Computational Antibody Papers
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2026-05-22
Fast and Ultra-Capable Protein Design: Advancing the Frontier Through Atomistic SE(3)-Equivariance with Genie 3
- protein design
- non-antibody stuff
- Novel protein design model, revisiting the SE(3)architecture.
- Genie 3 is an all-atom, SE(3)-equivariant structure diffusion model that treats proteins as branched polymers to capture sidechain details, utilizing a Latent Transformer with bidirectional layer updates and an Invariant Point Attention structural decoder.
- The authors did not test the model on therapeutic formats such as antibodies or nanobodies; instead, they focused entirely on generating generic de novo protein binders, unconditional monomers, and functional motif scaffolds.
- The method was computationally benchmarked using self-consistency pipelines (ProteinMPNN/ESMFold), MotifBench for functional sites, and a strict AF2M+ binder interface metric, alongside real-world experimental validation that yielded a 12.5% hit rate against the Nipah virus Glycoprotein G
2026-02-05
Multiple protein structure alignment at scale with FoldMason
- non-antibody stuff
- structure prediction
- Protocol for ultra fast protein structure alignment.
- FoldMason represents protein structures as 1D sequences using a structural alphabet (3Di+AA), which allows it to perform multiple alignments using fast string comparison algorithms and a parallelized progressive alignment following a minimum spanning tree.
- It operates two to three orders of magnitude faster than traditional structure-based methods, achieving a 722x speedup over tools like MUSTANG and scaling to align 10,000 structures in a fraction of the time required by competitors for just 100.
- It matches the accuracy of gold-standard structure aligners and exceeds sequence-based tools, particularly in aligning distantly related proteins or flexible structures that global superposition-based methods struggle to handle.
- It is used for large-scale structural analysis of massive databases like AlphaFoldDB, building structure-based phylogenies for proteins that have diverged past the "twilight zone" of sequence similarity, and providing interactive web-based visualizations of complex MSTAs
2026-02-05
Biophysically Grounded Deep Learning Improves Protein–Protein ΔΔG Prediction
- binding prediction
- non-antibody stuff
- Method addressing binding prediction strength training on low data noisy dataset.
- The researchers address the issue that the field's standard benchmark, SKEMPI2, has significant hidden data leakage where different protein complexes share over 99% sequence identity, leading to inflated performance estimates in models that simply memorize these patterns. Problem raised by many, addressed by hardly any.
- ProtBFF injects five interpretable physical priors, Interface, Burial, Dihedral, SASA, and lDDT, directly into residue embeddings using cross-embedding attention to prioritize the most structurally relevant parts of a protein.
- By evaluating models on stricter, homology-based sequence clusters (60% similarity), the authors proved that ProtBFF allows general-purpose models like ESM to match or outperform specialized state-of-the-art predictors, even in data-limited "few-shot" scenarios.
2026-02-05
Iterative Epitope Expansion Enables Structure-Guided de novo Design of IgE-Binding Miniproteins Targeting the FcεRI Interface
- non-antibody stuff
- protein design
- Describing a protocol to design mini-binders for a multi domain not that well characterized target using Latent-X1 and to lesser extent Chai.
- The protocol used Latent-X1 to generate de novo sequences and initial poses, which were then refolded using Chai-1 to ensure the designs were structurally consistent and plausible.
- The final rank was determined by the equation score = 2.0 * Binder PTM - 0.1 * min-iPAE - 0.1 * complex RMSD. This formula prioritized high global confidence (PTM) while penalizing designs where the Latent-X1 pose and Chai-1 refolded structure disagreed (iPAE and RMSD).
- To handle the complex, multidomain IgE interface, they first designed binders against a smaller, stable seed on the epsilon3 domain before iteratively expanding the interface toward the full receptor-binding site.
- Out of hundreds of generated designs, fewer than 80 candidates across two rounds were selected for wet-lab testing, resulting in a 6% hit rate and the identification of three specific IgE-binding miniproteins
- Demonstration showing how large language models (LLMs) can be adapted to reduce the computational cost of molecular dynamics (MD).
- They use the FoldToken encoding to discretize protein 3D conformations into tokens compatible with Mistral, and fine-tune the LLM on short MD trajectories of a single state. The model is then able to generate new sequences of conformations by predicting the next frame from previous frames.
- After fine-tuning, the model can extend trajectories beyond the training data. Starting from a native state, it can discover alternative conformations potential for bypassing kinetic barriers that normally require long MD runs.
- The approach is system-specific (requires an MD trajectory for each protein), does not yet encode thermodynamics/kinetics explicitly, and relies on the choice of structural tokenization.
2025-07-03
Mask-prior-guided denoising diffusion improves inverse protein folding
- protein design
- non-antibody stuff
- Novel inverse folding algorithm based on a discrete diffusion framework.
- Unlike earlier methods that focused on masked language modeling (MLM) (e.g., LM-Design) or autoregressive sequence generation (e.g., ProteinMPNN), this work introduces a discrete denoising diffusion model (MapDiff) to iteratively refine protein sequences toward the native sequence. The method incorporates an IPA-based refinement step that selectively re-predicts low-confidence residues.
- Structural input is limited to the protein backbone only, represented as residue-level graphs. All-atom information is not used for either masked or unmasked residues.
- On the CATH 4.2 full test set, their method achieves the best sequence recovery rate of 61.03%, outperforming baselines such as: ProteinMPNN: 48.63% PiFold: 51.40% LM-Design: 53.19% GRADE-IF: 52.63%
- MapDiff also achieves the lowest perplexity (3.46) across models.
2025-04-28
Boltzdesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design
- non-antibody stuff
- protein design
- Protein design method based on Boltz-1.
- Boltz-1 is an open-source reproduction of AlphaFold3, which uses a diffusion module to co-fold molecular structures (proteins, ligands, etc.).
- For design purposes, BoltzDesign1 sidesteps the full structure generation step and instead uses only the Pairformer (which outputs a distogram — a probabilistic representation of all pairwise residue distances). This allows broader exploration of sequence space, as it optimizes over the distribution of possible structures rather than committing to a single conformation.
- Given a target (such as a small molecule or protein), they weakly initialize a binder sequence using random logits. This sequence is then iteratively refined by backpropagating loss through the Pairformer (and optionally through the Confidence module) to increase the predicted quality of the binder–target interaction.
- A full 3D structure can be generated at the end using the Boltz-1 structure module, but this is not part of the optimization loop.
- They benchmarked their method in silico on small molecule targets and a set of protein–protein interactions from the BindCraft benchmark, comparing performance to RfDiffusion All-Atom.
- BindCraft is an easy-to-use pipeline for computational protein binder design.
- It employs AlphaFold2-Multimer to hallucinate binders via backpropagation.
- Given a target structure and binder parameters (e.g., sequence length), the binder sequence is initialized with random logits and iteratively optimized via gradient descent through the AF2-Multimer network.
- After binder hallucination, the sequence and surface residues are further optimized using MPNNsol, and AF2-Monomer is used to repredict and filter high-confidence designs.
- Binder designs were validated experimentally through in vitro assays, X-ray crystallography, and cryo-EM.
- Reported success rates ranged from 25% to 100%, with most binders in the nanomolar affinity range, a few in the micromolar range, and backbone RMSDs of ~1.7 Å to 3.1 Å between design models and solved structures.
2025-04-28
Atom level enzyme active site scaffolding using RFdiffusion2
- protein design
- non-antibody stuff
- Improvement upon earlier RFDiffusion, enhancing stability and accuracy in designing enzyme active sites.
- Catalytic sites can now be specified at the atomic level instead of the residue backbone level used previously. This eliminates the need to explicitly enumerate side-chain rotamers.
- Training uses flow matching, a technique that simplifies and stabilizes the diffusion training process.
- Benchmarked on a set of 41 diverse enzyme active sites; RFdiffusion2 succeeded in all 41 cases, significantly outperforming the earlier RFDiffusion, which succeeded in only 16.
2025-04-28
Scaling unlocks broader generation and deeper functional understanding of proteins
- protein design
- non-antibody stuff
- Novel protein generative language model — ProGen3
- The model can do autoregressive generation N-to-C, C-to-N, and also supports span infilling.
- The architecture is a Transformer with a Sparse Mixture of Experts (MoE), activating about 27% of parameters per forward pass to improve computational efficiency.
- They studied how sampling affects training by trying different family-level weighting schemes. Uniform sampling across families (where small and large families have equal chance) gave better diversity and generalization, while unmodified sampling (letting big families dominate) performed worst.
- They validated the models by showing that generated proteins express well in wet lab experiments (split-GFP assays, spanning both highly novel and moderately novel sequence spaces).
- They used a large thermostability dataset to align model predictions to stability. This alignment is not standard fine-tuning — instead, preference optimization was applied, teaching the model to prefer sequences predicted to have higher stability. Upon experimental validation, aligned models indeed produced proteins with higher expression and stability.