Antibody Structure Prediction and the Use of Mutagenesis in Docking Arvind Sivasubramanian, Aroop Sircar, Eric Kim & Jeff Gray Johns Hopkins University, Baltimore. RosettaCon 2007.

Antibody structure Ab structure schematicAb PDB structure

Homology model (WAM) errors frustrate high-resolution docking Antigen rmsd relative to native (Å) Better docking energy Ab crystal structure Ab homology model Docking of mAb 225 (Cetuximab) to its antigen

Antibody Modeling from Sequence mAb AQC2 (anti-VLA1) Light chain sequence: LTQSPSSLSASVGDRVTITC SASSQVNHMF WYQQ KPGKAPKPWIY LTSYLAS GVPSRFSGSGSGTDYTLT ISSLQPEDFATYYC QQWSGNPWT FGQGTKVE Heavy chain sequence: LVESGGGLVQPGGSLRLSCAAS GFTFSRYTMS WVR QAPGKGLEWVA VISGGGHTYYLDSVEG RFTISRDN SKNTLYLQMNSLRAEDTAVYYCTR GFGDGGYFDV W GQGTLVT

Antibody Modeling: Scientific Challenges Easier aspects of the modeling – Conserved framework structure – Canonical CDR loop conformations – Canonical loops erected on beta-barrel framework Challenges –CDR H3 conformation (Loop modeling) –V L -V H orientation (Protein-protein docking)

Step 1: Assemble β-Barrel Select V L and V H frameworks Assemble β-barrel Graft canonical loops for CDR’s L1-L3, H1 and H2 Antibody sequence H3 loop modeling Homology model Refine β-barrel orientation using V L - V H docking Iteration

Step 2: Graft canonical loops Select V L and V H frameworks Assemble β-barrel Graft canonical loops for CDR’s L1-L3, H1 and H2 Antibody sequence H3 loop modeling Homology model Refine β-barrel orientation using V L - V H docking Iteration

Step 3: H3 modeling Select V L and V H frameworks Assemble β-barrel Graft canonical loops for CDR’s L1-L3, H1 and H2 Antibody sequence H3 loop modeling Homology model Refine β-barrel orientation using V L - V H docking Iteration

CDR3 loop modeling protocol Low resolution, fragment-based loop library creation High-resolution loop refinement Loop building w. Rosetta /Ab fragments Pre-screened “kink” fragments One cycle of full-atom refinement Shear, small & CCD moves (5 each) 25 cycles of DFP minimization CDR H3 sequence CDR H3 conformation

Antibody structure database Download crystal structures from PDB based on SACS database Employ Filters (F c regions, L/H dimer’s, Single chain antibodies) Retain structures with R < 2.5Å Eliminate structures with redundant CDR’s 645 structures 451 structures 279 structures 167 structures Database Curation : Automatic V L and V H domain classification, elimination of structures with missing residues, broken loops, Chothia re-numbering.

CDR H3 length distribution in antibody structure database

Knowledge base: “Kinked” CDR H3 C-Terminus conformation N-Ter C-Ter N-Ter C-Ter KinkedExtended

Knowledge base: Conserved rotamers VLVL VHVH

Homology modeling: Canonical loop RMSD typically < 1.0Å Number Benchmark PDB’s RMSD to Native (Å)

Native H3 recovery: Best RMSD in top 10 models

Native H3 recovery: High, medium and low accuracy predictions 2B2X1DBA2CJU RMSD = 0.31ÅRMSD = 1.00 ÅRMSD = 1.56 Å

Homology modeling: Best RMSD in top 10 models CDR H3 Modeling Light-heavy chain docking Total PDB’s = 10

Use of antibody-derived fragments improves sampling marginally % decoys with H3 RMSD < 1.25 Å 1000 decoys are created during the “build” stage with Rosetta+Ab Fragments, with or without bias towards choice of Ab fragments

3-residue fragments from H3 loops cluster at the 0.5 Å level RMSD ( Å)

Rosetta Antibody Modeling server Input –Sequences of Light and Heavy Fv chain regions Outputs –Blast alignments for the framework and CDR loops –Homology model template with all CDR's except H3

Validate model with new blind computational & experimental mutagenesis Combing Protein-protein docking and computational mutagenesis Generate docking model using RosettaDock Calculate  G of interface mutations in model using RosettaInterface Reject NoYes Consistent with experiments? Antibody Coordinates Antigen Coordinates Interface mutations

Crystal structure or docking model Interface mutation RosettaInterface  G calc Kortemme et al, PNAS, 2002  G calc D1.3 – HEL RosettaInterface :  G values for mutations at protein interfaces Native Complex Mutant Complex  G = ?? Mutation Empirical criteria 0 <  G calc < 1.0 Neutral  G calc > 1.0 Binding loss  G expt

46% of native contacts 1.92 Å interface rmsd No Binding loss Binding loss Combining Computational & Experimental Mutagenesis (CAPRI Target 21: Orc1-Sir1 complex) Column 1 is the ΔΔG for our best model Column 2 is the ΔΔG for the native 1ZHI Predicted by Aroop Sircar, Arvind Sivasubramanian, Mike Daily

46% of native contacts 1.92 Å interface rmsd The native ORC1 is more closed than our prediction. No Binding loss Binding loss Combining Computational & Experimental Mutagenesis (CAPRI Target 21: Orc1-Sir1 complex) Column 1 is the ΔΔG for our best model Column 2 is the ΔΔG for the native 1ZHI Predicted by Aroop Sircar, Arvind Sivasubramanian, Mike Daily

Conclusions Antibody modeling –Curated antibody structure database –Knowledge-based rules for homology modeling –Canonical loops predicted with 1 Å accuracy –H3 loop accuracy is 1 Å in native recovery simulations; homology modeling accuracy is 1-2 Å –Light-heavy chain docking predicted within 1.5 Å accuracy Mutagenesis and docking – Better discrimination than using simple contact filters

Future work Antibody modeling – Generate homology models for entire benchmark set –Validate homology models in antibody-antigen docking simulations Mutagenesis and docking – Implement use of biochemical constraints during docking