Computational Antibody Papers
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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.
2025-01-21
Antibody affinity engineering using antibody repertoire data and machine learning
- binding prediction
- ngs
- Novel experimental/computational workflow that demonstrates how little data might be needed to develop antibody affinity predictors.
- Mice were immunized with hen egg white lysozyme and via computational procedure of clustering with known binders 35 antibodies were characterized together with their affinities.
- These 35 antibodies were used to train the methods: Gaussian Process (GP) models with Matern and RBF kernels, Kernel Ridge Regression (KRR), Random Forest (RF) and Linear Regression (used as a baseline).
- Seed sequences were point or double-mutated and their affinity predicted using GP (that performed the best). Eight mutants predicted to span the whole range of affinities were selected for experimental testing and they had very good agreement with the predictions.
2024-07-02
Insights into next generation sequencing guided antibody selection strategies
- ngs
- annotation/numbering
- Tour de force of impact of NGS sequencing depth and clustering on picking hits from display campaigns. Repository of information for a detailed walk-through of a display campaign.
- Altogether NGS is big help with respect to random colony picking - better binders can be produced and larger epitope diversity.
- Campaigns against three (related) antigens, sars-cov-2 trimer protein, monomer s1 and RBD.
- They define a set of research questions on the relation of NGS statistics and kinetics - e.g. is higher frequency in NGS correlating with higher affinity?
- Sequences for VH and VL of 200 unique antibodies were synthesized, cloned into expression vectors for mammalian IgG, and subsequently expressed and purified as complete IgG molecules. Out of the 200 antibodies tested, 169 (84.5%) exhibited affinities <1 µM for RBD, S1, or the trimer. The selection of these 200 distinct antibody sequences was based on 57 well-defined clusters, which were identified at the convergence of three target populations (41 clusters), exclusive to either the S1 (1 cluster) or RBD (1 cluster) populations, or originating from 14 clusters derived from the trimer NGS population. The selection criteria considered the most abundant representative per cluster, regardless of whether they intersected with S1 or RBD.
- Clustering methods used were 100% identity, clonotyping and their own unsupervised clustering (abscan!).
- Abscan is based on in-house usupervised method
- The Abscan clustering method typically results in higher diversity, relative to traditional clonotyping.
- The abundance of the top representative in each AbScan cluster gives best correlation to binding affinity.
- One of chief advantages of clustering is identifying sequences within cluster of interest with lower number of liabilities.
- They trained an XGBoost method on NGS statistics etc. to discriminate binders and non-binders (though the dataset is very small)
- Same cluster = same epitope
- They employ abundance threshold of 0.005% using concatenated CDRs as a basic way to discriminate binders and non-binders.
- Abscan can be described (in high level) as follows: They utilize an unsupervised machine learning approach to cluster specific regions of interest, such as HCDR3. This clustering process is based on various sequence-related properties, NGS statistics (including relative abundance and round-to-round enrichment) pertaining to different regions of interest (HCDR3, HCDR3 + LCDR3, concatenated CDRs), and employs diverse algorithms (such as the Elbow method, Ordering Points to Identify the Clustering Structure (OPTICS), physicochemical reduction of the amino acid space, traditional clonotyping, and Levenshtein distance (LD)).
2024-06-25
Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind the same epitope
- structure prediction
- ngs
- An update on SPACE1, employing ABodyBuilder2. Better coverage of the structural method.
- They used binders against coronavirus, ebola, lysozyme among others.
- Structures are modeled using ABodyBuilder2. Structures are sorted by CDR lengths, frameworks aligned by Cas and RMSD calculated for CDR loops. Finally a clustering algorithm is used.
- The clustering algorithms benchmarked were DBSCAN, OPTICS-xi, OPTICS-DBSCAN, K-means, Butina clustering, greedy clustering.
- Two more variants were developed, SPACE2-HC, for heavy chains only as well as SPACE2-Paratope, for paratyping.
- Two accuracy metrics were used, the fraction of epitope-consistent clusters (number of epitope-consistent multiple-occupancy clusters / number of multiple-occupancy clusters) and the fraction of clustered antibodies in epitope-consistent clusters (number of antibodies in epitope-consistent multiple-occupancy clusters / number of antibodies in multiple-occupancy clusters)
- Two coverage metrics were used, the number of multiple-occupancy clusters and the number of antibodies in multiple-occupancy clusters were used. In order to examine accuracy and coverage with one measure they calculated the number of antibodies in consistent multiple-occupancy clusters
- They selected agglomerative clustering as best, though it is not better than Optics-XI, but it was providing larger clusters.
- Space2 using all loops was better than SPACE2-HC or SPACE2-paratope
- Space2 improves the coverage over SPACE1, thanks to the ABodyBuilder2 protocol.
- Space2 increases coverage with respect to just clonotyping, but clonotyping remains much more accurate.
- Antibody diversity treatise, arguing that repertoire cannot possibly be ‘that big’, rather there is some, yet unknown commonality across independent repertoires.
- Human body has 10^11 B-cells.
- B-cells are produced at a rate of 10^9 per day but majority are removed due to self-reactivity etc.
- Naive B-cells are estimated at 10^15.
- The number of pathogenic species thought to be infectious for humans has been estimated at ~1400
- It would be not feasible for an organism to go through 10^15 possible antibodies in mounting an immune response.
- Author suggests that the antibody repertoire is highly redundant.
- Author suggests that the N individuals have different but significantly overlapping fractions, M1-n
- Author suggests that one should identify convergent motifs responsible for responses.