Computational Antibody Papers
Filter by tags
developability
Filter by published year
All
TitleKey points
2025-09-12
RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery
- binding prediction
- developability
- Combined in vitro/in silico method for optimization of binders.
- Start from a wild-type scFv (heavy chain), build a random-mutant library, FACS-sort on multiple antigens, deep-sequence bins + input, and use per-sequence enrichment (bin/library) as the supervised target for (antibody, antigen) training pairs.
- Train uncertainty-aware regressors (xGPR or ByteNet-SNGP) on those enrichment targets; run in-silico directed evolution (ISDE) from the WT, proposing single mutations and auto-rejecting moves with high predictive uncertainty while optimizing the worst-case score across antigens.
- Binding is protected by the multi-antigen objective + uncertainty gating during ISDE; risky proposals are discarded before they enter the candidate set.
- Filter candidates for humanness with SAM/AntPack and for solubility with CamSol v2.2 (framework is extensible to add other gates); final wet-lab set kept 29 designs after applying these filters and uncertainty checks.
- Beyond large in-silico tests, yeast-display across 10 SARS-CoV-2 RBDs shows most designs outperform WT; a representative clone (Delta-63) improves KD on 8/10 variants and competes with ACE2.
- Novel method to assess antibody immunogenicity.
- Created two reference libraries: a positive set from human proteins and antibodies (OAS + proteome) and a negative set from murine antibody sequences (OAS).
- Antibody sequences are fragmented into 8–12-mer peptides.
- Peptide fragments are scored: +1.0 if matching the positive reference, −0.2 if matching the negative reference.
- Validated on 217 therapeutic antibodies with known clinical ADA incidence, showing strong negative correlation between hit rate and ADA.
- On 25 humanized antibody pairs, ImmunoSeq correctly predicted reduced immunogenicity after humanization, consistent with experimental results.
2025-07-25
Tuning antibody stability and function by rational designs of framework mutations
- developability
- Mutational analysis of Trastuzumab framework (FW) regions to modulate antibody stability and function, moving beyond the traditional focus on CDRs.
- Authors evaluated antibody-specific language models (AbLang2, AntiBERTy, etc.), a general protein language model (ESM-2), and a structure-based Rosetta approach. While the language models showed limited utility in suggesting beneficial FW mutations, Rosetta provided more reliable predictions based on structural stability.
- Language model-derived mutation suggestions were generally less informative than Rosetta’s, which successfully identified stabilizing FW mutations not biased toward germline residues.
- Authors experimentally characterized selected mutants in vitro, assessing thermostability, antigen (HER2) binding, and functional effects such as ADCC and tumor cell viability. Some mutations preserved function, while others decoupled binding from downstream activity.
- Demonstration of generation of novel natural antibodies biased towards favorable biophysical scores.
- Developed a masked discrete diffusion–based generative model by retraining an ESM-2 (8M) architecture on paired heavy- and light-chain sequences from the Observed Antibody Space (OAS), using an order-agnostic diffusion objective to capture natural repertoire features.
- Built ridge-regression predictors on ESM-2 embeddings using experimental developability measurements for 246 clinical-stage antibodies, focusing on hydrophobicity (HIC RT) and self-association (AC-SINS pH 7.4), and achieved cross-validated Spearman’s ρ of 0.42 for HIC and 0.49 for AC-SINS.
- Showed that unconditionally generated sequences maintain high naturalness, scoring with AbLang-2 and p-IgGen language models to yield log-likelihood distributions comparable to natural and clinical repertoires.
- Applied Soft Value-based Decoding in Diffusion (SVDD) guidance during sampling to bias generation toward sequences with low predicted hydrophobicity and self-association, enriching the fraction of candidates in the high-developability quadrant.
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.
2025-04-10
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.
2025-02-20
ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains
- developability
- ngs
- Method to predict heavy light chain pairing.
- Heavy light chain pairing has long been posited to be random, or at the very least VERY promiscuous. Authors check that via training their model on different portions of the variable region and showing that there is signal where full sequences are used.
- Authors curated a set of ca. 233k positive heavy/light chain pairs from OAS. Negative samples were made by random shuffling - so they could occur in nature, just were not observed in this ds.
- They use Antiberta2 as a basis for training the classification model.
- The model achieves 0.75 and 0.66 ROC AUC on two test sets - so there seems to be some signal there.
- When the model is split between lamdbas/kappas, it does better - though lambda have signal for kappas (remember that lambda is a rescue rearrangement for not-working kappa).
- Naive B-cell pairs have less predictability than mature ones.
2025-02-03
Assessment and incorporation of in vitro correlates to pharmacokinetic outcomes in antibody developability workflows
- developability
- Nice developability dataset with associated computational modeling.
- A total of 334 antibodies were initially characterized, with a subset of 43 antibodies selected for in vivo pharmacokinetic (PK) assessment. These data points included high-throughput developability assays and various physicochemical measurements.
- A multivariate regression model, using Partial Least Squares (PLS) regression, was developed. This model combined multiple in vitro measures (nonspecific interactions, self-association, and FcRn binding) to predict in vivo clearance, significantly improving PK correlation over individual assays.
- Authors study 171 Roche clinical studies representing 28 drugs for their ADA incidence.
- Authors demonstrate that ADA is highly context-specific with non-trivial inter-drug variation and factors such as disease or mode of action impacting the incidence.
- They train a random forest model on T-cell epitope predictions and a model combined with non-epitope features. The extended model, including non-epitope features performs better than the one that is solely sequence-based.
2025-01-21
PROPERMAB: an integrative framework for in silico prediction of antibody developability using machine learning
- developability
- Computational framework to calculate descriptors correlating with certain developability features for early antibody screening.
- The framework calculates a number of sequence and structural descriptors.
- The correlations were demonstrated to bring value on a HIC and viscosity datasets.
- Exact calculation of descriptors takes time, so authors showed that it is possible to train a ML model to get the descriptors right away from sequence.