Computational Antibody Papers
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2025-04-10
DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity
- Developability
- CNN surrogate for costly SCM calculations to correlate with viscosity.
- Developed a shallow CNN (tens of thousands of params) to correlate with calculation of SCM using MD simulations - that are inherently slow.
- Correlation between CNN and MD-derived is ca. 0.8.
- The CNN’s output, when translated into a viscosity prediction (via a correlation with SCM score), achieves a reasonably high correlation with experimentally measured viscosity values—again, with correlation coefficients in the range of 0.7 to 0.8.
2025-04-10
Accelerating high-concentration monoclonal antibody development with large-scale viscosity data and ensemble deep learning
- developability
- Viscosity prediction of high-concentration antibodies.
- Antibodies at low volume high concentration are needed for subcutaneous injections, which poses issues when antibodies are high-viscosity.
- They measured 229 mabs in 20mm histidine H-CL formulation, ph 6.0 at 150 mg/mL
- 162 were low viscosity (<20 cP) and 67 high viscosity (>20 cP)
- They employed the dataset to perform binary classification of the high and low viscosity data points
- They used structural features obtained from DeepSP for featurazition as well as one-hot encoded sequence.
- They used a set of predictive models, including simple neural networks, random forests, logistic regression and others.
- Their method performs well on independent test sets achieving >80% accuracy.
- In comparison to other methods that can be used as proxy (DeepSCM, SHARMA, TAP), their method does the best.
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.
- 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.