengo,C. (2005)
Assessing strategies for improved superfamily recognition. Protein
Sci., 14, 1800–1810.
13. Lord,P.W., Stevens,R.D., Brass,A. and Goble,C.A. (2003) Semantic
similarity measures as tools for exploring the gene ontology. Pac.
Symp. Biocomput., 601–612.
14. Bray,J.E., Todd,A.E., Pearl,F.M., Thornton,J.M. and Orengo,C.A.
(2000) The CATH Dictionary of Homologous Superfamilies (DHS): a
consensus approach for identifying distant structural homologues.
Protein Eng., 13, 153–165.
15. Orengo,C.A. (1999) CORA—topological fingerprints for protein
structural families. Protein Sci., 8, 699–715.
16. Reeves,G.A., Dallman,T.J., Redfern,O.C., Akpor,A. and Orengo,C.A.
(2006) Structural diversity of domain superfamilies in the CATH
database. J. Mol. Biol., 360, 725–41.
17. Bairoch,A., Apweiler,R., Wu,C.H., Barker,W.C., Boeckmann,B.,
Ferro,S., Gasteiger,E., Huang,H., Lopez,R., Magrane,M. et al. (2005)
The Universal Protein Resource (UniProt). Nucleic Acids Res., 33,
18. Altschul,S.F., Gish,W., Miller,W., Myers,E.W. and Lipman,D.J.
(1990) Basic local alignment search tool. J. Mol. Biol., 215,
19. Bairoch,A. and Apweiler,R. (2000) The SWISS-PROT protein
sequence database and its supplement TrEMBL in 2000. Nucleic Acids
Res., 28, 45–8.
20. Kanehisa,M. and Goto,S. (2000) KEGG: kyoto encyclopedia
of genes and genomes. Nucleic Acids Res., 28, 27–30.
21. Tatusov,R.L., Koonin,E.V. and Lipman,D.J. (1997) A
genomic perspective on protein families. Science, 278,
22. Needleman,S.B. and Wunsch,C.D. (1970) A general method applicable
to the search for similarities in the amino acid sequence of two
proteins. J. Mol. Biol., 48, 443–53.
Nucleic Acids Research, 2007, Vol. 35, Database issueD297