Computational Antibody Papers
Filter by tags
protein design
Filter by published year
All
TitleKey points
2026-04-24
Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design
- protein design
- Protein design model applied to antibodies and lab-tested.
- Protenix-v2 is an integrated biomolecular modeling system that enables high-accuracy structure prediction, zero-shot generative binder design, and improved ligand-related plausibility.
- The system incorporates refined architecture and training optimizations, while strictly excluding all wwPDB entries released on or after September 30, 2021, to prevent data leakage.
- Performance was assessed using DockQ success rates on antibody-antigen interface benchmarks, BLI-confirmed hit rates across diverse soluble and membrane-protein targets, and PoseBusters-style chemical validity metrics
2026-04-17
Agent-guided de novo design of nanobody binders against a novel cancer target
- agents
- protein design
- Workflow for de novo nanobody design: Establishing an integrated computational-experimental pipeline for single-domain antibody discovery.
- Selected a novel target for Desmoplastic Small Round Cell Tumor (DSRCT) with no prior structural or antibody data.
- Used an AI agent to synthesize bioinformatics tool outputs and recommend 8 binding hotspots.
- Employed RFantibody, mBER, and IgGM to generate 288,000 unique candidates.
- Nominated 100,000 designs via Pareto-based filtering for yeast surface display and FACS enrichment.
- 116 enriched candidates were characterized by SPR, yielding 46 confirmed binders (39.7% hit rate) with affinities as low as 0.66 nM.
- Evedesign, an open-source, method-agnostic framework that standardizes biosequence design by enabling different machine learning models (sequence, structure, and evolutionary) to work together in a single workflow.
- It works by framing design as a conditional modeling problem using three composable operations: Generate (creating new sequences), Score (predicting fitness or likelihood), and Transform (mapping between representations like sequence-to-structure).
- The authors did not perform new wet-lab experiments; instead, they tested the framework by computationally reproducing previous studies, showing ESM-2 and ProteinMPNN could successfully rank and prioritize known beneficial mutations from existing antibody datasets.
2026-03-27
Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design
- nanobodies
- protein design
- agents
- First autonomous nanobody design agent.
- Prompted by high-level goals: It translates natural language objectives, like "inhibit X interaction with Y", into complete design campaigns.
- The agent queries literature/databases, uses bioinformatics tools, and prompts the user for specific strategic clarifications.
- 56x expert-level speedup, by compressing weeks of expert research and computational tasks into hours by automating reasoning-intensive steps.
- In lab tests, it successfully generated functional binders for 6 out of 9 attempted targets.
2026-03-20
Scaling Atomistic Protein Binder Design with Generative Pretraining And Test-time Compute
- protein design
- Novel generative framework to design protein binders from NVIDIA.
- Antibodies/nanobodie are not singled out for analysis.
- First framework to unify generative modeling with hallucination-based optimization, allowing for a strong generative prior to be steered by inference-time compute.
- The authors introduced Teddymer, a dataset of ~510,000 synthetic dimers created from AlphaFold predicted domain-domain interactions to overcome the scarcity of experimental multimer data.
- The model uses advanced search algorithms, including Beam Search, Feynman-Kac Steering, and MCTS, to navigate the generative space and find high-quality binders.
- It achieved state-of-the-art results on protein targets, small molecules, and enzyme design tasks, consistently outperforming baselines like RFDiffusion and BindCraft.
- No Wet-Lab testing. Hopefully just yet.
2026-03-20
Programming Biomolecular Interactions with All-Atom Generative Model
- nanobodies
- protein design
- AnewOmni, foundation model that unifies the design of small molecules, peptides, and antibodies into a single framework.
- The team evaluated approximately 3,000 candidates for the "undruggable" KRAS G12D target by alternating between AnewOmni for CDR design and AlphaFold3 for structural validation.
- Out of 7 synthesized nanobodies, the model achieved a 75% success rate (3 out of 4) when using a conservative structural consistency filter.
- The most successful nanobody design demonstrated a high binding affinity with a Kd of 587 nM
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
2026-01-20
Origin-1: a generative AI platform for de novo antibody design against novel epitopes
- protein design
- De novo platform for epitope-specific antibody design against “zero-prior” targets, i.e. antigen sites with no known antibody–antigen or protein–protein complex structures and limited homology to previously solved interfaces.
- The method combines three tightly integrated components: AbsciDiff, an all-atom diffusion model fine-tuned from Boltz-1 to generate epitope-conditioned antibody–antigen complex structures; IgDesign2, a structure-conditioned paired heavy–light CDR sequence design model; and AbsciBind, a modified AF-Unmasked / AlphaFold-Multimer–based scoring protocol using ipTM-derived interface confidence to rank and filter designs.
- The platform was evaluated on 10 zero-prior protein targets, with fewer than 100 antibody designs per target advanced to experimental testing; specific binders were successfully identified for 4 targets (COL6A3, AZGP1, CHI3L2, IL36RA).
- Experimental validation demonstrated both structural and functional accuracy, including cryo-EM confirmation at near-atomic resolution (DockQ 0.73–0.83) for two targets and AI-guided affinity maturation yielding a functional IL36RA antagonist with ~100 nM potency.
2026-01-09
Drug-like antibodies with low immunogenicity in human panels designed with Latent-X2
- protein design
- All-atom, zero-shot generative model that designs antibody sequence and structure directly in complex with a target from epitope-conditioned prompts.
- One specifies target, epitope, modality and the algorithm produces designs.
- They tested 4–24 designs per target, achieving 50% target-level success, producing VHHs and scFvs with pico- to nanomolar affinities (best ≈ 26 pM).
- Designed antibodies show therapeutic-grade developability (expression, aggregation, hydrophobicity, polyreactivity, stability) without optimization via wetlab validation.
- Human PBMC assays (10 donors) show no detectable immunogenicity for representative de novo nanobodies.
2025-12-12
Predicting Experimental Success in De Novo Binder 2 Design: A Meta-Analysis of 3,766 Experimentally 3 Characterised Binders
- protein design
- databases
- Large-scale benchmarking of structural, energetic, and confidence metrics to distinguish protein binders from non-binders.
- Curated 3,766 experimentally tested de novo binders across 15 targets from independent campaigns.
- Of these, 436 were confirmed binders, the remainder non-binders.
- Each design was re-modelled using AF2 (initial guess + ColabFold), Boltz-1, and AF3.
- From these predictions they computed 200+ structural and confidence descriptors.
- AF3-derived confidence scores (especially ipSAE_min) were the best single discriminators, although per-target precision still ranged widely (0.1–1.0), underscoring strong target dependence.