2 T
`
`he Kabat Database and a Bioinformatics Example
`
`George Johnson and Tai Te Wu
`
`1. Introduction
`In 1969, Elvin A. Kabat of Columbia University College of Physicians and
`Surgeons and Tai Te Wu of Cornell University Medical College began to col-
`lect and align amino acid sequences of human and mouse Bence Jones proteins
`and immunoglobulin (Ig) light chains. This was the beginning of the Kabat
`Database. They used a simple mathematical formula to calculate the various
`amino acid substitutions at each position and predict the precise locations of
`segments of the light-chain variable region that would form the antibody-com-
`bining site from a variability plot (1). The Kabat Database is one of the oldest
`biological sequence databases, and for many years was the only sequence data-
`base with alignment information.
`The Kabat Database was available in book form free to the scientific com-
`munity starting in 1976 (2), with an updated second edition released in 1979
`(3), third edition in 1983 (4), fourth edition in 1987 (5), and fifth printed edi-
`tion in 1991 (6). Because of the inclusion of amino acid as well as nucleotide
`sequences of antibodies, T-cell receptors for antigens (TCR), major histocom-
`patibility complex (MHC) class I and II molecules, and other related proteins
`of immunological interest, it became impossible to provide printed versions
`after 1991. In that same year, George Johnson of Northwestern University cre-
`ated a website to electronically distribute the database located temporarily at:
`
`http://kabatdatabase.com
`
`During the following decade, the Kabat Database had grown more than five
`times. Thanks to the generous financial support from the National Institutes of
`Health, access to this website had been free for both academic and commercial use.
`With the completion of the human genome project as well as several other
`genome projects, scientific emphasis has gradually shifted from determining
`
`From: Methods in Molecular Biology, Vol. 248: Antibody Engineering: Methods and Protocols
`Edited by: B. K. C. Lo © Humana Press Inc., Totowa, NJ
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`more sequences to analyzing the information content of the existing sequence
`data. With regard to the Kabat Database, the collection and alignment of amino
`acid and nucleotide sequences of proteins of immunological interest has been
`progressing side-by-side with the ability to determine structure and function
`information from these sequences, from its very start.
`
`1.1. Historical Analysis and Use
`After the pioneering work of Hilschmann and Craig (7) on the sequencing of
`three human Bence Jones proteins, many research groups joined the effort of
`determining Ig light chain amino acid sequences. By 1970, there were 77 pub-
`lished complete or partial Ig light chain sequences: 24 human κ-I, 4 human κ-
`II, 17 human κ-III, 10 human λ-I, 2 human λ-II, 6 human λ-III, 5 human λ-IV,
`2 human λ-V, 2 mouse κ-I, and 5 mouse κ-II proteins (1). The invariant Cys
`residues were aligned at positions 23 and 88, the invariant Trp residue posi-
`tioned at 35, and the two invariant Gly residues at positions 99 and 101. To
`align the variable region of kappa and lambda light chains, single-residue gaps
`were placed at positions 10 and 106A. Longer gaps were introduced between
`positions 27 and 28 (27A, 27B, 27C, 27D, 27E, and 27F) and between 97 and
`98 (97A and 97B), which was later changed to between 95 and 96 (95A, 95B,
`95C, 95D, 95E and 95F). A similar alignment technique with a different num-
`bering system was introduced for the Ig heavy-chain variable regions (8). The
`invariant Cys residues were located at positions 22 and 92, the Trp residue at
`position 36, and the two invariant Gly residues at positions 104 and 106.
`The most important discovery to come from alignment of the Ig heavy- and
`light-chain sequences was the location of segments forming the antibody-com-
`bining site, known as the complementarity (initially called hypervariable)-
`determining regions (CDRs). Since different antibodies bind different antigens,
`numerous amino acid substitutions occur in these segments, leading to large,
`calculated variability values. The first variability plot of the 77 complete and
`partial amino acid sequences of human and mouse light chains showed three
`distinct peaks of variability, located between positions 24 to 34, 50 to 56, and
`89 to 97 (1). Three similar peaks were discovered in heavy chains at positions
`31 to 35, 50 to 65, and 95 to 102. These six short segments were hypothesized
`to form the antigen-binding site and were designated as CDRL1, CDRL2,
`CDRL3 for light chains, and CDRH1, CDRH2, and CDRH3 for heavy chains,
`respectively.
`Initial Ig three-dimensional (3D) X-ray diffraction experiments suggested
`that the six binding-site segments were indeed physically located on one side of
`the Ig macromolecule. Final verification of this theoretical prediction came
`after the development of hybridoma technology (9). An anti-lysozyme mono-
`clonal antibody Fab fragment was co-crystallized with lysozyme (10), and the
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`combined 3D structure was determined by X-ray diffraction analysis. Several
`amino acid residues in each of the six CDRs of the antibody were found to be
`in direct contact with the antigen. As theoretically predicted, antibody speci-
`ficity thus resided exclusively in the CDRs. During the past decade, designer
`antibodies have been constructed genetically by selecting these CDRs for their
`affinity for the target antigen.
`By comparing the amino acid sequences of the CDRs as well the stretches of
`sequence that connect them, known as framework regions (FR), Kabat and Wu
`hypothesized that the Ig variable regions were assembled from short genetic
`segments (11,12). This hypothesis was verified experimentally by Bernard et
`al. (13) with the discovery of the J-minigenes, reminiscent of the switch pep-
`tide proposed by Milstein (14). The D-minigenes were soon identified as
`another component of the heavy-chain variable region (15,16). In addition, the
`idea of gene conversion (17) was proposed as a possible mechanism of anti-
`body diversification, and appears to play a central role in chickens (18), and to
`a varying extent in humans, rabbits, and sheep.
`For precisely aligned amino acid sequences of Ig heavy-chain variable
`regions, CDRH3 is defined as the segment from position 95 to position 102,
`with possible insertions between positions 100 and 101. The CDRH3-binding
`loop is the result of the joining of the V-genes, D-minigenes, and J-minigenes.
`This intriguing process has been studied extensively (19,20), and suggests the
`CDRH3 plays a unique role in conferring fine specificity to antibodies (21,22).
`Indeed, a particular amino acid sequence of CDRH3 is almost always associ-
`ated with one unique antibody specificity. The CDRH3 sequences within the
`Kabat Database have further been analyzed by their length distributions (23),
`for which the length distributions of 2,500 complete and distinct CDRH3s of
`human, mouse, and other species were found to be more-or-less in agreement
`with the Poisson distribution. Interestingly, the longest mouse CDRH3 had a
`length of 19 amino acid residues, and that of human had 32 residues, and only
`one of them was shared by both species (24), suggesting that CDRH3 may be
`species-specific.
`Because of the subtle differences between the variable regions of the Ig light
`and heavy chains, their alignment position numberings are independent. For
`example, in light chains, the first invariant Cys is located at position 23 and
`CDRL1 is from position 24 to 34—e.g., immediately after the Cys residue.
`However, in heavy chains, the invariant Cys is located at position 22 and
`CDRH1 is from position 31 to 35—e.g., eight amino residues after that Cys.
`Because of this important difference, the Kabat numbering systems are sepa-
`rate for Ig light and heavy chains. Attempts to combine these two numbering
`systems into one in other databases have resulted in the presence of many gaps
`and confusions. Similarly, variable regions of TCR alpha, beta, gamma, and
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`Table 1
`FRs and CDRs of Antibody and TCR Variable Regions
`
`FR or CDR
`
`FR1
`CDR1
`FR2
`CDR2
`FR3
`CDR3
`FR4
`
`VL
`1–23
`24–34
`35–49
`50–56
`57–88
`89–97
`98–107
`
`VH
`1–22
`31–35B
`36–49
`50–65
`66–91
`95–102
`103–113
`
`Vα
`
`Vβ
`
`Vγ
`
`Vδ
`
`1–22
`23–33
`34–47
`48–56
`57–92
`93–105
`106–116
`
`1–21
`1–23
`22–34
`24–33
`35–49
`34–49
`50–59
`50–56
`60–95
`57–94
`96–107
`95–107
`108–116A 108–116C
`
`1–22
`23–34A
`35–49
`50–57
`58–89
`90–105
`106–116
`
`delta chains are aligned using different numbering systems. The alignments are
`summarized in Table 1, with the locations of CDRs indicated.
`
`1.2. Current Analysis and Use
`There are approx 25,000 unique yearly logins to the website of the Kabat
`Database by immunologists and other researchers around the world. The web-
`site is designed to be simple to use by those who are familiar with computers
`and those who are not. A description of the tools currently available is shown in
`Table 2. We encourage researchers who use the database to share their sugges-
`tions for improving the access and searching tools.
`A common but extremely important question asked by researchers is
`whether a new sequence of protein of immunological interest has been deter-
`mined before and stored in the database. Without asking this simple question,
`one may encounter the following situation: a heavy-chain V-gene from goldfish
`was sequenced (25) and found to be nearly identical to some of the human V-
`genes. Subsequently, the authors suggested that it might be of human origin,
`possibly because of the extremely sensitive amplification method used in the
`study and minute contamination of the sample by human tissue.
`Another common use of the database is to confirm the reading frame of an
`immunologically related nucleotide sequence. Comparing short segments of
`sequence with stored database sequences can easily identify inadvertent omis-
`sion of a nucleotide in the sequencing gel. Of course, if the missing nucleotide is
`real, this can suggest the presence of a pseudogene. Researchers also use the
`website to calculate variability for groupings of similar sequences of interest. For
`example, the variability plots of the variable regions of the Ig heavy and light
`chains of human anti-DNA antibodies are shown in Figs. 1 and 2. These two
`plots seem to indicate that CDRH3 may contribute most to the binding of DNA.
`In many instances, investigators would like to identify the germline gene
`that is closest to their gene of interest, as well as the classification of that par-
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`Table 2
`Listing of Tools Available on the Kabat Database Website
`
`Tool
`
`Seqhunt II
`
`Align-A-Sequence
`
`Subgrouping
`
`Find Your Families
`
`Current Counts
`
`Variability
`
`Description
`
`The SeqhuntII tool is a collection of searching programs for
`retrieving sequence entries and performing pattern matches,
`with allowable mismatches, on the nucleotide and amino
`acid sequence data. The majority of fields in the database are
`searchable—for example, a sequence’s journal citation.
`Matching entries may be viewed as HTML files or down-
`loaded and printed. Pattern matching results show the match-
`ing database sequence aligned with the target pattern, with
`differences highlighted.
`The Align-A-Sequence tool attempts to programmatically align
`different types of user-entered sequences. Currently kappa
`and lambda Ig light-chain variable regions may be aligned
`using the program.
`The Subgrouping tool takes a user-entered sequence of either
`Ig heavy, kappa, or lambda light-chain variable region and
`attempts to assign it a subgroup designation based on those
`described in the 1991 edition of the database. In many cases
`the assignment is ambiguous because of a sequence’s simi-
`larity to more than one subgroup.
`The Find Your Family tool attempts to assign a “family”
`designation to a user-entered sequence. The user-entered tar-
`get sequence is compared to previously assembled groupings
`of sequences, based on sequence homology. Please note that
`the assigned family number is arbitrary, since the groupings
`usually change as new data is added to the database.
`Current amino acid, nucleotide, and entry counts may be made
`for various groupings of sequences.
`Variability calculations may be made over a user-specified
`collection of sequences. The distributions used to calculate
`the variability are also available for viewing and printing.
`Variability plots can be customized for scale, axis labels, and
`title, or downloaded for printing.
`
`ticular gene to a specific family or subgroup. SEQHUNT (26) can pinpoint the
`sequence available in the database with the least number of amino acid or
`nucleotide differences.
`The previous examples represent most of the current uses of the Kabat Data-
`base by immunologists and other scientists. However, many more detailed
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`Fig. 1. Variability plot for human anti-DNA heavy-chain variable region.
`
`analyses are possible from the data stored in the Kabat Database, as shown in
`Table 3.
`In the following section, a current bioinformatics example is illustrated,
`using the uniquely aligned data contained in the Kabat Database.
`
`2. Kabat Database Bioinformatics Example: HIV gp120 V3-loop
`and Human CDRH3 Amino Acid Sequences
`The human immunodeficiency virus (HIV) has intrigued the scientific com-
`munity for several decades. It is a retrovirus with two copies of RNA as its
`genetic material. Upon infecting humans, HIV uses its reverse-transcriptase
`molecules to convert its RNA into DNA, which are in turn transported into the
`nucleus and incorporated into the host chromosomes of CD4+ T cells.
`Although the infected individual produces antibodies against the initial viral
`strain, not all viruses can be eliminated because of the integration of its genetic
`material into the host cells. Gradually, the viral-coat proteins change in
`sequence, rendering the host’s antibodies less effective. Eventually, acquired
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`Fig. 2. Variability plot for human anti-DNA kappa light-chain variable region.
`
`immunodeficiency syndrome (AIDS) develops with a latent period of approx
`10 ± 3 yr. Because of this, HIV is classified as a lentivirus or slow virus.
`Several specific drugs have been synthesized during recent years to treat
`HIV infection and AIDS. They include reverse-transcriptase inhibitors, pro-
`tease inhibitors, and fusion inhibitors. However, these drugs have serious side
`effects, and most are very expensive, making the cost of treatment prohibitive
`in countries with a large percentage of HIV-positive patients. For years, the
`ideal solution has been to develop an inexpensive vaccine. Unfortunately,
`because of the rapid changes of its envelope coat proteins, especially gp120,
`HIV strains cannot be singled out as candidates for vaccine. Many research lab-
`oratories around the world have undertaken the task of sequencing gp120, and
`these sequences have been stored on two websites:
`
`http://ncbi.nlm.nih.gov and http://www.lanl.gov
`Figure 3 shows a variability plot for the 302 nearly complete sequences of
`HIV-1 stored at the latter site. For comparison, a variability plot of 138
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`Table 3
`Partial Listing of Bioinformatics Studies Performed Using
`the Kabat Database
`
`Subject
`
`Binding Site Prediction
`
`Antibody Humanization
`
`Gene Count Estimation
`
`MHC Class I gene
`assortment
`TCR CDR3 length
`distribution
`
`Antibody and TCR
`evolution
`
`Designer Antibodies
`
`Autoimmunity
`
`Summary
`
`The CDRs of Ig heavy and light chains were predicted from
`variability calculations made over the sequence align-
`ments (1,8).
`It is possible to identify the most similar framework regions
`between the mouse antibody and all existing human anti-
`bodies stored in the database (30).
`From the existing sequences, it is possible to estimate the
`total number of human and mouse V-genes for antibody
`light and heavy chains, as well as TCR alpha and beta
`chains (31,32).
`The known sequences of human MHC class I sequences
`suggest that their a1 and a2 regions can be assorted (33).
`The lengths of CDR3s in antibodies and TCRs have distinct
`features (34,35). In the case of TCR alpha and beta
`chains, their CDR3 lengths follow a narrow and random
`distribution. That may be a result of the relatively fixed
`size and shape of the processed peptide in the groove of
`MHC class I or II molecules. On the other hand,
`although the TCR gamma chain CDR3 lengths are simi-
`larly distributed, those of TCR delta chains exhibit a
`bimodal distribution (35). TCR delta chains with shorter
`CDR3s may be MHC-restricted, although those with
`longer CDR3s MHC-unrestricted.
`Possible mechanisms of antibody and TCR evolution can
`also be investigated by comparing aligned sequences
`from different species (36,37).
`More specific/potent antibodies may be designed using the
`preferred CDR lengths calculated from database
`sequences against the same antigen (34).
`Similarities between non-self antigens such as influenza
`virus and Ig autoantibodies have been found. Certain
`antigens may help initially trigger autoimmunity, and
`certain antibody clones may help to stimulate the
`autoimmune response (36).
`
`aligned human influenza virus A hemagglutinin amino acid sequences is
`shown in Fig. 4.
`Based on various studies, the V3-loop has been singled out for vaccine
`development. Although the V3-loop has the least amount of variation among
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`Fig. 3. Variability plot for HIV-1 gp120.
`
`Fig. 4. Variability plot for influenza virus A hemagglutinin.
`
`the five V-loops, there are still many different sequences from various strains of
`HIV. How these different sequences are related to the pathogenesis and pro-
`gression of HIV infection is unclear. Longitudinal analysis of sequences of the
`V3-loop as the disease progresses is of vital importance in understanding the
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`changes that occur during infection, so that an effective vaccine can be devel-
`oped. Unfortunately, there is only one published report for a 10-yr sequence
`analysis, and in that case, the authors were unable to describe how the V3-loop
`amino acid sequences are related to disease progression (27).
`When HIV infects a person, its gp120 is a foreign protein and the patient
`produces antibodies toward this foreign antigen. However, once the HIV gene
`is integrated into the host chromosome, as in various human endogenous retro-
`viruses, the gp120 becomes a self-protein. This transition from foreign to self
`usually cannot occur instantaneously, but as it occurs the host will have
`increasing difficulty producing effective antibodies. Indeed, initial antibodies
`from patients who are infected with HIV are usually ineffective in binding HIV
`at later stages of the disease.
`The V3-loop has been described as being located on the surface of gp120.
`One way for the gp120 to become less antigenic would be for the virus to
`replace portions of the exposed V3-loop with segments of the host chromo-
`some. Although any human protein could serve this purpose, we investigate the
`possibility that human CDRH3 regions are being used. CDRH3 is particularly
`attractive, because they can assume many possible configurations and they are
`on the surface of normal human proteins.
`To locate matches between the V3-loop and CDRH3, the Kabat Database is
`uniquely useful. BLAST (http://www.ncbi.nlm.nih.gov) has recently allowed
`matches of short amino acid sequences, and eMOTIF (http://emotif.stanford.
`edu/emotif/) can be used to search for various length sequences. However, both
`programs use sequence databases containing large numbers of HIV-1 sequences
`and relatively few antibody heavy-chain variable region sequences. A search for
`short V3-loop sequences at these two websites usually results in a listing of other
`V3-loop sequences, and few,
`if any, CDRH3 sequences. By using the
`SEQHUNTII program, we picked the human heavy-chain variable regions and
`searched for all penta-peptides in the sequences of V3-loops determined in the
`10-yr longitudinal study. The result of matching is listed in Table 4.
`The initial number of matches is gradually reduced over the years, until the
`CD4+ T-cell count drops below 200. At that time, the number of matches
`increases dramatically. The match number appears to closely correlate with the
`number of HIV RNA molecules in the patient’s blood. For example, after treat-
`ment, the number of matches drops to zero, along with a reduction in the
`plasma HIV RNA number. Subsequently, after 10 yr of HIV infection, the
`number of matches begins to creep up again.
`A possible explanation for this finding is that the presence of CDRH3 penta-
`peptides in the V3-loop reduces its antigenicity. Such mutant HIV would bind
`existing anti-HIV antibodies in the patient less effectively, becoming more
`pathogenic. Based on this observation, the use of amino acid or nucleotide
`sequences of V3-loop as a vaccine would not be very efficient.
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`Table 4
`Longitudinal Study of HIV gp120 V3-Loop Sequence Variations
`
`Months
`after
`Infection
`
`Sequence
`of V3-loop
`determined
`
`Matches
`in human
`CDRH3
`
`CDR4+
`T-cells
`
`HIV RNA
`per mL of
`plasma
`
`0
`12
`27
`42
`70
`94
`97
`110
`118
`
`10
`10
`7
`5
`3
`12
`
`12
`12
`
`6
`3
`0
`0
`0
`21
`
`0
`1
`
`230
`230
`2,300
`230
`230
`23,000
`
`2,300
`2,300
`
`427
`277
`186
`156
`
`248
`212
`
`Sample
`
`A1
`A2
`A2b
`A3
`A4
`A5
`treatment
`A6
`A7
`
`An effective vaccine would most likely be made from an area of the exposed
`surface that does not contain high variability, as indicated in Fig. 3. There are
`several segments of seven or more nearly invariant amino acid residues in HIV
`gp120, in contrast to influenza virus hemagglutinin. Nearly invariant residues
`are defined as those that occur more than about 95% of the time at a particular
`position (1). They are located at the following positions (numbering including
`the precursor region) in the C1, C2, or C5 region of gp120:
`
`Segment #
`
`I
`II
`III
`IV
`V
`VI
`VII
`
`Position #
`
`4 to 14
`23 to 30
`44 to 50
`225 to 231
`261 to 267
`269 to 282
`538 to 545
`
`Sequence
`
`WVTVYYGVPVW
`LFCASDA
`ACVPTDP
`PIPIHYC
`VQCTHGL
`PVVSTQLLL-NGSL
`ELYKYKVV
`
`Some of the adjacent residues occur more than 90% of the time. Further-
`more, segments II and III and segments VI and V form disulfide bonds. Seg-
`ment VI is only one residue away from segment V, and that residue is either K
`or R most of the time. Segment I is near the N-terminal and segment VII near
`the C-terminal, and they are physically located near each other in the folded
`structure of gp120 (28). If these segments are indeed located on the surface of
`gp120, we may then suggest that segment I linked to segment VII—with link-
`ers consisting of repeats of GGGGS, segment II disulfide bounded to segment
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`III, and segment IV S-S bounded to segment V joined to segment VI with an
`intervening residue of K or R—should be used as possible peptide vaccine can-
`didates. Additional residues that occur more than 90% of the time may also be
`included in these segments, suggesting the following three possible peptides:
`
`In contrast, for influenza virus hemagglutinin amino acid sequences, no such
`segments of seven or more residues are found.
`
`3. Future Directions
`As previously discussed, during the past few years a substantial decline in
`the number of published sequences of proteins of immunological interest has
`occurred. With the shift in focus from brute-force data collection to in-depth
`analysis and “data mining” by various researchers, well-characterized data sets
`have become extremely important. Each entry in the database inherently con-
`tains a large amount of bioinformatic analysis such as alignment information,
`the relationship between gene sequence and protein sequence, and coding
`region designation. These relationships prove most valuable in allowing
`researchers to ask more intuitive, abstract questions than would be possible
`with most unaligned, raw sequence databases. We continue to locate, annotate,
`and align sequences found in the published literature. Periodically, the database
`and website are updated to reflect inclusion of the new data. Corrections of
`errors found in the sequence data by us and by database users are constantly
`made, ensuring the collection’s accuracy. We continue to explore new ways of
`relating the database entries, such as incorporating links to journal abstracts,
`links to 3D structural information, and germline gene assignment.
`We continue to create and develop software programs for performing various
`analyses of the data. We are in the process of converting many tools we have
`used into Java and adding graphical interfaces. Two major groupings of tools are
`currently being created: the first to update and extend the current entry retrieval
`tools (such as SeqhuntII), and the second to perform distribution analyses on
`entire groups of sequences (such as variability). Java tools for locating sequences
`based on pattern matching, length distribution of a specified region, positional
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`examination of a codon or residue, and sequence length have been developed
`and are undergoing testing. Many of the studies we have performed on the data-
`base require tools for grouping and analyzing collections of sequences rather
`than each one individually. We are developing a Java interface for creating distri-
`butions based on position (used most frequently for calculating variability),
`region length (used in length distribution analyses), and sequence pattern (used
`in gene count estimations and various homology studies). Together, these power-
`ful interfaces will allow researchers to quickly perform many complex bioinfor-
`matics studies on the aligned sequence data and combine their results.
`
`4. Conclusion
`The fundamental reason for creating and maintaining most sequence data-
`bases is to study and correlate a protein’s primary sequence structure with its 3D
`structure. Although there are many proteins with known 3D structures, there are
`probably two orders of magnitude more proteins with known amino acid or
`nucleotide sequences. In the 1950s, Anfinsen proposed and summarized in his
`1973 paper (29) that the primary sequence of a protein should determine its 3D
`folding. Unfortunately, we still do not know how to decipher this information.
`In the long run, the Kabat Database must be self-sustained. However, the
`transition from a free NIH-supported database to a self-sustaining format will
`take time and continued investigator interest. For example, it is hoped that the
`rapid development of therapeutic antibody techniques, using chimeric or
`humanized approaches, will eventually lead to the de novo synthesis of
`designer antibodies. Thus, immunotherapy for cancers and viral infections may
`rely heavily on the Kabat Database collections.
`We will also rely on users to suggest to us what basic immunological ideas,
`what computer programs, and which types kinds of structure and function
`information will be of importance for future studies in this central problem in
`biomedicine. This feedback from users is of primary importance to the exis-
`tence of the Kabat Database.
`
`References
`1. Wu, T. T. and Kabat, E. A. (1970) An analysis of the sequences of the variable
`regions of Bence Jones proteins and myeloma light chains and their implications
`for antibody complementarity. J. Exp. Med. 132, 211–250.
`2. Kabat, E. A., Wu, T. T., and Bilofsky, H. (1976) Variable Regions of Immunoglobu-
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`5. Kabat, E. A., Wu, T. T., Reid-Miller, M., Perry, H., and Gottesman, K. (1987)
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`12. Kabat, E. A., Wu, T. T., and Bilofsky, H. (1978) Variable region genes for
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`21. Kabat, E. A. and Wu, T. T. (1991) Indentical V-region amino acid sequences and
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