Computational Antibody Papers
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binding prediction
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2024
TitleKey points
2024-07-23
Atomically accurate de novo design of single-domain antibodies
- binding prediction
- protein design
- Novel method to design antibodies, validated on VHHs.
- The protocol includes an antibody-fine tuned RFdiffusion to create the coordinates for the backbone followed by creating the sequence using ProteinMPNN (not antibody-fine-tuned). Filtering by RoseTTafold2 can be used.
- Fine-tuning RFDiffusion includes giving the coordinates of the antigen, framework of the antibody and information on the epitope. Only the CDR coordinates and orientation of the framework/CDRs are designed.
- They fine-tuned RoseTTafold2 with the aim to filter out the RFdiffusion designs.
- The fine-tuned RoseTTafold2 can accurately distinguish correct and incorrect ab-ag pairs, but when hot-spot information is provided (aka local docking).
- The best designs range from micromolar and high nanomolar and roughly 1 in 100 designs works as intended : https://www.nature.com/articles/d41586-024-00846-7
2024-07-16
PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces
- binding prediction
- epitope prediction
- Geometric transformer that receives a single structure at input and annotates residues with likelihood that they are part of a binding site.
- The geometric transformer only uses the atom names, no mass, charge etc.
- Similar to convolution their geometric attention mechanism focuses on 8 nearest neighbors (3.2A) and then increases up to 64 nn (8.2A).
- They use ~300,000 chains from the PDB for training (!). This is because they have extracted all the bio assemblies at 30% sequence identity.
- They defined the most common atom names for all molecule types which gave them 79 elements. Interactions between these elements can be given as a 79x79 matrix.
- Interaction cutoff is taken as 5A.
- Pesto outperforms Scannet by a large margin, 0.93 vs 0.87 ROC AUC.
- In some cases, processing MD trajectories of unbound proteins with PeSTo identifies certain interfaces better than when PeSTo is run on the starting static structure.
2024-07-09
Investigating the Volume and Diversity of Data Needed for Generalizable Antibody-Antigen ∆∆G Prediction
- binding prediction
- They created a siamese EGNN, one given WT the other one mutant, with their difference being the ddG prediction.
- They used the AB-Bind dataset which consisted of 645 mutants from 29 complexes.
- They created a set of non-redundant antibody-antigen binders with 1475 complexes. They imposed 70% clustering on antigens.
- They mutated one complex per cluster and ran foldx resulting in 942,723 ddG foldX data points.
- On ABBind dataset they achieve a pearson correlation of 0.8 - however when they impose stringent CDR cutoffs the correlation drops dramatically, indicating overtraining.
- When they run the training on the synthetic dataset, it stops being sensitive to overtraining.
2024-07-09
Improved prediction of protein-protein interactions using AlphaFold2
- binding prediction
- docking
- Using AF2 they developed a pipeline to fold and dock proteins simultaneously. The pipeline shows good performance in distinguishing interacting and non-interacting proteins.
- Acceptable models are those with DockQ > 0.23. Success rate is defined as percentage of acceptable poses.
- The best version of their model achieves a 39.4 success rate.
- AlphaFold2 outperforms other docking methods.
- Using the number of Cb in contact (within 8A) or plDDT of the interface results in ROC AUC in the region of .9 distinguishing interacting and non-interacting proteins.
- As input they insert a chain break of 200 residues to model the interaction.
- They note that it is very important to create the right MSAs for AF2.
- As negative cases for interactions (non-interacting proteins) they employ data from Negatome.
2024-07-02
Surface ID: a geometry-aware system for protein molecular surface comparison
- binding prediction
- They draw from the Masif method in that they define a triangular mesh. Each vertex is encoded with physicochemical information and then each patch of a defined radius is encoded numerically.
- They teach overlapping patches to have similar embeddings as they are assumed to have overlapping functions as well.
- They employed contrastive learning, annotating patches as positive if they were within 1.5A from centered vertices and negative if they were centered on vertex 5A away.
- Their learned similarity distances cluster by curvature, hydropathy and charge.
- They compared Surface ID to structure based similarity measurement approaches with SurfaceID performing slightly better.
- They clustered the antibody-epitope patches simultaneously. It clustered the binding modes between HIV-1 GP120, two for influenza HA and one for SARS-CoV-2 RBD. Anti-ha clustered had same epitope but different paratopes showing that the algorithm distinguishes on that level
- They proposed a design scheme for antibodies. Look for similar epitopes by surface id and use the antibodies as putative binders to the query.
2024-07-02
AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information
- binding prediction
- Using a siamese network and Sabdab to predict antibody-antigen binding in a binary fashion.
- They clustered the antigens at 0.9 sequence identity. They assumed that similar antibodies from the same antigen group bind in the same manner. This resulted in 3,892 antigen pairs.
- They also created a dataset of covid specific antibodies with 9309 positive samples and 1710 negatives.
- They used tha CKSAAP encoding, but compared against others such as one-hot, pssm or their-own trained word2vec.
- They benchmark the different encodings and models to show that CKSAAP + CNN come out on top.
- Their siamese CNN with CKSAAP achieve a staggering .85 PR AUC.
- They introduce a classifier for specificity (binary) for antibody-antigen complexes.
- They employ data from sdab-db, 47 antigens and 365 antibodies.
- They extend the positive pairs set by assuming nanobodies would bind antigens with high sequence similarity.
- They create a negative set by looking at similarity of antibodies and antigens and shuffling them if they are below similarity threshold to the complex.
- They employ the k-mer gappe scheme as their embedding of choice.
- They run a test on several embedding schemes versus several classification schemes (e.g. RF, SVM).
- The best combination, k-mer gapped embedding and RF achieve accuracy ~90%.
2024-03-13
Efficient evolution of human antibodies from general protein language models
- language models
- binding prediction
- They introduced a set of single and double mutations based on most likely variants proposed by an ensemble of language models (ESMs). Most of the mutations not only didn’t remove binding ability, but actually improved it.
- They performed evolution with the ESM-1b language model and the ESM-1v ensemble of five language models (six language models in total)
- In the first round of evolution, they measured the antigen interaction strength by biolayer interferometry (BLI) of variants that contain only a single-residue substitution from wild-type.
- In the second round, they measured variants containing combinations of substitutions, where we selected substitutions that corresponded to preserved or improved binding based on the results of the first round.
- They performed these two rounds for all seven antibodies, measuring 8–14 variants per antibody in round one and 1–11 variants per antibody in round two
- Across all seven antibodies, they found that 71–100% of the first-round Fab variants (containing a single-residue substitution) retained sub-micromolar binding to the antigen, and 14–71% percent of first-round variants led to improved binding affinity (defined as a 1.1-fold or higher improvement in Kd compared to wild-type)
- Thirty-six out of all 76 language-model-recommended, single-residue substitutions (and 18 out of 32 substitutions that lead to improved affinity) occur in framework regions.
- They found that Fabs for 21 out of the 31 language-model-recommended, affinity-enhancing variants that we tested had a higher melting temperature (Tm) than wild-type, and all variants maintained thermostability (Tm > 70 °C).
- They tested for polyspecificity but there were no off the chart changes in the poly profile.
- Five out of 32 affinity-enhancing substitutions (~16%) involve changing the wild-type residue to a rare or uncommon residue
- Approach based on general protein language models consistently outperformed all baseline methods, including the antibody-specific ones (!).
2024-03-13
Protein-Protein Interaction Prediction is Achievable with Large Language Models
- language models
- binding prediction
- They developed a language model to predict protein protein interactions from sequence on the basis of a large language model.
- They Train protBERT to predict PPI.
- They use the BIOGRID dataset, where interactors are mapped if they are confirmed by two independent sources, such as two independent experimental techniques in two separate studies. In total they have 179,018 positive pairs.
- They use negatome 2.0 as a negative dataset. It relies on various sources such as manual curation from the literature or subunits from the PDB that do not interact with each other. Total of 3,958 pairs were used.
- They use ProtBERT-BFD to pretrain the model.
- They mapped each protein pair as [CLS] Protein A [SEP] Protein B [SEP], mapping the final output to binary.
- They achieve 92% accuracy on the test set.
- They also perform well on annotating negative binders as coming from different subcellular compartments. On positive samples in this dataset, the model was 85% accurate. On negative samples, SYNTERACT was only 38% accurate, classifying many negatives from subcellular compartment sampling as interactors.