Jun 2025· Advancement of science· Vol 12· 9 citations· 1 references
Medicine
TL;DR
AntiBMPNN consistently outperforms AbMPNN, AntiFold, and ProteinMPNN in designing complementarity determining regions (CDR) 1‐3, yielding stronger binding affinities and confirming its potential to improve therapeutic antibody design.
Abstract
Antibodies are crucial for medical applications, yet traditional methods for designing sequences are inefficient. This study introduces AntiBMPNN, an advanced deep‐learning framework that leverages an antibody‐specific 3D dataset, a fine‐tuned message‐passing neural network (MPNN), a frequency‐based scoring function, and AlphaFold 3 to achieve highly accurate antibody sequence design. AntiBMPNN surpasses ProteinMPNN with a perplexity of 1.5 and over 80% sequence recovery. Its scoring function, combined with AlphaFold 3, effectively prioritizes sequences based on structural recovery, positional stability, and biochemical or complex properties. Experimental validation highlights a 75% success rate in single‐point antibody design. AntiBMPNN consistently outperforms AbMPNN, AntiFold, and ProteinMPNN in designing complementarity determining regions (CDR) 1‐3, yielding stronger binding affinities. For CDR1 of huJ3 (anti‐HIV nanobody), it achieves a half maximal effective concentration (EC₅₀) of 9.2 nM (nanomolar), better than ProteinMPNN (135.2 nM) and AntiFold (59.3 nM), and comparable to AbMPNN (6.6 nM). For CDR2 of the D6 nanobody (targeting CD16), AntiBMPNN reaches 0.3 nM, outperforming AbMPNN (2.3 nM), AntiFold (0.7 nM), and ProteinMPNN (0.7 nM). In CDR3 of huJ3, it achieves 1.7 nM, surpassing AbMPNN (51.2 nM), with no detectable activity from AntiFold or ProteinMPNN. These findings confirm that AntiBMPNN‐designed sequences for J3 and D6 outperform the originals, highlighting its potential to improve therapeutic antibody design.
CLDN18.2 is a promising tumor-specific antigen; however, the development of therapeutic antibodies against it is challenged by the need for simultaneous optimization of affinity and developability. To address this, we present cdrGPT, a deep learning framework based on GPT-2 for de novo generation of complementarity-determining region H3 (CDRH3) sequences. Our approach integrates pre-training on the Observed Antibody Space (OAS) database with structural templating derived from the known antibody zolbetuximab. Generated sequences were iteratively refined through rejection sampling and fine-tuned against a multi-parameter objective function encompassing predicted affinity and MHC class II binding risk. From an initial set of 50,000 sequences, this screening pipeline yielded 313 high-confidence candidates. Subsequent analysis using evolutionary scale modeling 2 (ESM2) embeddings, principal component analysis (PCA), and clustering revealed three structurally distinct clusters, with intra-cluster cosine similarities exceeding 0.99. Validation of seven representative sequences from the dominant cluster using AlphaFold3 confirmed high structural fidelity to the zolbetuximab template, demonstrating a root mean square deviation (RMSD) of 1.331 Å for the CDRH3 loop and positional deviations of less than 0.4 Å for key paratope residues. These results indicate that the designed variants preserve the core binding mode of the parent antibody. This study establishes a feasible pipeline for integrating AI-generated CDRH3 loops into functional antibody scaffolds, providing a foundation for the accelerated development of therapeutics targeting CLDN18.2 and other clinically relevant antigens.
Tao Qu, Lingyan Yuan, Wei-Ran Cui et al.· PLoS Computational Biology· 0 citations
This work designs a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes in antibody screening and drug residence time analysis.
G. Luo, Junkai Wang, Sizhe Zhang et al.· Bioinformatics· 0 citations
Accurate structural data in a standardized format is one of the key factors behind the success of machine learning (ML)‐based methods for protein design and structure prediction. However, their application to nanobody‐antigen complexes has lower success rates compared to globular protein complexes, partly due to the limited amount of high‐quality structural data. While several dedicated databases already exist, automated assembly pipelines frequently overlook various artifacts, which act as additional noise and limit the effectiveness of ML applications. Common issues include incorrectly defined antigen assemblies, redundancy bias, inclusion of crystal contacts, strained geometry due to crystal packing, as well as missing density or post‐translational modifications near the interface. To address these issues, we present Antigen‐VHH Interface Database (AVIDbase), a highly curated dataset of nanobody‐antigen structures. In addition to correcting structural artifacts, the dataset provides a nonredundant set of structures with standardized chain identifiers, harmonized metadata, and cleaned atomic coordinates in a ready‐to‐use format for ML applications. AVIDbase is available on GitHub (github.com/Novartis/AVIDbase) and Zenodo (doi.org/10.5281/zenodo.20488703).
Tadej Medved, J. Lah, G. Miličić et al.· Protein Science· 0 citations
DyAb is a pair-wise representation built on top of a pre-trained protein language model that achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across monoclonal antibodies targeting three different antigens.
J. Lin, Jennifer L. Hofmann, Andrew Leaver-Fay et al.· mAbs· 0 citations
DeepCGASPred is a cyclic GMP-AMP synthase (cGAS)-specific deep learning scoring function that integrates three-dimensional convolutional neural networks with multi-head attention mechanisms and composite structural descriptors, including Structural Protein–Ligand Interaction Fingerprints (SPLIF), hydrogen bond features, and extended connectivity fingerprints (ECFP).
Muhammad Junaid, Muhammad Zeeshan, Abbas Khan et al.· Discover Chemistry· 0 citations
The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible complex structures are available.
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
What does it take to trust AI-driven HVAC optimization? Our AI Model Factory combines agents, machine learning, reinforcement learning and deterministic checks in a governed workflow designed for messy, real-world building data. The post We built an AI factory for HVAC control appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
