Computational Antibody Papers
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2025-06-24
AbEpiTope-1.0: Improved antibody target prediction byuse of AlphaFold and inverse folding
- binding prediction
- structure prediction
- A novel method that repurposes AlphaFold-2.3 structure predictions and combines them with inverse folding–based machine learning models to assess antibody-antigen binding accuracy and specificity.
- They generate antibody-antigen complex models using AlphaFold-2.3 and evaluate them using the 'AbAgIoU' metric, which measures the overlap between predicted and true epitope/paratope residues — penalizing both missing and extra contacts.
- They demonstrate that the learned scores can distinguish true from incorrect antibody-antigen pairings (including swapped antibody scenarios), significantly outperforming random baselines.
- The method relies only on antibody and antigen sequences as input, using AlphaFold to model structures — making it applicable in real-world settings where experimental structures are unavailable.
2025-06-24
REPURPOSING ALPHAFOLD3-LIKE PROTEIN FOLDING MODELS FOR ANTIBODY SEQUENCE AND STRUCTURE CO-DESIGN
- generative methods
- protein design
- Novel method to design antibodies based on boltz-1.
- They added a sequence head to boltz-1 to perform simultaneous sequence/structure co-design.
- They employed data from SAbDab to fine-tune boltz-1 on antibody-antigen complexes.
- They compared to dyMEAN and DiffAB looking at amino acid recovery, RMSD and Rosetta InterfaceAnalyzer energy - their model does better on these computational benchmarks.
2025-06-24
Benchmark for Antibody Binding Affinity Maturation and Design
- binding prediction
- language models
- generative methods
- databases
- Benchmark of machine learning models for antibody-antigen binding affinity.
- A curated dataset of over 150,000 antibody-antigen complexes with associated experimental affinity values is compiled from literature.
- The benchmark compares a wide range of model types: language models, inverse folding models, graph-based, and diffusion-based generative models.
- Inverse folding models that are globally structure-aware perform best.
- General protein models like ESM-IF and ProteinMPNN outperform antibody-specific models such as AntiFold, DiffAb, and dyMEAN.
- Surprisingly, ESM-3 underperforms relative to ESM-IF, despite incorporating structural signals and improving upon earlier ESM models.
2025-06-24
NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores
- binding prediction
- nanobodies
- Introduced a novel machine learning method (NanoBinder) to predict the binding probability of nanobody-antigen structural complexes.
- Positive (binding) complexes were sourced from the SAbDab database, which contains experimentally validated nanobody-antigen interactions.
- Negative (non-binding) complexes were generated by structurally aligning nanobodies from different binding complexes (with RMSD < 2 Å) and recombining them with unrelated antigens to create likely non-binding pairs.
- Extracted Rosetta energy features from each complex and trained several machine learning models, including Random Forests, SVMs, AdaBoost, and Decision Trees, to classify binders vs. non-binders. Random Forests showed the best performance.
- They selected antibodies with known antigen targets (e.g., IL-6) and grafted their CDRs onto nanobody scaffolds using Rosetta-based protocols. The resulting nanobody-antigen complexes were evaluated in silico using NanoBinder, and selected candidates were experimentally validated. The predictions showed good correlation with binding outcomes, particularly for identifying non-binders.
- Novel LLM (MINT) that natively encapsulates protein protein interactions.
- MINT (Multimeric INteraction Transformer) extends the ESM-2 protein language model by incorporating a cross-chain attention mechanism. This allows it to process multiple protein sequences simultaneously while preserving inter-sequence relationships and contextual information critical for modeling protein-protein interactions.
- MINT was trained on a large, curated subset of the STRING database, consisting of 96 million high-quality physical protein-protein interactions and 16.4 million unique protein sequences. The training employed a masked language modeling objective adapted for multimeric inputs.
- MINT was benchmarked on several general protein interaction tasks including binary interaction classification, binding affinity prediction (PDB-Bind), and mutational impact prediction (e.g., SKEMPI and MutationalPPI). It consistently outperformed existing PLMs, achieving state-of-the-art performance on multiple datasets such as a 29% improvement over baselines in SKEMPI.
- MINT outperformed antibody-specific models (e.g., IgBert, IgT5, and AbMap) on the FLAB benchmark and SARS-CoV-2 antibody mutant binding prediction tasks. It showed >10% performance improvement on three FLAB datasets and a 14% gain in low-data settings (0.5% training data) for SARS-CoV-2 binding predictions.
2025-06-05
AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks
- developability
- generative methods
- protein design
- Novel generative modeling framework (AbBFN2) using Bayesian Flow Networks (BFNs) for antibody sequence optimization.
- Trains on sequences from Observed Antibody Space (OAS) combined with genetic and biophysical annotations, leveraging a denoising approach for both conditional and unconditional sequence generation. Targets include optimizing Therapeutic Antibody Profiler (TAP) annotations.
- Computationally validated for germline assignment accuracy, species prediction (humanness), and TAP parameter optimization.
- Combines multiple antibody design objectives into a unified, single-step optimization process, unlike existing software methods which are typically specialized for individual tasks.
2025-06-05
Adapting ProteinMPNN for antibody design without retraining
- protein design
- generative methods
- Novel method to bias ProteinMPNN for antibody design, without modifying model weights.
- Logits from protein-general ProteinMPNN and antibody-specific AbLANG are added and softmaxed. Addition of AbLANG is supposed to push the model into the antibody-acceptable space.
- On in-silico experiments ProteinMPNN+AbLang outperformed ProteinMPNN alone and rivalled antibody-specific AbMPNN.
- Authors designed 96 variants of Trastuzumab CDR-H3 using ProteinMPNN, AbLang and ProteinMPNN+AbLang each. AbLANG and ProteinMPNN produced 1 and 3 successful variants respecitively (both out of 96) whereas their combination produced 36 successful variants.
- None of the variants were better variants than WT Trastuzumab.
2025-06-05
PSBench: a large-scale benchmark for estimating the accuracy of protein complex structural models:
- structure prediction
- PSBench is a large benchmark dataset (>1M models) for training and evaluating model accuracy estimation (EMA) methods for protein complex structures, using data from CASP15 & CASP16.
- Models were generated by AlphaFold2-Multimer and AlphaFold3 under blind prediction conditions and annotated with 10 detailed global, local, and interface quality scores.
- The dataset enables development of advanced EMA methods (e.g. GATE), which showed top performance in blind CASP16 assessments.
- Fast and reliable numbering tool with an inbuilt free germline database, unifying functionalities of tools such as IgBlast, ANARCI etc.
- It can number both amino acid and nucleotide sequences.
- Rather than using statistical methods such as HMMs, MMSeqs-like methodology was used for rapid alignment.
- Alignments are more accurate than existing methods, with speed improvement, running on a CPU.
- Fast, alignment-based antibody numbering tool, significantly outperforming existing software in processing speed.
- Uses a simplified global alignment with a custom scoring matrix, facilitating rapid numbering of millions of sequences efficiently.
- Ensures accuracy comparable to established methods (ANARCI, AbNum) while numbering large-scale antibody datasets.
- Emphasizes interpretability and robustness, providing transparent sequence scoring useful for humanization tasks.