Analysis of Single Chain Antibody Sequences Using the VBASE2 Fab Analysis Tool

  • Svetlana Mollova
  • Ida Retter
  • Michael Hust
  • Stefan Dübel
  • Werner MüllerEmail author
Part of the Springer Protocols Handbooks book series (SPH)


The VBASE2 database integrates sequences present in the various DNA sequence databases and selects germline-encoded variable gene segments from these databases. The sequences are grouped into three classes: the first class consisting of the variable gene segments that are proven germline sequences are also found in rearrangements; class two sequences are only found as non-rearranged form, suggesting that most of these sequences are pseudogenes; and class three sequences are those sequences recovered from independent immunoglobulin rearrangements, but the germline counterpart has not been found so far.

Based on the VBASE2 database, an innovative sequence analysis tool is integrated into the VBASE2 Internet web page that allows the analysis of multiple rearranged immunoglobulin sequences. In this chapter, we demonstrate the use of the new Fab Analysis – a unique tool that is tailored for the analysis of both heavy and light chain sequences of a given antibody or collection of sequences. The tool also analyses sequences obtained from phage display libraries as both heavy and light chain sequences are extracted from the input sequences.

Output of the Fab Analysis tool are the identified V(D)J gene segments, nucleotide and amino acid sequences of the junction, as well as the nucleotide sequence of the rearrangement and its translation displayed with delimitations and numbering for all complementary determining regions (CDR) and framework regions (FR). Comparison alignment of the combination of heavy and light chain CDRs and summary tables practical for the export of results into a database or a spread sheet program complete the functionality of the tool.


Light Chain Phage Display Library Lambda Light Chain Germline Sequence Complementary Determine Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Svetlana Mollova
    • 2
  • Ida Retter
    • 2
  • Michael Hust
    • 2
  • Stefan Dübel
    • 2
  • Werner Müller
    • 1
    Email author
  1. 1.Faculty of Life ScienceUniversity of ManchesterManchesterUK
  2. 2.Technische Universität BraunschweigBraunschweigGermany

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