1 / 28

Exploring the need for a new Antibody Homology Modeling Protocol

Exploring the need for a new Antibody Homology Modeling Protocol. Antibody Fv regions. Gray CDR’s Brown SVR’s Cyan and Green Framework. Light. Heavy. Looking down into the Antibody Fv beta-barrel. Alignment using the beta-barrel. Isn’t Antibody homology modeling a solved problem?.

august
Download Presentation

Exploring the need for a new Antibody Homology Modeling Protocol

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Exploring the need for a new Antibody Homology Modeling Protocol

  2. Antibody Fv regions Gray CDR’s Brown SVR’s Cyan and Green Framework Light Heavy

  3. Looking down into the Antibody Fv beta-barrel

  4. Alignment using the beta-barrel

  5. Isn’t Antibody homology modeling a solved problem? • For the most part! • CDR loops erected on robust beta-barrel framework • CDR conformations can be predicted from sequence alone (Canonical conformations) • What is left to do then • Potential differences due to light/heavy chain types • CDR H3 conformation • Homology Model Quality Control • And we are interested in this problem because… • Docking!!! • Ab homology modeling is fairly important : 5 WAM citations per year

  6. WAM algorithm(Whitelegg and Rees , 2000) Select sequence homologous light and heavy chain frameworks Fit the frameworks on an AVERAGEbeta-barrel Build canonical loops Build CDR H3

  7. MolProbity analysis of WAM model quality • Several WAM errors occur in the CDR regions

  8. C-beta deviation Lovell et al 2003 • Based on ideal C-beta geometry calculated from backbone information

  9. mAb 225 xtal and WAM comparison L3 H1 H3 Light chain Heavy chain

  10. mAb 225 xtal and WAM comparison H1 L1 H2 H3 L3 L1

  11. 14b7 xtal and WAM model comparison Light chain Heavy chain

  12. 14b7 xtal and WAM model comparison H1 L1 H2 H3 L3 L1

  13. Creation of antibody database Download crystal structures from PDB based on SACS database 645 structures Employ Filters (Fc regions, L/H dimer’s, Single chain antibodies) 451 structures Retain better than 2.5Å structures 279 structures Eliminate structures with Redundant CDR’s 167 structures

  14. Quality of structures in the database

  15. H3 length distribution in Ab database • Current loop modeling techniques can handle < 12 residue loops (Rohl 2004)

  16. Questions for WAM • Is the AVERAGE beta-barrel assumption justified? • Is sequence homology the only criterion for choosing the initial framework on which the CDR’s are constructed?

  17. Four distinct Vh subtypes Honegger and Pluckthun, JMB 2001

  18. Is the AVERAGE Beta-barrel WAM assumption justified?  Type Ab’s Everything else! • The WAM AVERAGE beta-barrel assumption seems to to be constant, except for AB’s with Lambda chains. 0.7Å

  19. Light chain framework clusteringRadius = 0.5Å  Type Ab’s 0.5Å

  20. Light chain framework alignment Radius = 0.5Å

  21. Heavy chain framework clustering Radius = 0.5Å

  22. Heavy chain framework alignment Radius = 0.5Å • At the 0.7Å level, Vh subtypes there are 3 main clusters comprised of Subtypes 1,2 and 3+4 • All differences are eliminated at the 0.9Å level

  23. Conclusions from cluster analysis • Internal structural differences between • /  light chain types • Heavy chain subtypes • Heavy chain structural differences (identifiable using sequence motifs) cause variations in positioning of CDR stem regions • Beta-barrel fairly constant. However, • scFv’s with  light chains have different beta-barrel orientation from  light chains

  24. Homology server

  25. New Homology Server protocol Select sequence homologous light and heavy chain frameworks Account for light/heavy chain subtype Fit the frameworks on an AVERAGEbeta-barrel Account for light chain subtype Ensure consistency with canonical loop conformations Build canonical loops Minimize deviations from loop conformations of equal length Build CDR H3 Final Quality Control Rotamers, backbone  / C- deviations

  26. Scientific tasks • Stage 1 • Light/heavy subtype identification and framework assignment  • Identification of canonical loop conformations • Align these frameworks on the beta-barrel • Stage 2 • Thread sequence through framework • Build SVR regions • Build canonical loops extracted from relevant PDB • Stage 3 • Build CDR H3 • Stage 4 • Quality Control • Stage 5 • Output

  27. Conclusions • There is room for improving WAM’s performance in • Identification of light/heavy chain subtypes • Proper positioning of CDR stems • H3 modeling • Overall model quality • Creating scripts and setting up the web server requires an intensive 10-12 weeks.

More Related