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22 September, 2015
The need for antibody structures
Pharma perspective
Pistoia alliance webinar
Sebastian Kelm (UCB), Guy Georges (Roche), Bojana Popovic
(MedImmune)
©PistoiaAlliance
Why do we need more Ab structures?
• PDB contains ~ 2000 ab structures
– < 1000 non-identical structures
– Ab diversity is >1012
• Tools for Ab modeling exists in different flavors
– commercial, academic, owned by industrial research
groups
– quality is increasing, but highly dependent on PDB
• Abs among the top-grossing therapeutics
– growing interest since more than a decade!
– Bi-specifics and Ab fusion and coupling as second
generation therapeutics
©PistoiaAlliance
Common uses of antibody structures in
pharma
• Analysing, predicting and engineering molecular properties:
– Structure
• Visual analysis
• Computational prediction of antibody-antigen interactions
– Mode-of-action
• Affinity/specificity determining residues
• Protein-protein interactions blocked by antibody
• Species cross-reactivity
– Biophysical and immunogenic properties
• Humanisation
• Developability (can impact BPD and manufacturing of drugs)
• Better coverage and quality of structures in the PDB means:
– better models of antibody structure
– better predictions of activity and biophysical properties that help us
generate better medicines
©PistoiaAlliance
Large numbers enable statistics
Prediction of VH-VL domain orientation for antibody variable domain modeling.
Bujotzek A, Dunbar J, Lipsmeier F, Schaefer W, Antes I, Deane CM, Georges G.
Proteins: Stucture, Function, and Bioinformatics. 2015, 83, 681-695.
Predictors are retrained while database
is growing, accuracy increases!
©PistoiaAlliance
Large numbers enable statistics
MoFvAb: modeling the Fv region of antibodies
Bujotzek A, Fuchs A, Qu C, Klostermann S, Antes A, Georges G.
mAbs. 2015, 7(5), 838-852.
crystal structure
unrefined MoFvAb
neighborhood-refined
MoFvAb
Knowledge-based
positioning accuracy will
grow when increasing
the database size
©PistoiaAlliance
Large numbers enable statistics
Improving B-cell epitope prediction and its application to global antibody-
antigen docking. Krawczyk K, Liu X, Baker T, Shi J and Deane CM.
Bioinformatics. 2014, 30(16):2288-2294.
Intramolecular distances can be used to predict antibody-antigen contacts
©PistoiaAlliance
Initiative to get more Abs into the PDB
• Collect non published structures within big
Pharmas (“dead” projects or sleeping data) for
deposition into the PDB
• Convince the whole community to act similarly
• Define gaps and provide sequences. Diversity!
– How to define “diversity”?
• Exotic CDRs
• Non canonical CDRs
• Flexible/rigid, highly/less stable, prone or not to aggregation,
long/short PK properties, …
– Companies to contribute their own lists of sequences
info@pistoiaalliance.org @pistoiaalliance www.pistoiaalliance.org

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UCB Slides from Pistoia Alliance webinar on Antibody structures in the PDB

  • 1. 22 September, 2015 The need for antibody structures Pharma perspective Pistoia alliance webinar Sebastian Kelm (UCB), Guy Georges (Roche), Bojana Popovic (MedImmune)
  • 2. ©PistoiaAlliance Why do we need more Ab structures? • PDB contains ~ 2000 ab structures – < 1000 non-identical structures – Ab diversity is >1012 • Tools for Ab modeling exists in different flavors – commercial, academic, owned by industrial research groups – quality is increasing, but highly dependent on PDB • Abs among the top-grossing therapeutics – growing interest since more than a decade! – Bi-specifics and Ab fusion and coupling as second generation therapeutics
  • 3. ©PistoiaAlliance Common uses of antibody structures in pharma • Analysing, predicting and engineering molecular properties: – Structure • Visual analysis • Computational prediction of antibody-antigen interactions – Mode-of-action • Affinity/specificity determining residues • Protein-protein interactions blocked by antibody • Species cross-reactivity – Biophysical and immunogenic properties • Humanisation • Developability (can impact BPD and manufacturing of drugs) • Better coverage and quality of structures in the PDB means: – better models of antibody structure – better predictions of activity and biophysical properties that help us generate better medicines
  • 4. ©PistoiaAlliance Large numbers enable statistics Prediction of VH-VL domain orientation for antibody variable domain modeling. Bujotzek A, Dunbar J, Lipsmeier F, Schaefer W, Antes I, Deane CM, Georges G. Proteins: Stucture, Function, and Bioinformatics. 2015, 83, 681-695. Predictors are retrained while database is growing, accuracy increases!
  • 5. ©PistoiaAlliance Large numbers enable statistics MoFvAb: modeling the Fv region of antibodies Bujotzek A, Fuchs A, Qu C, Klostermann S, Antes A, Georges G. mAbs. 2015, 7(5), 838-852. crystal structure unrefined MoFvAb neighborhood-refined MoFvAb Knowledge-based positioning accuracy will grow when increasing the database size
  • 6. ©PistoiaAlliance Large numbers enable statistics Improving B-cell epitope prediction and its application to global antibody- antigen docking. Krawczyk K, Liu X, Baker T, Shi J and Deane CM. Bioinformatics. 2014, 30(16):2288-2294. Intramolecular distances can be used to predict antibody-antigen contacts
  • 7. ©PistoiaAlliance Initiative to get more Abs into the PDB • Collect non published structures within big Pharmas (“dead” projects or sleeping data) for deposition into the PDB • Convince the whole community to act similarly • Define gaps and provide sequences. Diversity! – How to define “diversity”? • Exotic CDRs • Non canonical CDRs • Flexible/rigid, highly/less stable, prone or not to aggregation, long/short PK properties, … – Companies to contribute their own lists of sequences