Computational Antibody Papers

Filter by tags
All
Filter by published year
All
TitleKey points

  • 2025-04-10

    DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability

    • Developability
    • Neural network that provides structural features for an antibody sequence that can be used to predict developability.
    • Models spacial charge map and spatial aggregation propensity - properties that are normally obtained by MD simulations.
    • They model ca. 20,000 paired sequences from OAS and perform MD simulations to calculate the properties the canonical way.
    • They use these features as labels to train a small neural network that achieves correlation 0.87 on average for each of the 30 features predicted.

  • 2025-04-10

    Computational tool for the early screening of monoclonal antibodies for their viscosities.

    • Developability
    • Introduced Spatial Charge Map - simple structural descriptor that correlates with viscosity measurements.
    • Prior studies have correlated pronounced negative surface patches on the antibody’s Fv domain with elevated solution viscosity.
    • The SCM score aggregates partial charges from the Fv and correlates them to viscosity readouts.
    • Pfizer Medi and Novartis contributed antibodies to benchmark.
    • A panel of IgG1 antibodies was selected and their viscosities were measured experimentally at high concentrations (around 150 mg/mL) under nearly identical formulation conditions (e.g., pH 5.8, temperature at 25°C).
    • In each of the cases the high viscosity abs had the highest SCM scores showing that this is a good way to go about predicting viscosity.

  • 2025-03-31

    Precise, Specific, and Sensitive De Novo Antibody Design Across Multiple Cases

    • protein design
    • Benchmarking of a proprietary antibody design algorithm.
    • The method generates novel antibodies against a target, for a specific epitopic constraint & can be used to re-design antibodies.
    • Altogether they find good affinity scfv binders for six targets for which they found a complex in the PDB, like PD1 and Her2.
    • The de novo antibody design methods were computationally benchmarked against a curated set of 32 experimentally resolved antibody–antigen complexes using metrics like the G-pass rate and orientation recovery (measured by Fw RMSD). This allowed the authors to compare their method (across different versions) against other approaches.
    • They compare against RFAntibody and dyMEAN but in the computational tasks - reproducing the orientation of an existing antibody.
    • Several rounds of biopanning are employed to enrich for high-affinity, target-specific binders from a pre-designed library and do not involve the introduction of new mutations.
    • They benchmark the developability properties such as monomericity, yield and polyreactivity to show that their antibodies have good properties.
    • They demonstrate that most of their designed binders have less than 50% H3 sequence identity to antibodies in the PDB.
    • Computational details and binders are not given.

  • 2025-03-31

    Learning the language of antibody hypervariability

    • language models
    • AbMAP - Language model transfer learning framework with applications to antibody engineering.
    • Authors address the process of dichotomy of language models in antibodies - either one uses a bare-bones protein model like ESM or only antibody model like Antiberty/IgLM. Normal protein models will not capture hypervariability of CDRs whereas antibody models would focus too much on the framework. They focus solely on CDRs + flanking regions as a solution.
    • They show their applicability to three off the shelf models with structure template finding as well as low-n generative modeling.

  • 2025-03-31

    IgCraft: A versatile sequence generation framework for antibody discovery and engineering

    • generative methods
    • language models
    • Novel generative model for antibodies that allows one to fill in, inpaint inverse fold etc.
    • The model employs Bayesian Flow Networks which is somewhat similar to diffusion.
    • The model is trained on data from OAS - unpaired data as a first pass and paired data as a second pass.
    • Models are benchmarked on a range of computational metrics, chiefly sequence recovery (for infiling/inverse folding).
    • Developability is checked by computational prediction of solubility (CamSol) and humanness (AbNativ)

  • 2025-03-31

    AI-Augmented Physics-Based Docking for Antibody-Antigen Complex Prediction

    • epitope prediction
    • docking
    • structure prediction
    • Benchmarking of the structure prediction/docking and co-folding methods for antibody design
    • Authors measure the impact of antibody-antigen model quality on the success rate of epitope prediction and antibody design.
    • For epitope prediction and antibody design they use a proxy measure of DockQ score - they call success when DockQ is better than 0.23, for antibody design they use a stricter threshold of 0.49.
    • Using these measures, AlphaFold3 comes out on top, and it would be successful roughly ~47% times.
    • THey introduce an approach where ProPOSE and ZDOCK decoys are refined using AlphaFold. With this combined protocol they reach success rates of 35% for epitope mapping and 30% for antibody design.

  • 2025-03-21

    How well do contextual protein encodings learn structure, function, and evolutionary context?

    • generative methods
    • protein design
    • Novel inverse folding algorithm, studying the effect of pretraining on the effectiveness of Antibody design
    • Authors check multiple inverse folding regimens, pretraining on general proteins, ppi interfaces and antibody-antigen interfaces and likewise finetuning on these.
    • They only use the backbone atoms (N,C,Ca), with special provisions for Cb.
    • They mask portion of the sequence and have the model guess its amino acids.
    • The 37% recovery at 100% masking appears slightly lower than the same feat for proteinMPNN.
    • Pretraining on antibodies still holds a signal towards antibody-antigen complexes, showing the power of such pre-training.

  • 2025-03-21

    Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

    • binding prediction
    • nanobodies
    • Review on computational methods applied to nanobodies.
    • The review covers databases, modeling and design methods.
    • Much room is given to conformational sampling with molecular dynamics
    • They highlight a special class of nanobodies, quench-bodies (Q-bodies) that can also detect small molecules alongside normal proteins.
    • The focus presented is chiefly on binder design, rather than fine-tuning other biophysical properties.

  • 2025-03-21

    NanoDesigner: Resolving the complex–CDR interdependency with iterative refinement

    • protein design
    • nanobodies
    • Novel pipeline for computational protein design of nanobodies
    • Several tools are collated and adjusted to nanobody case - IgFold for structure prediction, HDOCK for docking and ABDESIGN, DiffAb and dyMEAN for backbone/sequence prediction.
    • They chiefly perform computational validation showing the performance on the RMSD/DockQ (re-docking) and the amino acid recovery. Results indicate that focusing on nanobodies provides benefit.
    • The entire pipeline can be used for de novo design and optimization.

  • 2025-03-21

    NanoGen: A High-affinity Nanobody Generation Model with Guided Diffusion

    • protein design
    • nanobodies
    • Novel method for nanobody sequence re-design using quite a small network.
    • The model was pre-trained using a large-scale collection of nanobody sequences from the INDI dataset, heavy-chain antibody sequences from the OAS, and antibody complex structures from SabDab. For fine-tuning, affinity data was generated by with 17,500 nanobody–antigen interaction data points—7,500 generated via the ANTIPASTI model and 10,000 through random pairing—with a CD45 patent dataset used for testing. So all computational predictions are not real affinity points.
    • NanoGen uses a two-stage training framework with a shared encoder-decoder architecture based on CNN layers that learns sequence patterns via a Masked Language Modeling task. During generation, a guided discrete diffusion process, augmented with Discrete Bayesian Optimization, is employed to refine the sequence outputs for enhanced binding affinity.
    • The model was tested using sequence recovery (REC) and binding affinity improvement (pKD improvement). Benchmarking involved comparing NanoGen against baseline models such as ESM-2 650M, AbLangHeavy, and nanoBERT under both random masking and CDR-specific masking strategies on the CD45 patent dataset.