Chuance Suna,
Xiangyi Li
a,
Honglin Xu
a,
Yike Tang
b,
Ganggang Bai
b,
Yanjing Wang
a and
Buyong Ma
*ab
aEngineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China. E-mail: mabuyong@sjtu.edu.cn
bShanghai Digiwiser Biotechnology, Limited, Shanghai 201203, China
First published on 12th August 2025
Predicting Antibody–Antigen (Ab–Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset. In nanobody–antigen structure prediction, SAGERank, coupled with a protein dynamics structure prediction algorithm, slightly outperforms Alphafold3. Most importantly, our study demonstrates the real potential of inductive deep learning to overcome the small dataset problem in molecular science. The SAGERank models trained for antibody–antigen docking can be used to examine general protein–protein interaction tasks, such as T Cell Receptor-peptide-Major Histocompatibility Complex (TCR-pMHC) recognition, classification of biological versus crystal interfaces, and prediction of ternary complexes of molecular glues. In the cases of ranking docking decoys and identifying biological interfaces, SAGERank is competitive with or outperforms state-of-the-art methods.
The high expressive power of deep neural networks enables efficient training with a large amount of data.12 Enlarging the antibody–antigen dataset is certainly the right direction,5,13 and some approaches also use a combination of structure modeling and computational docking to create training data sets of antibody–antigen complexes. Still the problem stands: if we can use general protein–protein interaction data to study antibody–antigen recognition or vice versa? For the small-data challenges in molecular science, exploration of the latest advances in deep learning algorithms and new methods are needed.14
In the last few years, deep learning techniques have attracted much attention as a promising alternative to physicochemical based approaches.15 Compared to docking calculations, deep learning methods have improved the performance by learning the extracted features from protein–ligand complexes.16 They can automatically extract task-related features directly from data without handcrafted features or rules. Among many deep learning methods, various graph neural networks (GNNs) are especially suitable for questions related to protein structure and protein–protein interactions, as illustrated in a recent study on hierarchical graph neural networks for protein–protein interactions.17
Here, we tested the generalization of a Graph Sample and Aggregate Network (GraphSAGE) model, SAGERank initially trained for ranking antibody–antigen docking models using only an antibody–antigen dataset. Low-dimensional vector embeddings of nodes in large graphs have proved extremely useful as feature inputs for a wide variety of prediction and graph analysis tasks.18,19 However, most embedding frameworks are inherently transductive and can only generate embeddings for a single fixed graph. These transductive approaches do not efficiently generalize to unseen nodes (e.g., in evolving graphs), and these approaches cannot learn to generalize across different graphs.20 In contrast, GraphSAGE is an inductive framework that leverages node attribute information to efficiently generate representations on previously unseen data. Our work demonstrated that the SAGERank model trained for ranking antibody–antigen docking poses learned general features of protein–protein interaction and can be directly applied to different tasks including distinguishing antibody–antigen pairing, TCR–pMHC recognition, and non-antibody protein–protein interactions.
We have considered two types of networks, one based on amino acids represented by Ca coordinates (GCa) and another based on specific atoms in each amino acid (Gatom). Starting from the 3D structures of Ab–Ag complexes, the interface region is identified as a set of residues located within 10.0 Å of any residues of the other antibody or antigen (Fig. 1). Graph G is defined by V (node set), E (set of edges), and A (adjacency matrix). Therefore, the amino acids in the interface region and the atoms that make up the amino acids represent two different types of nodes. The residue-based and atomic node features are listed in Tables 1 and 2, respectively. Overall, 50-bit features and 28-bit features are used to describe the residue nodes and atom nodes, respectively.
Name of the feature | Full name | Description | Dimension |
---|---|---|---|
Type | Residue type | One-hot encoded | 20 |
Aliphatic | Residue aliphatic | One-hot encoded | 1 |
Aromatic | Residue aromatic | One-hot encoded | 1 |
Polar neutral | Residue polar neutral | One-hot encoded | 1 |
Charged | Acidic/basic charged | — | 2 |
Weight | Residue weight | — | 1 |
Pka | The negative of the logarithm of the dissociation constant for the –COOH group | — | 1 |
Pkb | The negative of the logarithm of the dissociation constant for the –NH3 group | — | 1 |
Isoelectric point | Residue isoelectric point | — | 1 |
Hydrophobicity | Hydrophobicity of the residue (pH = 2) | — | 1 |
Hydrophobicity | Hydrophobicity of the residue (pH = 7) | — | 1 |
PSSM | Position-specific scoring matrix | — | 20 |
SASA | Solvent-accessibility surface area | — | 1 |
Name of the feature | Full name | Description | Dimension |
---|---|---|---|
Type | Atom type | One-hot encoded | 10 |
Degree | Number of atomic connections | One-hot encoded | 6 |
TotalNumHs | Number of hydrogen atoms | One-hot encoded | 5 |
ImplicitValence | Atomic implicit valence | One-hot encoded | 6 |
Aromatic | Atomic aromatic | — | 1 |
The adjacency matrix is constructed using the following procedure. For the amino acid network GCa with N nodes, the adjacency matrix ACa has a dimension of N*N (eqn (1)). Within the ligand (antigen) and receptor (antibody) ACaij = 0 if the Euclidean distance dCaij between the i-th node and the j-th node is greater than 4.5 Å, and ACaij = 1 otherwise. In addition, we also take into account the specific properties of the antibody CDR region in the receptor where ACaij = 1 if the i-th node and the j-th node belong to the CDR loops, and ACaij = 0 otherwise. Between the receptor and ligand, ACaij = 1 if the distance dCaij between the i-th node and the j-th node is greater than 10 Å, and ACaij = 0 otherwise. For the atomic network Gatom with M nodes, the adjacency matrix Aatom has a dimension of M*M (eqn (2)). Within the receptor and ligand where Aatomij = 1 if atom i and atom j are connected by a covalent bond, and Aatomij = 0 otherwise. Between receptor and ligand Aatomij = 1 if the distance datomij between the i-th node and the j-th node is greater than 8 Å, and Aatomij = 0 otherwise. Intraprotein edges (4.5 Å) capture covalent bonds and close-range contacts, while interprotein edges (10 Å) accommodate larger interface distances typical of antibody–antigen interactions. This differentiation aligns with physicochemical principles and improves model accuracy.
![]() | (1) |
![]() | (2) |
Node aggregation and update follow the standard GraphSAGE algorithm. The core steps of GraphSAGE are neighbor sampling and feature aggregation. The forward propagation algorithm for GraphSAGE is as follows:
In the algorithm, the first for loop is used to traverse the number of layers, and the second for loop is used to traverse all nodes in the Graph. Sampling is performed on the neighbors of each node v to obtain Nv. Next, the embedding of neighbor nodes are aggregated through AGGREGATEk to obtain hN(v)k. Then hN(v)k is spliced with the current embedding hvk−1 of the target node, and assigned to hvk after nonlinear transformation, thereby completing an update of the target node v. When the outer loop (k = 1…K) ends, the node v will complete the information aggregation of k-order neighbors.
Hyperparameter | Setting |
---|---|
Epoch | 50 |
Batchsize | 512 |
Learning rate | 0.0003 |
Optimizer | Adam |
Loss function | Cross entropy loss |
The layers of GraphSAGE | 4 |
The hit-rate and success-rate are used to evaluate the performance of different scoring functions for ranking docked decoys. The hit-rate is defined as the percentage of near-native (models with iRMSD ≤ 4 Å) models in the top-ranked models for a specific complex:
![]() | (3) |
![]() | (4) |
As can be seen in SI Fig. 1-1, the model with atoms as nodes achieves much better ranking accuracy than that with amino acids as nodes. There are two factors contributing to the different performances. Firstly, the graph core and the interaction between interfaces can be more accurately represented and captured using a larger number of nodes in the atomic network Gatom than in the residue-based network GCa. Secondly, the atomic network Gatom captures essential physicochemical features underlying antibody–antigen recognition.
We then compared the SAGERank docking model with four leading scoring functions in ranking protein–protein docking poses: Zrank,22 Pisa,23 FoldX,24 and Rosetta25 (Fig. 2B, SI 1-2, 1-3 and Table 1-1). Clearly, the SAGERank docking model exhibits superior performance compared to other methods in the Ab–Ag docking decoys set. A detailed analysis of the success rates of the SAGERank docking model and Pisa in specific cases, such as the 7MLH case (SI Fig. 1-4) and the 2B42 case (SI Fig. 4-3), further validates its outstanding performance. In these instances, when we select the consensus hits from the top 200 poses predicted by both the SAGERank docking model and Pisa, we observe only a minimal number of false positive hits. This finding underscores the reliability and accuracy of our model in identifying true positive docking poses, even when compared to other leading scoring functions.
Because the proportion of positive and negative samples in the dataset is not balanced, it is more reliable to use the AUC for model evaluation in this case, with AUC reaching 0.82 in the test set (SI Fig. 2-1), slightly smaller than the AUC obtained in the docking pose ranking model. Fig. 3B shows the distribution of SAGERank model2 scores for positive and negative samples in test sets. The average scores for all positive and negative samples are 0.57 and 0.20, respectively. It is obviously seen from Fig. 3C that when the threshold is 0.3, the F1 score can achieve a maximum value of 0.74. Therefore, choosing a threshold of 0.3 on the dataset for determining whether an antibody–antigen can be recognized maximizes the performance of the SAGERank model2. Fig. 3E shows the confusion matrix of SAGERank model2 on the test dataset with a threshold of 0.3. SAGERank model2 accurately classified 9813 out of 11307 negative samples and 3685 out of 4642 positive samples.
We further tested the model to examine antibody–antigen complexes for three targets with a large number of structures available, including 222 structures for the SARS-CoV-2 target, 41 structures for the lysozyme target, and 367 structures for the HIV target. All these 630 positive samples were chosen to test the distinguishing ability of SAGERank model2. The recognition success rates of the model for three targets HIV, SARS-CoV-2 and Lysozyme are 77.7%, 65.3%, and 61.0%, respectively (Fig. 3D). Overall, the SAGERank model2 achieved a considerable success rate in determining whether antibodies and antigens can bind. It is interesting to compare the performance of the SAGERank docking model trained with antibody–antigen docking poses. The success rates of the docking model for three targets, SARS-CoV-2, HIV, and lysozyme, are 50%, 48.6%, and 70.0%, respectively (SI Fig. 2-3). It is worth noting that the recognition accuracy of the docking model for antigens and antibodies in the lysozyme group is higher than that of the specifically trained SAGERank model2. Finally, we constructed positive protein–protein and pseudo protein–protein binding datasets. It can be seen in SI Fig. 2-4B that the SAGERank docking model still has good distinction accuracy; it correctly classified 153 out of 230 positive protein–protein and 304 out of 438 negative protein–protein bindings.
We collected 153 pairs of TCR-pMHC1 ternary complexes from the STCRDab database. Unlike typical protein–protein or antibody–antigen complexes with diverse interfacial morphologies, TCR-pMHC1 ternary complexes not only share similar interfaces but also conserve a diagonal orientation, presenting a further challenge for accurate prediction. To generate negative samples with rational binding patterns, TCRs were randomly picked and aligned to other non-cognate pMHC1 complexes, followed by 1000 steps of OpenMM energy minimization to optimize interface conformations. Here, we employ a contact map format to illustrate the conformational differences at the interfaces of positive and negative samples. Using complex 3bnq (PDB ID) as an illustrative example, it becomes evident that, in comparison to the TCR-pMHC1 complex of negative samples, the TCR-pMHC1 complex of positive samples exhibits a stronger mutual contact between the peptide interface and the TCR interface (Fig. 4A). We compared the performance of the SAGERank docking model with two different types of networks, the atom-based (Gatom) features and the amino-acid-based (GCa) features. The results indicate that the GCa network better discriminates between homologous and non-homologous TCRs, with ROCAUC and PRAUC values of 0.6467 and 0.6739, respectively (Fig. 4C). The Gatom features exhibited a high false positive rate (Fig. 4B) and the difference in the scores between positive and negative samples is more pronounced (Fig. 4D). We attribute this to the highly conserved binding geometry of TCR-pMHC1 interfaces, which leads to nearly identical atomic-level graph representations across different complexes. These structurally similar inputs produce indistinguishable embeddings in the GNN, limiting the model's ability to discriminate binding specificity. In contrast, residue-level (GCa) features implicitly incorporate critical sequence information—such as physicochemical and evolutionary profiles of key peptide and CDR3 residues—effectively complementing the structural data. This observation further suggests that future models combining high-resolution structural features with explicit sequence-based information (e.g., amino acid propensities or positional motifs) could significantly improve TCR specificity prediction. In conclusion, our study demonstrates the potential of the SAGERank docking model, particularly when using amino-acid-based features, in accurately predicting TCR specificity for TCR-pMHC1 ternary complexes. This approach holds promise for accelerating the discovery of neoantigens and improving the efficacy of adoptive immunotherapy for cancer treatment.
After discovering the superior accuracy of the SAGERank docking model in protein docking ranking, we intended to employ this model for predicting binding sites and epitopes. The specific operational steps are illustrated in Fig. 5. Initially, two proteins or antibody–antigen pairs undergo unrestricted docking using Megadock (proteins do not specify binding sites and antigens do not specify epitopes), resulting in 2000 docking decoys. Subsequently, these decoys are ranked by the SAGERank docking model, and the top 100 decoys from the ranking are subjected to amino acid contact frequency analysis. By applying a specific threshold, we can determine the binding sites or epitopes of the protein or antigen.
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Fig. 5 The schematic diagram of binding sites prediction for protein using the SAGERank docking model. |
Thus, we selected 10 protein groups and 5 antigen groups for binding site and epitope prediction. As demonstrated in SI Fig. 4-6 and 4-7, which showcase representative examples from our test dataset, the SAGERank docking model achieves high predictive accuracy for both binding sites and epitopes. Through comprehensive analysis of amino acid frequency distributions and three-dimensional structural visualizations, our results reveal that the model successfully identified binding sites in 8 out of 10 protein cases (80% accuracy) and correctly predicted epitopes in 3 out of 5 antigen cases (60% accuracy). These findings not only validate the model's robust inductive capabilities but also highlight its strong generalization performance, positioning SAGERank as a reliable computational tool for structural immunology and protein interaction studies.
We then examined if SAGERank can discriminate biological protein–protein interfaces and “crystal interfaces” arising from packing in crystals using a commonly used DC dataset containing 90 biological and 71 crystal interface entries. DeepRank,26 PISA,27 and PRODIGY-crystal28,29 have shown the highest prediction performance in distinguishing crystal interfaces from biological ones in previous studies. The results using SAGERank are reported in Fig. 6 and SI 4-8. Fig. 6A illustrates protein structures with annotations for both biological and crystal interfaces. Fig. 6B shows a confusion matrix from SAGERank prediction, with 0 and 1 indicating crystal interfaces and biological interfaces, respectively. Fig. 6C presents the number of structures in different types within the commonly – used DC dataset, which contains 90 biological interfaces (in blue) and 71 crystal interfaces (in red). Comparative analysis (Fig. 6D) demonstrates that our SAGERank model achieves 80% accuracy in interface differentiation—comparable to PISA (79%) and superior to PRODIGY (74%), although slightly below DeepRank's 86% benchmark, which was specifically trained using the DC dataset.
We chose the molecular glue system FKBP12–rapamycin–FRAP as a paradigmatic case.30 (Fig. 7). We conducted a series of group deletions on rapamycin (the positive compound) to transform it into a compound (negative) incapable of binding two targets simultaneously. The flowchart depicting the overall process is presented in Fig. 7. In the first step 1, we employed the SAGERank docking model to predict the possible binding poses of protein 1 with protein 2. Subsequently in step 2, Autodock-vina was utilized to dock the molecular glue compound (MGC) near protein–protein binding sites, allowing full conformational flexibility of MGC, generating 10 MGC conformations. Next in step 3, Megadock was invoked to dock the complex composed of the protein 1–MGC binary complex with protein 2, generating 2000 ternary complex poses for each set of 10 MGC conformations. Finally in step 4, the ternary complex poses were ranked and screened using the SAGERank docking model to select the possible ternary complex structure. For each set of 10 MGC conformations, we selected the top ten ternary complexes (from 2000) based on the SAGERank docking model's ranking. We then calculated the structural differences between these complexes and the authentic ternary complex, defining those with RMSD values between protein 2 and the docking poses of less than 8 Å as native samples, i.e., the near native ternary complexes.
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Fig. 7 Reproduction of the molecular glue system (FKBP12-rapamycin-FRAP) by the SAGERank docking model. |
We counted the total near-native ternary structures in the top rankings (top 10) using SAGERank and Pisa (for comparison). The comprehensive statistical outcomes are presented in Table 4, with additional details available in SI Fig. 5-1. It is evident that the SAGERank docking model demonstrates a remarkable ability to distinguish between ternary complexes formed by positive and negative compounds. There are 10 rapamycin conformations generated using the positive compound, and we selected top 10 ternary complexes ranked by SAGERank and Pisa, leading to 100 ternary complex conformations selected for comparison. SAGERank correctly identified 30 positive structures, and mis-classified 2 negative complexes as positive. In comparison, Pisa only identified 10 positive structures, and mis-classified 6 negative complexes as positive. Thus, utilizing the SAGERank docking model, we accurately reproduced the structural configuration of this three-member complex. This underscores the superiority of the SAGERank docking model in accurately recognizing the interfaces formed between small molecular compounds and proteins, suggesting its possible application in the screening of molecular glue systems. The results not only enhance our understanding of molecular glue mechanisms but also hold promise for future drug design and discovery efforts.
Positive compound | Negative compound | ||||
---|---|---|---|---|---|
Compound conformations (native/total) | The number of native ternary complexes in top10 by the SAGERank docking model | The number of native ternary complexes in top10 by Pisa | Compound conformations (native/total) | The number of native ternary complexes in top10 by the SAGERank docking model | The number of native ternary complexes in top10 by Pisa |
Conf1(16/2000) | 2 | 1 | Conf1(43/2000) | 0 | 0 |
Conf2(21/2000) | 6 | 2 | Conf2(32/2000) | 1 | 0 |
Conf3(14/2000) | 1 | 2 | Conf3(40/2000) | 0 | 1 |
Conf4(20/2000) | 2 | 1 | Conf4(38/2000) | 0 | 0 |
Conf5(19/2000) | 2 | 1 | Conf5(31/2000) | 0 | 0 |
Conf6(6/2000) | 3 | 1 | Conf6(14/2000) | 0 | 0 |
Conf7(16/2000) | 4 | 1 | Conf7(11/2000) | 0 | 1 |
Conf8(34/2000) | 2 | 1 | Conf8(48/2000) | 0 | 3 |
Conf9(17/2000) | 5 | 0 | Conf9(22/2000) | 0 | 0 |
Conf10(10/2000) | 3 | 0 | Conf10(28/2000) | 1 | 1 |
All | 30 | 10 | All | 2 | 6 |
Next, we delved deeper into our analysis by selecting two targets for closer analysis. One of these is the versatile GTP-binding protein, which orchestrates a myriad of cellular processes, ranging from protein biosynthesis to intricate intracellular membrane trafficking. In recent times, the functions and underlying mechanisms of GTP-binding proteins have significantly broadened, with the small GTP-binding protein Ras garnering particular attention due to its pivotal role in neoplastic transformation.32,33 Leveraging X-ray crystallography, the crystal structure of the RAS–anti-RAS single domain complex (PDB ID: 2UZI) was obtained.32 As depicted in Fig. 8A, the genuine epitope of RAS encompasses approximately 18 amino acids. Notably, the position of the epitope predicted by the SAGERank docking model aligns almost perfectly with the actual epitope, achieving an impressive coincidence rate of 88.9%. Furthermore, we conducted a comprehensive evaluation of the top 50 complex structures prioritized by our model, as shown in Fig. 8B. The analysis revealed that 37 of these structures closely resemble the native conformation, while 13 deviate from it.
![]() | ||
Fig. 8 (A) The results of epitope amino acids predicted by the model; (B) the relationship between the ranking of each complex by the model and iRMSD. |
Another cancer target under investigation is Plasminogen Activator Inhibitor-1 (PAI-1), a prominent member of the serine-protease inhibitor superfamily, commonly referred to as serpins. PAI-1 serves as the primary inhibitor for both tissue-type and urokinase-type plasminogen activators, enzymes crucial for activating plasminogen.34 Recent research underscores PAI-1's central role in various age-related subclinical (such as inflammation, atherosclerosis, and insulin resistance) and clinical conditions (including obesity, comorbidities, and Werner syndrome). Structurally, PAI-1 is a single-chain glycoprotein composed of 379 amino acids, with a molecular weight of approximately 48 kDa. As the primary inhibitor of t-PA and u-PA, PAI-1 inactivates these plasminogen activators by cleaving a specific Arg–Val peptide bond within their protease domain.35 Intriguingly, PAI-1 possesses the capacity to form complex structures with two distinct types of nanoantibodies, suggesting the presence of at least two antigen epitopes. Fig. 9A showcases the predicted epitope of PAI-1 (the prediction result for the other epitope can be found in SI Table 1), encompassing 12 amino acids. Remarkably, the prediction aligns perfectly with the actual epitope, achieving a coincidence rate of 100%. When assessing the top 50 complex structures ranked by our model, we find that 41 resemble the native structure closely, while 9 deviate. Notably, even among the incorrect structures, the iRMSD remains below 10 Å, demonstrating the model's remarkable capacity to discern between positive and negative samples. These findings further validate the SAGERank docking model's accuracy and effectiveness in epitope prediction.
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Fig. 9 (A) The results of epitope amino acids predicted by the model; (B) the relationship between the ranking of each complex by the model and iRMSD. |
No | pdb_code | min_irmsd_sagerank1 | min_irmsd_af3_score |
---|---|---|---|
0 | 7nxx | 7.314 | 7.756 |
1 | 7q6c | 10.678 | 9.923 |
2 | 7sp8 | 1.534 | 1.478 |
3 | 7sqp | 1.261 | 11.214 |
4 | 7srk | 1.366 | 9.763 |
5 | 7uia | 5.065 | 11.331 |
6 | 7unz | 6.94 | 9.327 |
7 | 7usv | 7.054 | 9.201 |
8 | 7vke | 5.748 | 3.755 |
9 | 7vnb | 14.66 | 14.613 |
10 | 7voa | 9.798 | 10.108 |
11 | 7wn0 | 6.759 | 6.585 |
12 | 7wn1 | 7.43 | 3.818 |
13 | 7x2j | 12.05 | 4.654 |
14 | 7x2l | 1.083 | 0.708 |
15 | 7x2m | 14.155 | 13.73 |
16 | 7x7e | 9.89 | 10.548 |
17 | 7z1c | 2.455 | 4.038 |
18 | 7z1x | 12.887 | 12.485 |
19 | 7zkz | 4.457 | 10.298 |
20 | 7zml | 12.706 | 3.123 |
21 | 7zmm | 1.116 | 0.652 |
22 | 7zw1 | 7.34 | 6.999 |
23 | 8b17 | 0.781 | 0.646 |
24 | 8b8i | 1.194 | 0.728 |
25 | 8be2 | 1.023 | 0.624 |
26 | 8c3l | 0.952 | 1.118 |
27 | 8ce4 | 12.368 | 10.417 |
28 | 8dly | 14.274 | 9.857 |
29 | 8dqu | 5.333 | 5.317 |
30 | 8e0e | 2.008 | 2.584 |
31 | 8eln | 0.736 | 0.649 |
32 | 8emz | 8.607 | 4.053 |
33 | 8en3 | 4.103 | 4.186 |
34 | 8gni | 1.522 | 1.493 |
35 | 8h3x | 1.374 | 1.29 |
36 | 8 h3y | 11.851 | 15.079 |
37 | 8h5u | 18.321 | 16.717 |
38 | 8hxq | 4.308 | 4.877 |
39 | 8ido | 9.005 | 14.181 |
40 | 8ont | 5.184 | 7.891 |
41 | 8oud | 21.177 | 21.478 |
42 | 8pyr | 0.772 | 0.502 |
43 | 8qf5 | 1.172 | 1.158 |
44 | 8sk5 | 15.284 | 2.681 |
The overall performance metrics revealed interesting insights. While SAGERank showed a marginally better mean iRMSD (6.62 Å vs. 6.88 Å), AF3_score demonstrated superior median performance (5.32 Å vs. 5.75 Å), suggesting that AF3_score generally yields better predictions but is more susceptible to outliers. Notably, AF3_score achieved near-native predictions (iRMSD ≤ 2.0 Å) in 27.9% of cases, slightly outperforming SAGERank's 25.6% success rate.
A detailed examination of individual cases revealed distinct strengths and weaknesses for each method. AF3_score exhibited remarkable predictive capabilities for certain complexes, such as 8sk5 (improving from 15.284 Å to 2.681 Å) and 7x2j (12.05 Å to 4.654 Å), demonstrating its potential to identify highly accurate conformations missed by SAGERank. However, this method also showed significant inconsistencies, with notable failures in complexes like 7sqp (1.261 Å to 11.214 Å) and 7uia (5.065 Å to 11.331 Å). In contrast, SAGERank displayed more consistent performance, with only one case (8oud) exceeding 20 Å iRMSD.
The strong positive correlation (r > 0.7) between the iRMSD values from both methods indicates substantial agreement in their assessment of conformation quality. This correlation suggests that both scoring functions recognize similar structural features important for near-native docking predictions, with each method typically identifying comparable low-iRMSD conformations. These findings suggest that while AF3_score can provide superior predictions in specific cases, SAGERank offers more reliable performance overall. The complementary strengths of these methods highlight the potential value of a hybrid approach that could leverage the best features of both scoring functions to further improve docking accuracy (Fig. 10).
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Fig. 10 Comparison of SAGERank + deepconformer with alphafold3 in prediction of nanobody–protein recognition. |
Two major factors are responsible for the above difficulties. Firstly, although the principles of protein–protein interactions have been actively investigated during last two decades,2 we also increasingly realize the complexity of these interactions. Many PTMs for example, phosphorylation and glycans, exist and modulate protein–protein interfaces and interactions. Protein structures and conformations are dynamic,48–51 which adds a hidden dynamical element to protein–protein interaction. The dynamics is especially important for antibody–antigen recognition due to the highly flexible CDR regions in antibodies. Secondly, the traditional transductive learning limited the application of proteins with unusual sequence features and those with only small data available, which is the second major difficulty that need to be solved for the development of biological drugs.
Here we developed the SAGERank model to predict the structure of antibody–antigen complexes, using an antibody–antigen complex dataset. Then we thoroughly examined the generalization of SAGERank to broad protein–protein interaction prediction tasks. Overall, our model has demonstrated excellent performance. First, in the application of ranking docking decoys, the SAGERank docking model outperformed major existing scoring functions. SAGERank successfully predicted majority epitopes in a cancer target dataset. In nanobody–antigen structure prediction, SAGERank coupled with a protein dynamics structure prediction algorithm slightly outperformed Alphafold3.
Most importantly, our study demonstrated the real potential of inductive deep learning, coupled with atomic interaction features, to overcome the small-dataset problem in molecular science. A natural graph network with atoms as nodes can be formed at the interface between antibody–antigen complexes and protein–protein complexes to accurately capture the fundamental physicochemical features of amino acid interactions. As a result, even though SAGERank was trained using only a small antibody–antigen complex dataset, it can be extended to predict general protein–protein interaction problems, such as TCR-pMHC recognition, classification of biological versus crystal interfaces, and prediction of ternary complexes of molecular glues.
In summary, we have designed a reliable and efficient deep learning framework for accelerating research based on antibody–antigen 3D structures, with the potential to expand to general protein–protein interaction. In the future, we will examine models trained with larger datasets to fully explore SAGERank's potential.
The supplementary information file contains additional parameters of model training, including learning rate settings at different stages, and more experimental data such as detailed performance metrics of the model on various validation datasets. See DOI: https://doi.org/10.1039/d5sc03707g.
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