Computational Docking of Antibody-Antigen Complexes, Opportunities and Pitfalls Illustrated by Influenza Hemagglutinin
Abstract
:1. Introduction
1.1. Computational Docking
- “Bound”, if it originates from an experimental structure of the complex that needs to be docked. This is interesting when developing docking procedures but it is generally not biologically attractive, because computational docking is unlikely to add relevant information if an experimental structure is already available.
- “Unbound”, if it originates from an experimental structure of the molecule not bound to the partner that needs to be docked, i.e., either free or bound to a different partner. This is the most common scenario for antigens, especially since the number of available protein structures is increasing thanks to several structural genomics efforts. Structures of free antibodies, instead, are usually not available, nor they would be particularly useful since Abs are known to drastically change conformation upon binding [4].
- “Modeled”, if it has been predicted by homology modeling and/or other computational techniques like ab initio predictions or molecular dynamics. A thorough description of homology modeling for protein antigens is beyond the scope of this manuscript. Suffice to say that the results are remarkably accurate if the target protein has sequence similarity to a protein with known structure and that even ab initio predictions are starting to produce accurate results, albeit much less than homology modeling [5–7]. Antibody structures can be predicted with remarkable accuracy and precision as well; the process is relatively different from standard protein modeling and is covered in the next sections.
1.2. Antibody Structure, Implications for Modeling
1.3. Antibody Modeling Based on Canonical Structures, the PIGS Server
- Best heavy and light chains. Use the chains with highest sequence identity as templates. Since they come from different antibodies, the two chains need to be packed together by a least-squares fit of the residues conserved at the interface. This may introduce errors in the relative orientation of the two chains, with adverse consequences for the accurate modeling of the antigen binding site.
- Same canonical structures. Use a template whose CDR loops have the same canonical structures as the target even if a template with higher sequence identity exists for one or both chains. If framework and loops are taken from different templates, then the loops need to be grafted in, possibly introducing errors: the residues adjacent to the loop are superimposed to the framework by a weighted least-square fit of the main chain.
- Same antibody. Use the same antibody as template for both heavy and light chain, even if templates with higher sequence identity exist. This does not require optimization of the relative orientation of the two chains and thus avoids the errors illustrated earlier.
- Same antibody and canonical structures. The template is an antibody with the same canonical structures as the target and it is used to model both framework and the CDR loops. This option does not require optimization of framework orientation nor loop grafting and may offer more accurate results even if templates with higher sequence identity are available for one of the chains. The approach tends to fail, however, if the identity is too low.
1.4. Antibody Modeling by Rosetta Antibody
1.5. Other Procedures for Antibody Modeling
1.6. The Docking Calculation
1.7. Exploiting the Peculiarities of Antibodies to Simplify the Docking Search
2. Results and Discussion
2.1. Modeling Antibodies against Influenza Virus Hemagglutinin
2.2. Docking Antibodies against Influenza Virus Hemagglutinin
2.3. Selecting the Most Accurate Solution: the Scoring Problem
2.4. Discussion
3. Experimental Section
3.1. Antibody Modeling
3.2. Docking
3.3. RMSD Calculations
4. Conclusions
Acknowledgements
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RMSD (Å) C Only | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CR6261 | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | PIGS same Ab | PIGS same CanSt | PIGS Best HcLc |
Hc + Lc | 1.2 | 1.1 | 1.4 | 1.3 | 1.1 | 1.9 | 1.5 | 1.1 | 1.5 | 1.6 | 1.0 | 2.1 | 1.7 |
Lc | 1.0 | 0.9 | 1.2 | 1.1 | 0.9 | 1.6 | 1.3 | 0.9 | 1.2 | 1.3 | 0.9 | 1.9 | 1.7 |
Hc | 1.4 | 1.2 | 1.6 | 1.5 | 1.3 | 2.2 | 1.7 | 1.2 | 1.7 | 1.8 | 1.1 | 2.5 | 1.7 |
CDR (all) | 1.5 | 1.3 | 1.6 | 2.0 | 1.3 | 2.3 | 2.8 | 1.8 | 1.7 | 1.9 | 1.1 | 2.3 | 1.8 |
CDR (Lc) | 1.2 | 1.0 | 1.3 | 1.8 | 1.1 | 1.8 | 1.4 | 1.7 | 1.4 | 1.5 | 1.0 | 2.0 | 1.7 |
CDR (Hc) | 1.5 | 1.3 | 1.7 | 1.6 | 1.4 | 2.4 | 1.9 | 1.3 | 1.8 | 2.0 | 1.2 | 2.4 | 1.8 |
L1 (12) | 1.2 | 1.0 | 1.3 | 1.5 | 1.1 | 1.9 | 1.5 | 1.4 | 1.4 | 1.5 | 1.0 | 2.0 | 1.7 |
L2 (4) | 1.2 | 1.1 | 1.4 | 1.4 | 1.1 | 1.9 | 1.5 | 1.3 | 1.4 | 1.6 | 1.0 | 2.1 | 1.7 |
L3 (8) | 1.2 | 1.1 | 1.3 | 1.5 | 1.1 | 1.9 | 1.5 | 1.3 | 1.4 | 1.5 | 1.0 | 2.0 | 1.7 |
H1 (9) | 1.3 | 1.2 | 1.5 | 1.4 | 1.2 | 1.9 | 1.6 | 1.2 | 1.5 | 1.6 | 1.1 | 2.1 | 1.8 |
H2 (5) | 1.2 | 1.1 | 1.4 | 1.3 | 1.1 | 1.9 | 1.5 | 1.1 | 1.4 | 1.6 | 1.0 | 2.4 | 2.1 |
H3 (9) | 1.5 | 1.3 | 1.7 | 1.6 | 1.3 | 2.5 | 1.9 | 1.3 | 1.8 | 2.0 | 1.1 | 2.4 | 1.7 |
F10 | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | PIGS same Ab | PIGS same CanSt | PIGS Best HcLc |
Hc + Lc | 2.1 | 1.9 | 1.8 | 1.7 | 2.0 | 1.9 | 2.0 | 2.6 | 2.1 | 2.3 | 1.3 | - | - |
Lc | 2.1 | 1.9 | 1.7 | 1.7 | 2.0 | 1.9 | 2.0 | 2.6 | 2.1 | 2.3 | 1.3 | - | - |
Hc | 2.5 | 2.1 | 1.9 | 1.8 | 2.3 | 2.1 | 2.2 | 3.1 | 2.3 | 2.7 | 1.2 | - | - |
CDR (all) | 2.5 | 2.5 | 2.0 | 1.9 | 2.4 | 2.3 | 2.3 | 3.1 | 2.5 | 2.8 | 1.5 | - | - |
CDR (Lc) | 2.1 | 2.2 | 1.7 | 1.6 | 2.0 | 1.9 | 1.9 | 2.5 | 2.0 | 2.3 | 1.3 | - | - |
CDR (Hc) | 2.6 | 2.3 | 2.1 | 2.0 | 2.5 | 2.4 | 2.4 | 3.2 | 2.5 | 2.9 | 1.5 | - | - |
L1 (11) | 2.1 | 2.1 | 1.7 | 1.7 | 2.0 | 1.9 | 1.9 | 2.6 | 2.1 | 2.3 | 1.3 | - | - |
L2 (4) | 2.1 | 2.0 | 1.7 | 1.7 | 2.0 | 1.9 | 1.9 | 2.6 | 2.1 | 2.3 | 1.3 | - | - |
L3 (7) | 2.1 | 2.0 | 1.7 | 1.6 | 2.0 | 1.9 | 1.9 | 2.6 | 2.1 | 2.3 | 1.3 | - | - |
H1 (8) | 2.1 | 1.9 | 1.8 | 1.7 | 2.0 | 1.9 | 1.9 | 2.6 | 2.1 | 2.3 | 1.3 | - | - |
H2 (4) | 2.1 | 1.9 | 1.7 | 1.7 | 2.0 | 1.9 | 1.9 | 2.6 | 2.1 | 2.3 | 1.3 | - | - |
H3 (12) | 2.7 | 2.3 | 2.1 | 2.0 | 2.6 | 2.4 | 2.4 | 3.4 | 2.6 | 3.0 | 1.4 | - | - |
Bound R6261 | Bound F10 | Unbound | Model CR6261 | Model F10 | |
---|---|---|---|---|---|
Bound CR6261 | 0.6 | 1.0 | 1.4 | 0.9 | |
Bound F10 | 0.6 | 1.1 | 1.3 | 1.6 | |
Unbound | 1.0 | 1.1 | 1.0 | 1.1 | |
Model CR6261 | 1.4 | 1.3 | 1.0 | 0.6 | |
Model F10 | 0.9 | 1.6 | 1.1 | 0.6 |
Bound | Unbound | Model | |
---|---|---|---|
R1 | 1.9 | 4.9 | 2.0 |
R2 | 3.0 | 4.2 | 1.3 |
R3 | 2.5 | 3.7 | 1.4 |
R4 | 2.4 | 3.7 | 2.1 |
R5 | 2.1 | 3.2 | 1.8 |
R6 | 1.6 | 3.8 | 1.5 |
R7 | 1.0 | 3.9 | 1.8 |
R8 | 2.6 | 3.6 | 2.6 |
R9 | 2.4 | 3.7 | 1.2 |
R10 | 2.1 | 4.1 | 1.6 |
PIGS | 5.8 | 6.9 | 6.8 |
Bound | 2.0 | 1.9 | 1.1 |
Bound | Unbound | Model | |
---|---|---|---|
R1 | 0.9 | 0.8 | 1.3 |
R2 | 1.3 | 1.2 | 1.6 |
R3 | 1.0 | 1.0 | 1.7 |
R4 | 0.8 | 1.8 | 1.6 |
R5 | 1.0 | 1.2 | 0.9 |
R6 | 0.6 | 1.3 | 2.0 |
R7 | 1.3 | 0.9 | 0.9 |
R8 | 1.6 | 0.5 | 0.8 |
R9 | 1.0 | 2.0 | 0.5 |
R10 | 0.9 | 1.4 | 2.5 |
PIGS | 0.7 | 1.2 | 0.4 |
Bound | 0.3 | 2.1 | 1.5 |
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Pedotti, M.; Simonelli, L.; Livoti, E.; Varani, L. Computational Docking of Antibody-Antigen Complexes, Opportunities and Pitfalls Illustrated by Influenza Hemagglutinin. Int. J. Mol. Sci. 2011, 12, 226-251. https://doi.org/10.3390/ijms12010226
Pedotti M, Simonelli L, Livoti E, Varani L. Computational Docking of Antibody-Antigen Complexes, Opportunities and Pitfalls Illustrated by Influenza Hemagglutinin. International Journal of Molecular Sciences. 2011; 12(1):226-251. https://doi.org/10.3390/ijms12010226
Chicago/Turabian StylePedotti, Mattia, Luca Simonelli, Elsa Livoti, and Luca Varani. 2011. "Computational Docking of Antibody-Antigen Complexes, Opportunities and Pitfalls Illustrated by Influenza Hemagglutinin" International Journal of Molecular Sciences 12, no. 1: 226-251. https://doi.org/10.3390/ijms12010226