A new clustering of antibody CDR loop conformations

By Benjamin North, A. Lehmann, Roland L. Dunbrack. 2010

387 citations recorded by the source corpus.

Previous analyses of the complementarity determining regions (CDRs) of antibodies have focused on a small number of “canonical” conformations for each loop. This is primarily the result of the work of Chothia and colleagues, most recently in 1997. Because of the widespread utility of antibodies, we have revisited the clustering of conformations of the six CDR loops with the much larger amount of structural information currently available. In this work, we were careful to use a high-quality data set by eliminating low-resolution structures and CDRs with high B-factors or high conformational energies. We used a distance function based on directional statistics and an effective clustering algorithm using affinity propagation. With this data set of over 300 non-redundant antibody structures, we were able to cover 28 CDR-length combinations (e.g., L1 length 11, or “L1-11” in our nomenclature) for L1, L2, L3, H1 and H2. The Chothia analysis covered only 20 CDR-lengths. Only four of these had more than one conformational cluster, of which two could easily be distinguished by gene source (mouse/human; κ/λ) and one purely by the presence and positions of Pro residues (L3-9). Thus using the Chothia analysis does not require the complicated set of “structure-determining residues” that is often assumed. Of our 28 CDR-lengths, 15 of them have multiple conformational clusters including ten for which Chothia had only one canonical class. We have a total of 72 clusters for the non-H3 CDRs; approximately 85% of the non-H3 sequences can be assigned to a conformational cluster based on gene source and/or sequence. We found that earlier predictions of “bulged” vs. “non-bulged” conformations based on the presence or absence of anchor residues Arg/Lys94 and Asp101 of H3 have not held up, since all four combinations lead to a majority of conformations that are bulged. Thus the earlier analyses have been significantly enhanced by the increased data. We believe the new classification will lead to improved methods for antibody structure prediction and design.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

AbDesign: an algorithm for combinatorial backbone design guided by natural conformations and sequences (2015, methodology): “”22,24–26 For instance, in hundreds of antibody molecular structures only seven conformation variants are observed for L2.”
Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization (2014, background): “So as in previous works,14,18,26,60 the CDRs, other than H3, form canonical conformations, which, when identified correctly, provide high-quality backbone atom coordinates for the loop.”
Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server (2014, background): “…and others have analyzed the known structures of antibodies 33–35 , compiled an exhaustive repertoire of canonical structures and their sequence determinants 32–42 and investigated the relationship between the sequence of the light and heavy chain variable domains and their packing modes 21,43–46 .”
Antibody Structure and Function: The Basis for Engineering Therapeutics (2019, methodology): “Symmetrical CDRs, where the N- and C-terminal residues are opposite each other in the structure, were used for the purpose of canonical structure classification [38] as implemented in the PyIgClassify database [101].”
RosettaAntibodyDesign (RAbD): A general framework for computational antibody design (2017, methodology): “These variable residues form a loop physically in contact with CDR1 and are occasionally observed making contacts with antigen [53,84].”
Improving Loop Modeling of the Antibody Complementarity-Determining Region 3 Using Knowledge-Based Restraints (2016, methodology): “[10] defined seven canonical torso conformations from experimentally-determined antibody structures.”
Accurate structure prediction of CDR H3 loops enabled by a novel structure-based C-terminal constraint (2016, methodology): “With few exceptions, RosettaAntibody selects templates for the framework regions and the non-H3 CDR loops that have backbone atomic coordinates with subangstrom optimal root mean square deviations (RMSDs) from the native structure.”
Improved prediction of antibody VL-VH orientation. (2016, background): “Many structural studies of the FV have focused on the conformation of the CDRs, particularly CDR H3 (Al-Lazikani et al., 1997; North et al., 2011; Wang et al., 2013; Weitzner et al., 2015; Xu et al., 2015).”
A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes (2013, background): “,(44) but updated to include CDR3 loops as performed recently for antibody CDRs.(45) Along with improved simulation of flexible CDR loops, other areas of the binding interface with less pronounced conformational changes could also be addressed, such as the MHC helices, which have been observed to un”
Evaluation of template-based modeling in CASP13 (2019, methodology): “The score for a given dihedral was defined based on the metric previously used for protein dihedral analysis by North et al 26 (the squared length of the chord on a unit circle resulting from the angular deviation from the target), normalized to the range (0 … 1): For each residue present in both…”
A Review of Deep Learning Methods for Antibodies (2020, background): “Whereas other CDR loops exhibit much less variation and can even be reasonably separated into canonical structural clusters, the H3-encoding gene is actively mutated in isolation before being recombined with the rest of the gene sequence in a process called V(D)J recombination, which creates both se”
Modeling and docking antibody structures with Rosetta (2017, methodology): “Assign the CDR loops in your models to the CDR loop clusters described by North et al. 54 , using the same methodology as in Adolf-Bryfogle et al. 55 , and check whether the chosen templates are suitable.”
Repertoire Analysis of Antibody CDR-H3 Loops Suggests Affinity Maturation Does Not Typically Result in Rigidification (2017, neutral): “Protein Eng Des Sel (2012) 25(10):507–21.”