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, 83 (8), 1385-406

AbDesign: An Algorithm for Combinatorial Backbone Design Guided by Natural Conformations and Sequences

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AbDesign: An Algorithm for Combinatorial Backbone Design Guided by Natural Conformations and Sequences

Gideon D Lapidoth et al. Proteins.

Abstract

Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function--essential to exert control over all polypeptide degrees of freedom--remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity.

Keywords: CDRs; Rosetta; V(D)J recombination; canonical conformations; computational protein design; conformation-sequence optimization; fuzzy-logic design; modular segments.

Figures

Figure 1
Figure 1. Overview of the design protocol workflow.
Briefly, structures of naturally occurring antibodies are extracted from the Protein Data Bank (PDB) and aligned to a template antibody structure. Backbone segment conformations and sequences are extracted into two correlated databases: a Position-Specific Site Matrix (PSSM, step 1) database (step 1) and a backbone-torsion database (step 2), where PSSMs and their respective torsion databases are linked. From the torsion database a set of antibody conformations representing all combinations of canonical conformations is generated (step 3), docked against the target surface (step 4) and designed for optimal binding affinity, subject to sequence constraints derived from the PSSMs (step 5). Antibodies passing structure and energy filters are then subjected to a backbone and sequence refinement protocol (step 7): for each backbone segment (VL, VH, L3, H3) alternative conformations are sampled from the pre-computed torsion database and designed in the context of the modeled antibody-bound structure. The backbone conformation with the highest computed stability and affinity for the ligand is selected using fuzzy-logic design, and serves as input in the optimization of the next backbone segment. Finally, designs are filtered using energy and structural criteria derived from natural antibodies (step 8).
Figure 2
Figure 2. Natural V (D) J gene segmentation versus conformation segmentation used in AbDesign represented on the 4m5.3 (PDB entry 1X9Q) antibody.
AbDesign segments the antibody structure at the disulfide-linked cysteines and in a structurally conserved position at the end of CDR3 (stem positions are underlined). Natural antibody recombination follows a similar, but not precisely the same, segmentation (bars above sequence and the V, D, and J labels). Sequence and structure are color-coded by conformation segments (red: CDRs L1&L2 and framework, green: L3, blue: H1&H2, yellow: H3). Gray segments are only subjected to sequence, rather than backbone optimization.
Figure 3
Figure 3. Sequence and conformation coupling during design.
During design of a new backbone the PSSMs used to constrain sequence choices are altered. In this example, the H3 backbone segment from antibody 5G9 (PDB entry: 1AHW) is modeled in the context of the 4m5.3 antibody (PDB entry: 1X9Q). Web-logos for the two conformation segments are shown on the right, revealing different amino acid conservation patterns, which are important for the structural integrity of the modeled segment. For instance, the H3 backbone conformation from 1X9Q is in an extended conformation, whereas the imposed H3 backbone conformation is kinked , and characterized by a hydrogen bond between the conserved stem Trp (Trp103, Chothia numbering) Nε1 atom and a carbonyl oxygen (Met100, Chothia numbering). The conserved salt bridge between Arg94 and Asp101 is similarly frequently observed in kinked conformations. Surrounding residues in a 6 Å shell around the inserted backbone segment are also designed and repacked under sequence constraints to accommodate the new backbone conformation. In the example shown, residues Phe27 and Tyr32 from the heavy chain and residue Tyr32 from the light chain are repacked to avoid clashes with the designed H3 conformation.
Figure 4
Figure 4. Fuzzy-logic design is used to optimize binding affinity and antibody stability.
A. Plot of the fuzzy-logic objective function, which is the product of the stability and binding sigmoids (Eq. 1). A transformed value of a -10 R.e.u change in binding and stability is preferredto a -30 R.e.u change in stabilty and a -1 R.e.u change in binding. The product of the two transformations gauges the effect the incorporated segment has on the antibody’s stability and binding affinity for the target relative to the baseline score (the interim best scoring antibody structure so far). B & C. Comparison between the stability and binding energy of a set of designed antibodies before and after refinement (algorithm, section f). The X-axis is the calculated energy (R.e.u) of the antibody-target complex after sequence optimization (algorithm, section e) and before refinement. Y-axis is the designed antibody energy (R.e.u) after the backbone refinement phase (algorithm, section f).
Figure 5
Figure 5. Energy and structure criteria used to filter designed antibody structures.
In the final step of AbDesign we filter the designed antibodies according to four parameters: predicted binding energy, buried surface area, shape complementarity between antibody structure and ligand, and packing quality between the variable light and heavy domains and the ligand. Cutoffs (green dashed lines) were derived from a set of 303 natural protein-binding antibodies (Table S3). Antibody designs (purple) that passed all filters are compared to the natural protein-binding antibodies (gold).
Figure 6
Figure 6. Antibody designs have similar backbone conformations to natural antibodies that target the same surface.
Comparison between the backbone conformation of designed (magenta) and natural (orange) antibodies targeting to the same surface. (A). The anti-transmembrane glycoprotein (D5 neutralizing mAb, PDB entry 2CMR). Cα RMSD between the design and natural antibody is 1.1Å, and ligand interface RMSD is 2.7 Å. (B). The anti-tissue factor protein (D3H44, PDB entry 1JPS). Cα RMSD between design and natural antibody is 1.05 Å and ligand interface RMSD is 2Å.
Figure 7
Figure 7. Designed antibody-backbone atoms form polar contacts with the ligand and supporting polar interactions within the antibody.
The best predicted binding affinity design (magenta) of an anti tissue-factor antibody is shown with the target ligand (blue). Two polar contacts (dashed orange lines) are formed between the L3 Ser94 amide nitrogen and the carbonyl group of Thr167 from tissue factor and between Tyr92 carbonyl and the amide nitrogen of Lys169 from tissue factor. The hydroxyl group of Tyr 96 forms an additional hydrogen bond with the ε-amino group of Lys169. In addition the conserved Glu90 forms multiple hydrogen bonds with the backbone atoms of the L3 loop that stabilize the conformation.
Figure 8
Figure 8. Designed backbone fragments conserve the stabilizing interactions observed in the natural source antibody.
The natural VL segment from PDB entry 3IDI (orange) encodes long-range stabilizing interactions between CDR L1 and the framework, for instance, using hydrogen bonds (dashed green lines), hydrophobic, and aromatic-stacking interactions. Though the VL segment used in the design targeting tissue factor (target PDB entry: 1JPS, left) has a different sequence than that of the source fragment (right), the same types of stabilizing interactions are made in the designed fragment.
Figure 9
Figure 9. AbDesign favors larger binding surfaces.
(A). Comparison between the top-ranked anti-lysozyme design (magenta) and the natural antibody, F10.6.6, PDB entry 1P2C (gold). The designed antibody uses a longer L1 (16 amino acid, compared to 11 in the natural antibody) and a longer L3 (11 amino acids compared to 10), increasing the buried surface area from 1470 Å2 to 1680 Å2. (B) Comparison between the anti-hepatocyte growth factor activator designed antibody (magenta) and the natural antibody, Fab40, PDB entry 3K2U (orange). Structures are oriented so CDRs are pointing towards the viewer. A 10o difference in the packing angle between the variable light and heavy domains creates a gap between the CDRs of the natural antibody’s variable light and heavy domains compared to the designed one (marked by red arrows). This opening in the light and heavy domain interface produces a larger binding surface in the natural antibody compared to the design.
Figure 10
Figure 10. Designed antibodies recapitulate the identity and conformation of binding surface and core residues.
The anti-tissue factor design. Tissue factor is shown in surface representation colored by vacuum electrostatics using Pymol (69). Residues at the VL/VH interface are shown as sticks. The natural antibody D3H44 (PDB entry: 1JPS) is colored orange.
Figure 11
Figure 11. Designed sidechains are predicted to be rigid.
(A). The sidechain conformation probabilities in the unbound state were computed using the method in Ref. . AbDesign produces antibody complexes with a lower proportion of low-probability conformations (≤ 0.05 probability) compared to natural antibody complexes. The natural antibody complex set comprises 303 antibody-protein complexes (supplemental table S3) extracted from the SabDab database, and the designed antibody set includes all designs generated and filtered by the design protocol. (B). The designed antibody against the sonic hedgehog protein. The constrained tyrosine (colored green, with rotamer Boltzmann probability 90%) is stabilized by packing against surrounding backbone atoms and the side chain atoms of Tyr53 and a hydrogen bond with Asn31. (C). The anti-tissue factor protein designed antibody. Tyr137 on H1 (rotamer Boltzmann probability: 60%) is stabilized by packing against the backbone atoms of H1 and H3 and the side chain of Phe132.

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