In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists.

Abstract

Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new skills and new understanding of genome-scale studies in order to take advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies, expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select examples.

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Critical Reviews in Toxicology, 32(2):67–112 (2002)
1040-8444/02/$.50
© 2002 by CRC Press LLC
In Silico Approaches to Mechanistic and Predictive
Toxicology: An Introduction to Bioinformatics for
Toxicologists
Mark R. Fielden, Jason B. Matthews, Kirsten C. Fertuck, Robert G.
Halgren, and Tim R. Zacharewski
Department of Biochemistry and Molecular Biology, National Food Safety and Toxicology Center and Institute
for Environmental Toxicology, Michigan State University, East Lansing, MI, U.S.A
* Corresponding author: Tim Zacharewski, Ph.D, Department of Biochemistry & Molecular Biology, 223 Biochemistry Building,
Wilson Road, Michigan State University, East Lansing, MI 48824. Tel: 517-355-1607. Fax: 517-353-9334. E-mail: tzachare@msu.edu
ABSTRACT: Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and
application of computational approaches to model, predict, and explain biological function at the molecular level.
This information rich field requires new skills and new understanding of genome-scale studies in order to take
advantage of the rapidly increasing amount of sequence, expression, and structure information in public and private
databases. Toxicologists are poised to take advantage of the large public databases in an effort to decipher the
molecular basis of toxicity. With the advent of high-throughput sequencing and computational methodologies,
expressed sequences can be rapidly detected and quantitated in target tissues by database searching. Novel genes
can also be isolated in silico, while their function can be predicted and characterized by virtue of sequence
homology to other known proteins. Genomic DNA sequence data can be exploited to predict target genes and their
modes of regulation, as well as identify susceptible genotypes based on single nucleotide polymorphism data. In
addition, highly parallel gene expression profiling technologies will allow toxicologists to mine large databases
of gene expression data to discover molecular biomarkers and other diagnostic and prognostic genes or expression
profiles. This review serves to introduce to toxicologists the concepts of in silico biology most relevant to
mechanistic and predictive toxicology, while highlighting the applicability of in silico methods using select
examples.
KEY WORDS: in silico, bioinformatics, toxicogenomics, microarray, cluster analysis, expressed sequence tags,
SAGE, position weight matrix, ligand docking, molecular modeling, multiple sequence alignment, BLAST, single
nucleotide polymorphisms.
I. INTRODUCTION
Emerging challenges of managing and inter-
preting large amounts of complex biological data
have given rise to a quickly growing field of
computational biology, or bioinformatics. While
this field is not new, it is rapidly becoming indis-
pensable to life scientists as biology evolves to-
ward high throughput approaches to whole ge-
nome molecular analyses. Bioinformatics can be
defined as ‘a scientific discipline that encom-
passes all aspects of biological information acqui-
sition, processing, storage, distribution, analysis
and interpretation’.1 This interdisciplinary field
attempts to link the theory and application of
mathematics, biology, and computer science in
order to understand the significance and relation-
ships between quantitative and qualitative bio-
logical data at the cellular, biochemical, and mo-
lecular level. To this end, numerous public,
commercial, and proprietary software applications
and databases have been developed to allow re-
searchers to analyze physical and genetic data to
infer functionality in a variety of model organ-
isms, as well as humans. Computational, or in
silico, analyses lend substantial predictive power
in addition to traditional methods of experimental
biology. In silico analyses can provide alternative

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and efficient means to generate new hypotheses,
aid in designing appropriate experiments, and in
interpreting large amounts of information from
genome-scale studies. As a result, computational
biology has the potential to influence all levels of
biologically based research and increase the scope
and efficiency of both basic and applied science.
The field of toxicology is rapidly evolving in an
effort to assess the genetic, molecular, and bio-
chemical basis of toxicity for natural and anthro-
pogenic chemicals, as well as therapeutic agents.
Consequently, toxicologists must now adapt and
enable themselves with new tools to leverage the
increasing amount of biological information.
The purpose of this review is to introduce
the concepts behind contemporary computational
methods applicable to the study of toxicology.
The review focuses on those methods that take
advantage of the large databases of publicly avail-
able sequence, expression, and structure data. A
list of computational methods and public data-
bases mentioned in this review can be found in
Table 1, as well as a glossary of commonly used
terms in the appendix. This review does not
address knowledge-based systems that attempt
to predict ADMET properties in silico (absorp-
tion, distribution, metabolism, excretion, and
toxicity) based on physical and chemical de-
scriptors.2,3 Predicting ADMET properties are
desirable for lead candidate selection in high
throughput drug screening programs; however,
these methods are usually commercially or in-
ternally developed and rely on private databases
of experimental data, thus limiting their use to
commercial environments. Examples are also
used where available to illustrate the applicabil-
ity of in silico methods to a variety of toxicologi-
cal interests.
II. IDENTIFICATION OF TARGET
TISSUES
Xenobiotics can preferentially target specific
organs, tissues, or cell types due to their affinity
for target proteins, which are often expressed in
a tissue-specific manner or expressed at higher
levels in the target tissue. Targeting of specific
cell types can also occur by virtue of the lack of
expression of proteins involved in detoxifying
the foreign compound or by expression of pro-
teins that bioactivate the foreign compound.
Knowledge of the distribution, abundance, and
ontogeny of a target protein in the whole animal
can aid in the identification of susceptible tis-
sues and help characterize toxicity. The expres-
sion of target proteins and their mRNAs have
traditionally been examined experimentally us-
ing immunological, hybridization, or PCR-based
detection methods. However, with the advent of
expressed sequence tags (ESTs)4 in conjunction
with the increased availability of cDNA libraries
of diverse origin, it has become possible to quali-
tatively and quantitatively measure mRNAs of
target genes in a tissue and stage-specific man-
ner by searching and counting ESTs in public
databases.
A. Expressed Sequence Tags (ESTs)
ESTs are partial DNA sequences of cDNA
clone inserts, which were originally used to rap-
idly identify expressed genes and expedite gene
discovery and genome mapping.4 ESTs are gen-
erated by first constructing a cDNA library and
sequencing each derived clone in a single pass
on the 5′ or 3′ end of the cDNA insert. The
anonymous sequence reads are then compared,
typically using the BLAST suite of programs, to
large sequence databases to establish a putative
clone identity (Figure 1).
When the intent is to discover novel genes
or a representative set of genes from a cDNA
library, it is preferable to reduce the redun-
dancy of the clone set. A cDNA library can
be normalized in such a way as to decrease
the relative abundance of very highly ex-
pressed mRNAs and increase the abundance
of rare mRNAs.5, 6 Libraries can also be gen-
erated by subtractive hybridization, which
results in an enrichment of cDNAs that are
differentially expressed between two mRNA
populations, or a combination of subtraction
and normalization techniques.7-9 While nor-
malized or subtracted cDNA libraries are use-
ful for discovering novel or rare transcripts,
they are unsuitable for estimating relative
transcript abundance between two mRNA
populations.

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TABLE 1
Summary of Publicly Available Databases and Tools for In Silico Toxicology

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TABLE 1 (continued)

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FIGURE 1. Construction of cDNA libraries and generation of expressed sequence tags
(ESTs) for gene identification. A mRNA sample from a tissue or cell line is converted into
double-stranded cDNA, cloned into a vector and transformed in bacterial host. Individual
clones are sequenced on the 5’ or 3’ end of the insert. Hundreds to thousands of single
pass sequence reads from a library can be grouped into clusters to reduce the redun-
dancy of the sequences. Each cluster represents a unique expressed transcript. The
number of ESTs within a gene cluster, relative to the total number of ESTs sequenced
from its respective cDNA library, represents the relative level of expression for that gene
in the sample.

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B. EST Clustering
Public EST sequence data are curated in the
Database of Expressed Sequence Tags (dbEST)10
(Table 1), which is maintained by NCBI, the
National Center for Biotechnology Information.
The Institute of Genome Research (TIGR) also
maintains a database, termed the Gene Index, of
publicly available EST sequence data from dbEST
and from EST sequence data produced by TIGR11
(Table 1). Both NCBI and TIGR cluster EST
sequences within species, such that subsets of
EST sequences that belong together, based on
overlapping sequence, are grouped into a larger
set, where each set theoretically represents a unique
expressed gene from that species (Figures 1 and 2).
Such sets are represented by clusters in the Unigene
NCBI database12 and consensus sequences in the
TIGR Gene Index11 (Figure 2). The advantage of
the TIGR Gene Index is the conservative assem-
bly of the EST sequences within each cluster to
form a consensus sequence. Unigene does not
attempt to assemble the ESTs within each cluster
to produce a consensus sequence. As a result it is
possible to find several TIGR consensus sequences
contained within one UniGene cluster. These
multiple consensus sequences can represent dif-
ferent splice variants of the same gene. The meth-
ods used to cluster or assemble the sequences
within Unigene (http://www.ncbi.nlm.nih.gov/
UniGene/build.html) and the Gene Indices13 have
been described previously in detail. While both
methods ‘clean’ the EST data by removing vector
contaminants, repeats, and low-complexity se-
quence, the quality of the data relies heavily on
the quality of the original sequence data. Both
databases allow query searches based on acces-
sion number or other unique identifier, gene name,
tissue, and nucleotide or protein sequence simi-
larity using BLAST. TIGR currently maintains
Gene Indices for nine organisms, including hu-
man, mouse, rat, Drosophila, and zebrafish, as
well as plant and microbial species. Unigene cur-
rently clusters EST sequence data for human,
mouse, rat, bovine, and zebrafish. Unigene clus-
ters are updated frequently based on new sequence
data; however, there is instability in the cluster
assignments between revisions of the database as
a result of the clustering algorithm used. There-
fore, when the clustering process is repeated, ESTs
may be assigned to a different cluster such that
clusters with lone ESTs can be orphaned and
subsequently retired. GenBank accession num-
bers for each EST should be used, rather than
cluster IDs, when tracking the identity of clones.
TIGR has a more stable identification system due
to the more conservative assembly algorithm, but
this database is updated less frequently and may
not contain the most recent EST sequence sub-
missions.
In addition to grouping EST sequences into
unique clusters, TIGR and Unigene also provide
annotation for each cluster or consensus sequence,
including a putative gene name. The identity and
corresponding annotation of each EST sequence
is derived by comparing the EST sequence against
known sequences already in a database. The an-
notation for each EST is tentative because it is
based on the limited sequence information avail-
able for each cDNA clone and is susceptible to
sequencing errors and errors based on the propa-
gation of dubious annotations from the records of
other homologs. Many ESTs are termed single-
tons, because they do not bear a sufficient se-
quence identity to any other sequence in the data-
base and represent the only member of a cluster.
Most ESTs are of unknown identity, having vari-
able degrees of similarity to other known genes in
the database.
C. Serial Analysis of Gene Expression
SAGE (Serial Analysis of Gene Expression)
is a high-throughput sequencing-based method
for the generation of expression data.14 Like EST
sequencing, this method provides a rapid means
to identify and quantitate the number of tran-
scripts in a mRNA population. The SAGE method
is diagrammed in Figure 3. The short (9 to 12
base) tag sequence produced by the SAGE method
theoretically provides enough information to
uniquely identify a transcript, while quantitating
the number of times a tag is observed is propor-
tional to the expression level of the corresponding
transcript. Like EST data, this method sacrifices
accuracy in the assignment of tags to a gene, and
the ability to quantitate gene expression in return
for high throughput. Furthermore, a 9 to 14 base
tag is not always sufficient to uniquely identify a

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FIGURE 2. Example of a partial output of a TIGR Tentative Human Consensus (THC) sequence THC532369 (androgen receptor). (A) Sequence assembly
of full-length cDNAs, protein translations, and ESTs from various public sources. Arrow indicates the direction of sequence in relation to the consensus
sequence. The sequence was obtained from GenBank and other DNA and protein databanks. The THC 532369 represents the consensus sequence derived
from the overlapping partial sequences. (B) Listed for each THC are sequence source (Src; e.g., GenBank), unique identifier (EST Id), GenBank accession
(GB#), IMAGE clone ID (clone; another unique identifier), left and right position of the sequence within the THC, and the library mRNA source. Not all sequence
annotations are shown for the partial sequences in A. DNA sequence, BLAST search results, tentative orthologous sequences for mouse and rat, and mapping
data also available but not shown here. (C) Expression report details for the libraries from which each EST within THC532369 was derived, the number of times
it was sequenced from that library, and a link to the library record (Cat#). Horizontal bars represent the level of expression in each library.

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FIGURE 3. Schematic of the serial analysis of gene expression (SAGE) method. PolyA RNA is transcribed into
double-stranded cDNA with a biotinylated oligo(dT) primer and cleaved with an anchoring enzyme (AE) that cleaves
each transcript at least once every 256 bp (44). The 3’ portion of the cDNA is then bound to streptavidin beads, thus
providing a unique site (i.e., the AE site) on each transcript closest to the polyA tail. The cDNA pool is then divided
in half and ligated via the anchoring enzyme restriction site to one of two linkers containing a type IIS restriction site
(TE) and a unique primer site (A or B). The type IIS restriction enzyme blunt end cleaves at a defined distance up
to 20 bp away from the recognition site. Blunt ended cleavage by the tagging enzyme produces a short piece of
cDNA containing the tagging and anchoring enzyme site, the primer site, and the cDNA tag 9 to 14 bases long,
depending on the tagging enzyme used. The blunt ended products are pooled and ligated to each other to produce
ditags flanked by the linkers. Ligated tags then serve as a template for PCR amplification with primers specific to
each linker. This amplification step serves to amplify tag sequences and provide orientation. The resulting ampli-
fication products are cleaved with the anchoring enzyme allowing isolation of the ditags, which are then concat-
enated by ligation, cloned, and sequenced. Software identifies individual tags, makes tag to gene assignments, and
quantitates the tags to generate expression levels for the genes.

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gene because one or more genes may share the
same tag, and one gene may be represented by
more than one tag. Sequencing errors can also
compound the problem of tag to gene assign-
ments.
The absence of an EST or SAGE tag from a
cDNA library does not necessarily indicate that
the gene is not expressed in that tissue. This is
often the case when only a small fraction of the
clones from a cDNA library are sequenced, or the
library was normalized or subtracted. Nonethe-
less, due to the large size and complexity of the
EST and SAGE databases, and the comprehen-
sive representation of many tissues, it is possible
to discover genes that are specifically or preferen-
tially expressed in a particular tissue by compar-
ing EST or tag counts between libraries.
D. Other Expression Resources
In addition to EST and SAGE data, gene
expression information for the mouse is also avail-
able from the Jackson Laboratory Gene Expres-
sion Database (GXD)15 (Table 1). This resource
curates gene expression information, including
Northern Blot, RNase protection, Western Blot,
and reverse transcriptase-polymerase chain reac-
tion (RT-PCR) assays from mouse tissues ob-
tained from various stages of development. These
data are collected from a number of sources, in-
cluding direct online submission and annotations
from the literature by the curators of the database.
This resource allows complex queries for genes
based on accession number, tissue of interest, or
developmental stage.
E. In Silico Northern Blots
Determining whether a gene is expressed in a
particular tissue involves querying the Unigene
database or the TIGR Gene Index for the gene of
interest after selecting an organism to search. The
output returns expression information as a list of
the cDNA library sources from which the EST or
tag was derived. The Unigene output also in-
cludes a link to SAGE results if available, which
not only provides a list of cDNA libraries con-
taining a tag representative of the queried gene,
but also a computer-generated visual indication
of the relative abundance of the tag in the cDNA
library. The visual indicator resembles a Northern
blot and is referred to as a virtual Northern.
Automated comparison of EST counts (digi-
tal differential display, or DDD) was first de-
scribed for the discovery of prostate-specific genes,
in which genes were identified in dbEST that
have many related ESTs from a prostate cDNA
library, but have none or few ESTs in nonprostate
libraries.16 A potential application is to identify
tissue-specific proteins for targeted therapy of
prostate cancer, but the approach is generally
applicable to finding tissue-specific targets for
other forms of therapy. When 7 of 15 putative
prostate-specific genes identified in silico were
examined experimentally for their tissue distribu-
tion, only three were found to be prostate specific.
This indicates that cDNA libraries are generally
incomplete and many more expressed sequences
exist that have not been detected by EST sequenc-
ing. DDD of human cDNA libraries is available
through the NCBI Unigene DDD (Table 1).
Unigene clusters can be identified that are specifi-
cally or preferentially expressed within a cDNA
library, or pool of libraries. The output returns a
list of clusters, their relative expression level in
the cDNA library or pool of libraries being com-
pared, as well as significance level associated
with their relative difference. The statistical sig-
nificance of digital gene expression profiles based
on tag frequency data has also been addressed by
Audic and Claverie.17 In this manner, gene ex-
pression can be examined across many organs or
between normal and diseased cells.
SAGE can be used to quantitatively catalog
expressed sequences and monitor differential gene
expression between two mRNA populations. The
virtual northern tool, SAGEMap vNortherns, al-
lows an mRNA sequence to be queried for ex-
pression in SAGE libraries represented in
SAGEMap, a database of SAGE results18 (Table 1).
Representative tags are extracted from the query
sequence and links are provided to SAGE librar-
ies containing a representative of the tag. Follow-
ing the tag hotlinks provides a list of the SAGE
libraries containing the tag, as well as a virtual
northern blot representing the relative level of
expression of the tag in the SAGE library. Pool-
ing and comparing different SAGE libraries can

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be performed with SAGEMap xProfiler, a tag-
based method of DDD (Table 1). Using this tool,
SAGE libraries can be pooled based on character-
istics, such as normal, diseased, or tissue type,
and compared with another pool to identify tran-
scripts that are differentially expressed between
two pools. Fold difference factors and coefficient
of variance cutoffs can be used to prioritize genes
differentially expressed. The results, including
library information, tag sequences, tag counts and
statistical p-values, can be downloaded in text
format.
In a similar manner to DDD, EST frequencies
can be experimentally compared between two
cDNA libraries from control and test samples to
measure differential gene expression following
chemical exposure. This approach has been used
to identify changes in gene expression in mouse
liver following exposure to dioxin.19 Based on the
frequency of almost 5000 ESTs derived from male
mice treated with dioxin or vehicle, the overall
inventory of transcripts was altered by approxi-
mately 30%. In addition, 20 genes were identified
to be significantly up- or down-regulated in re-
sponse to dioxin treatment. One advantage of this
approach is the generation of a set of cDNA clones
from mouse liver. These clones were then used to
construct tissue-specific cDNA microarrays to
measure gene expression and verify the EST fre-
quency results.
F. ESTs and Microarrays
ESTs also provide the raw material from which
to construct cDNA microarrays. As an initial step,
EST data can be analyzed for tissue distribution
to develop target-specific cDNA microarrays. This
approach attempts to maximize the amount of
useful expression information extracted from ar-
ray experiments and increase the probability of
identifying changes in gene expression (see be-
low for an introduction to microarray analysis).
This approach is desirable because a large propor-
tion of genes represented on a microarray may not
be expressed or detected in the tissue of study.
For example, it was shown that a large proportion
of differentially expressed genes in a melanoma
cell line were not identified when analyzed using
microarrays that were constructed from libraries
without origin bias, in contrast to microarrays that
were constructed with ESTs from a melanocyte-
derived library.20
The Database of Transcripts Expressed in
Spermatogenesis and in Testis (dbTEST) is an
example of how public expression information
can be used to develop tissue-specific cDNA
microarrays to study testicular gene expression in
the mouse (http://www.bch.msu.edu/~zacharet/
dbTestExp.html). Entries in dbTEST are derived
from a number of sources, including the literature
and the mouse GXD. The majority of the entries,
however, are derived from information contained
in the dbEST. This takes advantage of the fact
that EST sequence records are annotated with
their library of origin. To compile dbTEST, the
UniGene database was queried for a list of all
clusters that contained at least one EST sequence
derived from a mouse testis cDNA library. These
testis libraries cover many stages of development,
including pre- and postnatal stages. The use of
public EST expression information has an advan-
tage in that cDNA clones representative of each
cluster are likely to be publicly available through
licensed distributors of IMAGE (Integrated Mo-
lecular Analysis of Genomes and their Expres-
sion) Consortium cDNA clones.21 This strategy
rapidly identifies a large pool of potential target
genes, while concurrently identifying readily avail-
able cDNA clones for these genes.
Several assumptions are inherent in the above
approach. The first, and most important, assump-
tion is that a gene can be considered to be ex-
pressed in a tissue if a single EST with similarity
to that gene is identified within that tissue. This
assumption is overly simplistic and likely results
in the false identification of many genes due to
erroneous clustering of tags. The utility of tag
detection and counting methods also assumes that
the database is representative of the true abun-
dance and distribution of tags. This assumption is
certainly false, because the results of tag detec-
tion and quantitation methods are biased toward
the libraries that are within the database. The
libraries do not comprehensively represent all tis-
sues or developmental stages, as most libraries
are derived from normal and untreated control
animals. Therefore, it is possible that genes ex-
pressed only under pathological conditions, or
only following exposure to an inducing agent, are

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underrepresented. However, tag databases such
as dbEST and SAGEMap continue to grow in
size, thus improving the utility of these methods.
It is likely that with the increase in use of
microarray technology for gene expression pro-
filing, microarray databases will provide a more
valuable source of expression data under a wider
variety of experimental conditions.
III. IDENTIFICATION OF TARGET GENES
DNA or protein sequence data can be used to
identify other, potentially novel, proteins of inter-
est, such as other protein family members, or to
identify related proteins in other species as a means
of establishing an in vivo model system for study.
While these tasks would have been exceedingly
difficult if not impossible to address a decade ago,
the rapid increase in the availability of DNA and
protein sequence data and the development of
tools to mine these data now allow for in silico
identification and characterization of target pro-
teins based on sequence data alone. These ap-
proaches can expedite the cloning of toxicologi-
cally relevant genes and aid in the interpretation
and prediction of protein function and dysfunc-
tion.
A. Terminology
Comparisons between homologous sequences
provide a powerful means for predicting protein
function based on sequence data alone. However,
before considering the structural or evolutionary
comparison of proteins within or between spe-
cies, it is appropriate to first clarify protein termi-
nology that is often misused or misunderstood.
Two proteins that exhibit high amino acid se-
quence or structural similarity are often said to be
homologous, which implies a common evolution-
ary origin. There is no strict rule that determines
what degree of similarity between two protein
sequences implies homology, because protein se-
quences evolve at different rates. The degree of
similarity is often expressed as a percentage of
sequence similarity or identity that are often mis-
taken for each other. Percent sequence similarity
refers to the percentage of the amino acid pairs in
a sequence alignment that are of similar chemical
property, such as glutamate and aspartate (i.e.,
both acidic). Percent sequence identity refers to
the percentage of amino acid pairs in a sequence
alignment that are identical. Therefore, sequence
similarity is always greater than sequence iden-
tity. Homologous proteins can be considered ei-
ther paralogs or orthologs. Paralogs refer to ho-
mologous proteins within a species that diverged
as a result of an early gene duplication event.
Although similar in amino acid sequence and pro-
tein structure, they often have unique but related
functions and are often members of the same
protein family. Orthologs refer to homologous
proteins in different species that arose from a
common ancestral gene during speciation. As a
result, they usually serve the same function in
both organisms and can often, but not always,
genetically complement each other. When com-
paring two related protein sequences between
species, the distinction of whether a protein of
similar sequence is a true ortholog is often un-
clear. When dealing with incompletely sequenced
genomes, there exists the possibility that the true
ortholog (i.e., most similar protein) has not yet
been sequenced, and that the most similar se-
quence is just a paralog that fulfills a related but
distinct function.22 A reciprocal sequence com-
parison will quickly reveal if the most similar
sequence (i.e., the putative ortholog) is also most
similar to the query sequence in the original spe-
cies.
B. Pairwise Sequence Alignments
Aligning novel sequences with previously
characterized proteins affords an efficient and
powerful means to extract functional information
from unknown proteins based on the evolutionary
conservation of protein function. Sequences can
be compared by local or global alignments, de-
pending on the purpose of the comparison. The
Needleman-Wunsch algorithm was first described
for global sequence alignment in which an opti-
mal forced alignment between two sequences is
determined.23 The Smith-Waterman algorithm
finds the optimal local alignment within two se-
quences.24 The method of choice will depend on
whether the two sequences are presumed to be

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related over their entire length or only within
related domains within the sequence. The Smith-
Waterman algorithm is the preferred approach
because similarities between sequences are usu-
ally restricted to relatively short segments, such
as motifs or active sites, rather than the entire
length. Searching for sequence similarity between
a query sequence and a large number of other
sequences in a database using the Smith-Waterman
approach is computationally intensive and time
consuming. Therefore, a heuristic strategy, which
makes use of approximations, has been utilized in
the FASTA25 and BLAST26 programs (Table 1) to
increase computational speed at the cost of sensi-
tivity in detecting relevant matches. However,
adjusting the parameters of the search can influ-
ence the heuristics and alter the balance between
speed and sensitivity.
When trying to identify homologous proteins,
aligning amino acid sequence is preferred over
nucleotide sequences due to the degenerate nature
of the amino acid code. Both the FASTA and
BLAST suite of programs support the virtual trans-
lation of DNA sequences such that a DNA query
sequence can be compared to a protein database
or a DNA database that has also been virtually
translated in all six reading frames. Although
FASTA supports this feature, we will concentrate
on the BLAST suite of programs due to its popu-
larity and speed. The choice of BLAST program
will depend on the nature of the query sequence,
the database being searched, and the purpose of
the search. Table 2 outlines the family of BLAST
programs available and when they should be used.
To search for weaker, but perhaps biologically
relevant, sequence similarities, a position-specific
iterative form of BLAST (PSI-BLAST) has been
introduced27 (Table 1). This method produces a
position-specific scoring matrix (or profile) from
statistically significant matches produced by the
BLAST program. A position-specific scoring
matrix describes the position and frequency of
amino acids in a sequence alignment. This matrix
is then used as a query to search a protein data-
base for matches in an iterative fashion until no
new homologous sequences have been found.
While not as sensitive as the best motif search
programs (see Protein Domain Classification be-
low for more detail on motif searches), its speed
and ease of use makes this program attractive.
Another advantage of this program is its ability to
detect distant but biologically relevant relation-
ships between proteins that were once only de-
tectable by structural comparisons.28,29 It is likely
of limited practical use for most sequence com-
parisons when the intent is to find homologs in
closely related species or to predict function based
on domain analysis. While the source of errors in
PSI-BLAST is the same as BLAST, they are eas-
ily amplified during the iteration process. For
example, deceptive alignments can easily be pro-
duced if proteins with highly biased amino acid
composition are included in the profile.30
C. Evaluating Sequence Alignments
To judge the effectiveness of a sequence align-
ment, an alignment score between two sequences
is calculated based on residue similarity and the
presence of gaps. A substitution score is given to
each pair of residues that can be aligned. A com-
plete set of these substitution scores is called a
substitution matrix. PAM31 and BLOSUM32 are
the most widely used scoring matrices for protein
sequence comparison. A scoring matrix is con-
structed from target frequencies, which represent
the observed frequency of point mutations within
related proteins that have been accepted during
evolution because they do not seriously disrupt
the function of the protein. When calculating a
substitution score, a conservative substitution, such
as a serine for a tyrosine (both polar), is given a
higher score than a nonconservative substitution,
such as a serine for a valine (polar to a nonpolar).
The substitution scores in the matrix are propor-
tional to the natural log of the ratio of target
frequencies to background frequencies, which are
dictated by the frequency of the different amino
acids in nature. To estimate target frequencies in
a PAM matrix, a set of very closely related se-
quences were used to collect mutation frequen-
cies corresponding to 1 PAM (Point Accepted
Mutation), where 1 PAM represents a unit of
evolutionary divergence in which 1% of the amino
acids have been changed. Target frequencies are
then extrapolated to a distance of 250 PAMs.
Note that 100 PAMs does not mean that 100% of
the amino acids have changed, as the amino acids
can change many times, as well as changing back

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TABLE 2
Sequence Searches Using the BLAST Family of Programs

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81
to the original amino acid. When comparing se-
quences from evolutionarily divergent species
where the sequence similarity is expected to be
weak, such as between mouse and yeast, best
results are obtained when using higher PAM val-
ues, such as PAM200 or PAM250. When com-
paring sequences from related species where se-
quence similarity is expected to be strong, such as
between human and rodents, a lower PAM value
is appropriate, such as PAM30.33
The BLOSUM substitution matrix uses a dif-
ferent strategy to estimate target frequencies. Fre-
quencies are derived from local multiple sequence
alignments of distantly related sequences. The
advantage of this approach is the empirical deri-
vation of matrix scores, rather than by extrapola-
tion. The BLAST program uses a BLOSUM-62
matrix as the default scoring matrix. Sequences in
the multiple sequence alignment used in construc-
tion of the matrix having at least 62% identity are
merged into a single sequence such that the target
frequencies are influenced more by the divergent
sequences in the alignment. As a result, BLOSUM
matrices with high cutoffs, such as BLOSUM-90,
are more appropriate for comparing closely re-
lated sequences. The BLOSUM-62 matrix is con-
sidered efficient over a broad range of evolution-
ary distances and is suitable for query lengths
greater than 85 amino acids. Because short se-
quence alignments need to be strong to rise above
background, higher BLOSUM matrices or lower
PAM matrices are recommended. In addition to
substitution scores, gaps can contribute negatively
to the score of an alignment. While no theory
exists for imposing or selecting gap costs, it is
common to impose an initial fixed cost (G) for
each gap and an additional gap penalty (L) pro-
portional to the length of the gap. For practical
purposes, the default settings of BLAST suffice.
The biological relevance of a match can only be
ascertained experimentally; however, local align-
ment statistics can indicate which alignments are
unlikely to have arisen by chance alone. While theory
does not exist to describe the expected distribution
of global alignments, the distribution scores for op-
timal local ungapped alignments has been shown to
follow an extreme value distribution.34 Although
unproven, evidence suggests the same distribution
holds for gapped alignments,35 which is supported
by the current version of BLAST. BLAST programs
calculate a raw score (denoted S) for alignments
based on the substitution scoring matrix and gap
penalties imposed. Bit scores, which represent raw
scores normalized for the statistical variables that
define a given scoring system, are reported for all
alignments. Bit scores are more informative and
allow for accurate comparisons between different
alignments, even when using different scoring ma-
trices. To calculate the significance of the align-
ment, the observed alignment score (S) is compared
with the expected extreme value distribution to gen-
erate an E (expect) value. An E value represents the
number of distinct alignments with an equivalent or
superior alignment score (S) than would be ex-
pected to occur purely by chance alone. From the E
value, a P value can be calculated. E values less than
0.01 are essentially identical to P values and gener-
ally considered significant, or at least of interest.
Local alignments with no gaps are referred to as
High Scoring Pairs (HSPs). To calculate the signifi-
cance of the ungapped alignment, the observed align-
ment score for the HSP is compared with the Pois-
son distribution, which describes the number of
random HSP scores equal to or greater than S. This
is the P value associated with S and describes the
probability of finding an alignment with an equiva-
lent or superior alignment score simply by chance
alone. The probability of randomly generating an
alignment is in part governed by the length of the
query sequence and the size of the database searched.
While there is no strict rule for judging significance,
a comparative analysis of sufficiently divergent
human and mouse orthologs reveals that coding
sequences as low as 36% identical at the protein
level result in highly significant E values less than 2
× 10–33 (BLOSUM-62, BLASTP) or less than 6 ×
10–58 at the DNA level (BLASTN).36 Ultimately, the
decision as to whether a match is significant will
depend on the species being compared, the query
sequence, the database searched and an informed
decision by the investigator to judge whether the
alignment is of biological relevance.
D. Identifying Novel and Orthologous
Genes
Database similarity searching identifies which
sequences in a database of hundreds of thousands
are most similar to the particular sequence of