Accurate prediction of CDR-H3 loop structures of antibodies with deep learning
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, School of Pharmaceutical Sciences, Tsinghua University, China
- Tsinghua Institute of Multidisciplinary Biomedical Research, Tsinghua University, China
- Department of Natural Language Processing, Baidu International Technology (Shenzhen) Co Ltd, China
Figures
Figure 1
Accuracy of AF2 on antibody modeling.
(a) Schematic for CDR heavy chain loops. (b) The CDR lengths of monoclonal antibodies (mAbs) (n = 47) and nanobodies (Nbs) (n = 78). The error bars represent the standard deviation of the data. (c) …
Figure 2 with 1 supplement
Accuracy of AF2 on different antibody regions.
(a) The performance of AlphaFold2 in DB1 relative to other methods after superimposing Fv backbones. (b) The performance of H3-OPT in DB1 relative to other methods after superimposing VH backbones. …
Figure 2—figure supplement 1
Local accuracy of AlphaFold2 prediction.
(a) Side-by-side comparison of backbone and CDR3 heavy atom root mean square deviations (RMSDs) for DeepAb and AlphaFold2 in DB1. (b) Side-by-side comparison of backbone and CDR3 heavy atom RMSDs …
Figure 3
Molecular dynamics (MD) generated conformations for benchmark target 7N0R.
(a) Comparison of CDR-H3 loops of MD (gray), AF2 (pink), and experimentally determined structure (cyan). (b) Root mean square fluctuation (RMSF) of antibody residues during simulation. CDR-H3 loop …
Figure 4
H3-OPT architecture.
(a) Schematic for dataset preparation. Structures were screened from the SAbDab database based on resolution and sequence identity. Clustering of the filtered, high-resolution structures yielded …
Figure 5
Template module and ablation studies.
(a) Side-by-side comparison of Cα-RMSDs of AF2 and IgFold for Sub1 (n = 52); color scale for data points reflects CDR3 length. AF2 outperformed IgFold for targets left of the dashed diagonal; IgFold …
Figure 6
PLM-based structure prediction module (PSPM) module.
(a) Side-by-side comparison of Cα-RMSDs for AF2 and IgFold, IgFold and H3-OPT in the Sub2 (n = 46) and Sub3 (n = 33) test sets, respectively. (b) Comparison of prediction accuracy between AF2 and …
Figure 7 with 1 supplement
Accuracy of CDR-H3 loop prediction by H3-OPT.
(a) The performance of H3-OPT in the test set (nmAbs = 119, nNbs = 12) relative to other methods. The RMSDCα of H3-OPT was significantly lower than other existing methods (p<0.001). (b) The …
Figure 7—figure supplement 1
Comparison of accuracy between AF2, H3-OPT, and tFold-Ab methods using the CAMEO 2022 benchmark dataset (Leemann et al., 2023).
The x-axis represents different targets, and y-axis represents Cα-RMSD values. RMSD, root mean square deviation.
Figure 8 with 1 supplement
Analysis of surface patches.
(a) Analysis of surface amino acids for predicted H3 loops. Y-axis represents average number of surface residues for H3 loops (n = 131). The surface residues of AF2 models are significantly higher …
Figure 8—figure supplement 1
Solvent-accessible surface area (SASA) analysis of predicted H3 loops.
The values represent the difference in SASA between H3 structures predicted by AF2 or H3-OPT and experimentally determined structures. Positive values indicate that the predicted structures have …
Figure 9 with 1 supplement
Accuracy of H3-OPT predictions of antibody–antigen interactions.
(a) Performance of H3-OPT in binding site prediction. comparison of prediction accuracy between H3-OPT and AF2 for antibody–antigen binding sites (n = 27). Box represents interquartile range (IQR); …
Figure 9—figure supplement 1
RMSDbackbone during production runs.
Author response image 1
Author response image 2
Author response image 3
Tables
Table 1
The root mean square deviation (RMSD) results of PM6D3 level re-ranking method on 14 same CDR-H3 antibodies.
| PDB ID | Ranked 0 RMSD | Lowest energy RMSD | Lowest RMSD | ΔRMSD* | ||
|---|---|---|---|---|---|---|
| 4kmt | 1.06 | 1.14 | 1.05 | –0.08 | ||
| 5i19 | 2.16 | 1.91 | 1.77 | 0.25 | ||
| 5i1l | 3.80 | 3.20 | 3.19 | 0.60 | ||
| 5i17 | 2.86 | 3.71 | 2.86 | –0.85 | ||
| 5i1d | 2.10 | 2.10 | 2.02 | 0.00 | ||
| 5i1c | 2.43 | 1.66 | 1.45 | 0.77 | ||
| 5i1a | 2.37 | 0.85 | 0.59 | 1.52 | ||
| 5i1i | 3.72 | 3.51 | 3.51 | 0.21 | ||
| 5i15 | 2.16 | 1.94 | 1.35 | 0.22 | ||
| 5i16 | 3.19 | 1.70 | 1.39 | 1.49 | ||
| 5i18 | 2.88 | 2.88 | 2.88 | 0.00 | ||
| 5i1e | 1.62 | 1.13 | 0.92 | 0.49 | ||
| 5i1g | 2.08 | 2.08 | 2.00 | 0.00 | ||
| 5i1h | 1.58 | 1.84 | 1.32 | –0.26 | ||
-
*
ΔRMSD was calculated by subtracting the RMSD of predicted model from the RMSD of Ranked_0 model.
Table 2
Accuracy of quantum mechanics (QM)-based re-ranking methods.
| Method | Freeze terminal Cα | CDR* | Phase | Ranked 0 RMSD | Lowest energy RMSD | Lowest RMSD | ΔRMSD |
|---|---|---|---|---|---|---|---|
| PM6D3 | Y | H3 | Gas | 2.64 | 2.76 | 2.16 | –0.12 |
| PM6D3 | N | H3 | Gas | 2.53 | 2.67 | 2.03 | –0.14 |
| PM6D3 | N | H1, H2, H3 | Gas | 2.50 | 2.64 | 2.00 | –0.14 |
| B3LYP | N | H3 | Gas | 2.66 | 2.87 | 2.30 | –0.21 |
| B3LYP | N | H3 | Water | 2.66 | 2.68 | 2.30 | –0.02 |
-
RMSD = root mean square deviation.
-
*
CDR means the energy of which loop is used to re-rank AF2 models.
Table 3
Accuracy of quantum mechanics (QM)-based optimization methods.
| Method | Freeze terminal Cα | Structure generation method | Phase | Ranked 0 RMSD | Lowest energy RMSD/opted RMSD | Lowest RMSD | ΔRMSD |
|---|---|---|---|---|---|---|---|
| PM6D3 | Y | / | Gas | 1.69 | 1.74/1.87 | 1.37 | –0.05 |
| B3LYP | N | / | Gas | 1.63 | 1.65/2.55 | 1.38 | –0.02 |
| B3LYP | N | / | Water | 1.63 | 1.58/2.25 | 1.38 | 0.05 |
| B3LYP | N | Boltzmann | Gas | 1.56 | 2.05 | 1.28 | –0.49 |
| B3LYP | N | Boltzmann | Water | 1.56 | 1.81 | 1.28 | –0.25 |
| B3LYP | N | Boltzmann, minimized | Gas | 1.56 | 1.96 | 1.28 | –0.40 |
| B3LYP | N | Boltzmann, minimized | Water | 1.56 | 1.84 | 1.28 | –0.28 |
-
RMSD = root mean square deviation.
Table 4
The accuracy of molecular dynamics (MD)-based CDR-H3 loop optimization in the 10 worst cases of AF2.
| PDB ID | Cα-RMSDRanked_0 | Cα-RMSDMD_opt | ΔCα-RMSD |
|---|---|---|---|
| 7n0r | 10.92 | 5.62 ± 0.97 | 5.30 |
| 3juy | 6.37 | 5.71 ± 0.23 | 0.66 |
| 5y80 | 6.61 | 7.59 ± 0.47 | –0.98 |
| 7a4t | 6.19 | 7.48 ± 0.29 | –1.29 |
| 4nzr | 6.57 | 7.73 ± 0.26 | –1.16 |
| 6xzu | 7.45 | 6.34 ± 0.94 | 1.11 |
| 6x05 | 6.32 | 7.48 ± 0.63 | –1.16 |
| 3c08 | 6.68 | 7.01 ± 0.11 | –0.33 |
| 4z9k | 9.04 | 8.01 ± 0.37 | 1.03 |
| 6oca | 7.61 | 8.01 ± 0.34 | –0.40 |
-
RMSD = root mean square deviation.
Table 5
Performance of H3-OPT with different protein language models (PLMs).
| RMSDCα (Å) | |
|---|---|
| H3-OPT | 2.24 ± 1.05 |
| AF2 | 2.85 ± 0.69 |
| ESM2 | 2.31 ± 1.13 |
| Without PLM | 2.41 ± 1.26 |
| AntiBERTy | 2.49 ± 1.42 |
| ProtTrans-T5 | 2.40 ± 1.28 |
-
RMSD = root mean square deviation.
Table 6
Comparison of binding affinities obtained from molecular dynamics (MD) simulations using AF2 and H3-OPT.
| PDB ID | AF2 (kcal/mol) | AF2 RMSDCα (Å) | H3-OPT (kcal/mol) | H3-OPT RMSDCα (Å) | AF2 (kcal/mol) | H3-OPT (kcal/mol) | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| MM/GBSA | MM/PBSA | MM/GBSA | MM/PBSA | |ΔMM/GBSA*| | |ΔMM/PBSA| | |ΔMM/GBSA| | |ΔMM/PBSA| | ||||
| 2ghw | –29.20 | –33.36 | 2.7 | –14.70 | –21.36 | 3.0 | 8.63 | 2.42 | 23.13 | 14.42 | |
| 2yc1 | –38.85 | –37.73 | 2.3 | –43.80 | –48.72 | 1.5 | 6.80 | 18.67 | 1.85 | 7.68 | |
| 3l95 | –29.59 | –53.35 | 2.5 | –47.22 | –68.86 | 2.5 | 23.60 | 11.44 | 5.97 | 4.07 | |
| 3u30 | –37.31 | –42.41 | 2.6 | –44.94 | –50.07 | 2.5 | 9.64 | 2.18 | 2.01 | 5.48 | |
| 4cni | –36.96 | –42.93 | 1.0 | –31.92 | –40.39 | 1.3 | 8.54 | 7.44 | 3.50 | 4.89 | |
| 4nbz | –36.59 | –43.79 | 1.9 | –59.61 | –54.23 | 0.6 | 10.17 | 3.30 | 12.85 | 13.74 | |
| 4xnq | –13.55 | –17.47 | 2.7 | –31.51 | –30.94 | 0.5 | 15.40 | 12.32 | 2.57 | 1.15 | |
| 4ydl | –52.51 | –74.25 | 4.8 | –49.17 | –73.57 | 3.6 | 6.82 | 6.53 | 10.15 | 7.21 | |
| 5e5m | –59.72 | –71.15 | 3.0 | –41.29 | –53.70 | 7.3 | 0.50 | 5.79 | 18.93 | 11.66 | |
| 5f7y | –61.76 | –69.46 | 2.7 | –60.33 | –69.43 | 1.4 | 3.95 | 6.38 | 5.38 | 6.41 | |
| 6kyz | –12.66 | –20.32 | 4.0 | –9.36 | –17.13 | 3.7 | 17.63 | 17.21 | 20.93 | 20.40 | |
| 6o9h | –39.53 | –43.45 | 2.8 | –52.27 | –57.51 | 0.6 | 10.45 | 13.31 | 2.29 | 0.74 | |
| 6pyd | –45.87 | –58.71 | 1.0 | –35.75 | –45.28 | 1.1 | 6.29 | 13.50 | 3.83 | 0.06 | |
| 6u9s | –36.54 | –48.66 | 1.0 | –39.79 | –44.80 | 1.3 | 14.35 | 10.42 | 11.11 | 14.28 | |
| Average | / | / | 2.6 | / | / | 2.4 | 10.20 | 9.35 | 8.89 | 8.01 | |
-
*
ΔMM/GBSA (or ΔMM/PBSA) was calculated by subtracting the MM/GBSA (or MM/PBSA) of predicted model from the MM/GBSA· (or MM/PBSA) of experimental structure.
Table 7
Features of the model.
Nres is the number of residues (Jumper et al., 2021).
| Feature and shape | Description |
|---|---|
| Amino acid type [Nres, 21] | One-hot representation of the input amino acid sequence (including 20 amino acids and unknown). |
| 3D coordinates [Nres, 3] | Cα coordinates of all AlphaFold2-predicted residues |
| Backbone torsion angles [Nres, 6] | Sine and cosine encoding of all predicted three backbone torsion angles. |
| Torsion angles mask [Nres, 3] | A mask indicating if the angle was presented in the predicted structure. |
| H3 residue mask [Nres, 1] | A mask indicating if the residue was located in H3 loop. |
| Pairwise distances [Nres, Nres, 39] | One hot representation of residue alpha carbon atoms distance. The pairwise distances ranging from 3.25 Å to 50.75 Å were put into 38 bins equally and the last bin contained any larger distances. |
| Pairwise amino acid type [Nres, Nres, 21] | One-hot representation of the input amino acid sequence. |
Table 8
Hyperparameters for H3-OPT models.
| Model | 2 | 5 | 1 | 3 | Best |
|---|---|---|---|---|---|
| Initial learning rate | 1–4 | 5–4 | 1–3 | 5–4 | 1–4 |
| Hidden layers | 64 | 64 | 64 | 64 | 64 |
| Iterations numbers of Evoformer-like layer | 6 | 6 | 4 | 4 | 4 |
| Average RMSDCα (Å) | 2.42 | 2.36 | 2.35 | 2.33 | 2.24 |
-
RMSD = root mean square deviation.
Table 9
Average Cα-RMSDs of our test set under different confidence cutoffs.
| Cutoff | Cα-RMSD (Å) |
|---|---|
| 0.70 | 2.46 |
| 0.75 | 2.30 |
| 0.80 | 2.24 |
| 0.85 | 2.17 |
| 0.90 | 2.29 |
| 0.95 | 2.28 |
-
RMSD = root mean square deviation.
Author response table 1
| PDBID | AF2(kcal//mol) | AF2 RMSD_(Cu) (A) | H3-OPT(kcal//mol) | AF2(kcal//mol) | H3-OPT(kcal//mol) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| MM/ GBSA | MM/ PBSA | MM/ GBSA | MM/ PBSA | ("Å") | |/_\MM//GBSA^(**)| | | /_\MM//PBSA∣ | [ /_\MM//GBSA] | |/_\MM//PBSA| | ||
| 2ghw | -29.20 | -33.36 | 2.7 | -14.70 | -21.36 | 3.0 | 8.63 | 2.42 | 23.13 | 14.42 |
| 2yc1 | -38.85 | -37.73 | 2.3 | -43.80 | -48.72 | 1.5 | 6.80 | 18.67 | 1.85 | 7.68 |
| 3195 | -29.59 | -53.35 | 2.5 | -47.22 | -68.86 | 2.5 | 23.60 | 11.44 | 5.97 | 4.07 |
| 3u30 | -37.31 | -42.41 | 2.6 | -44.94 | -50.07 | 2.5 | 9.64 | 2.18 | 2.01 | 5.48 |
| 4cni | -36.96 | -42.93 | 1.0 | -31.92 | -40.39 | 1.3 | 8.54 | 7.44 | 3.50 | 4.89 |
| 4nbz | -36.59 | -43.79 | 1.9 | -59.61 | -54.23 | 0.6 | 10.17 | 3.30 | 12.85 | 13.74 |
| 4xx nq | -13.55 | -17.47 | 2.7 | -31.51 | -30.94 | 0.5 | 15.40 | 12.32 | 2.57 | 1.15 |
| 4ydl | -52.51 | -74.25 | 4.8 | -49.17 | -73.57 | 3.6 | 6.82 | 6.53 | 10.15 | 7.21 |
| 5e5m | -59.72 | -71.15 | 3.0 | -41.29 | -53.70 | 7.3 | 0.50 | 5.79 | 18.93 | 11.66 |
| 577 y | -61.76 | -69.46 | 2.7 | -60.33 | -69.43 | 1.4 | 3.95 | 6.38 | 5.38 | 6.41 |
| 6kyz | -12.66 | -20.32 | 4.0 | -9.36 | -17.13 | 3.7 | 17.63 | 17.21 | 20.93 | 20.40 |
| 609h | -39.53 | -43.45 | 2.8 | -52.27 | -57.51 | 0.6 | 10.45 | 13.31 | 2.29 | 0.74 |
| 6pyd | -45.87 | -58.71 | 1.0 | -35.75 | -45.28 | 1.1 | 6.29 | 13.50 | 3.83 | 0.06 |
| 6u9s | -36.54 | -48.66 | 1.0 | -39.79 | -44.80 | 1.3 | 14.35 | 10.42 | 11.11 | 14.28 |
| Average | 1 | I | 2.6 | l | 1 | 2.4 | 10.20 | 9.35 | 8.89 | 8.01 |
