Generation of structure-guided pMHC-I libraries using Diffusion Models
- URL: http://arxiv.org/abs/2507.08902v1
- Date: Fri, 11 Jul 2025 08:29:18 GMT
- Title: Generation of structure-guided pMHC-I libraries using Diffusion Models
- Authors: Sergio Mares, Ariel Espinoza Weinberger, Nilah M. Ioannidis,
- Abstract summary: We introduce a structure-guided benchmark of pMHC-I peptides designed using diffusion models conditioned on crystal distances.<n>This benchmark is independent of previously characterized peptides yet reproduces canonical anchor residue preferences.<n>We demonstrate that state-of-the-art sequence-based predictors perform poorly at recognizing the binding potential of these structurally stable designs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Personalized vaccines and T-cell immunotherapies depend critically on identifying peptide-MHC class I (pMHC-I) interactions capable of eliciting potent immune responses. However, current benchmarks and models inherit biases present in mass-spectrometry and binding-assay datasets, limiting discovery of novel peptide ligands. To address this issue, we introduce a structure-guided benchmark of pMHC-I peptides designed using diffusion models conditioned on crystal structure interaction distances. Spanning twenty high-priority HLA alleles, this benchmark is independent of previously characterized peptides yet reproduces canonical anchor residue preferences, indicating structural generalization without experimental dataset bias. Using this resource, we demonstrate that state-of-the-art sequence-based predictors perform poorly at recognizing the binding potential of these structurally stable designs, indicating allele-specific limitations invisible in conventional evaluations. Our geometry-aware design pipeline yields peptides with high predicted structural integrity and higher residue diversity than existing datasets, representing a key resource for unbiased model training and evaluation. Our code, and data are available at: https://github.com/sermare/struct-mhc-dev.
Related papers
- Robust Molecular Property Prediction via Densifying Scarce Labeled Data [51.55434084913129]
In drug discovery, compounds most critical for advancing research often lie beyond the training set.<n>We propose a novel meta-learning-based approach that leverages unlabeled data to interpolate between in-distribution (ID) and out-of-distribution (OOD) data.<n>We demonstrate significant performance gains on challenging real-world datasets.
arXiv Detail & Related papers (2025-06-13T15:27:40Z) - AlphaFold Database Debiasing for Robust Inverse Folding [58.792020809180336]
We introduce a Debiasing Structure AutoEncoder (DeSAE) that learns to reconstruct native-like conformations from intentionally corrupted backbone geometries.<n>At inference, applying DeSAE to AFDB structures produces debiased structures that significantly improve inverse folding performance.
arXiv Detail & Related papers (2025-06-10T02:25:31Z) - Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data [33.562685684224995]
cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for imaging near-native structural heterogeneity.<n>Here, we combine cryo-EM density maps with the rich sequence and biophysical priors learned by protein structure prediction models.<n>Our method, CryoBoltz, guides the sampling trajectory of a pretrained protein structure prediction model using both global and local structural constraints.
arXiv Detail & Related papers (2025-06-04T22:16:27Z) - Causal Discovery from Data Assisted by Large Language Models [50.193740129296245]
It is essential to integrate experimental data with prior domain knowledge for knowledge driven discovery.<n>Here we demonstrate this approach by combining high-resolution scanning transmission electron microscopy (STEM) data with insights derived from large language models (LLMs)<n>By fine-tuning ChatGPT on domain-specific literature, we construct adjacency matrices for Directed Acyclic Graphs (DAGs) that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO3 (SmBFO)
arXiv Detail & Related papers (2025-03-18T02:14:49Z) - Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms.
We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds.
Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
arXiv Detail & Related papers (2024-07-26T10:29:16Z) - SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction [3.406882192023597]
Accurate prediction of protein-ligand binding affinity is crucial for drug development.
Traditional methods often fail to accurately model the complex's spatial information.
We propose SPIN, a model that incorporates various inductive biases applicable to this task.
arXiv Detail & Related papers (2024-07-10T08:40:07Z) - SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive
Molecular Property Prediction [1.534667887016089]
We present a novel method for generating molecular fingerprints using multi parameter persistent homology (MPPH)
This technique holds considerable significance for drug discovery and materials science, where precise molecular property prediction is vital.
We demonstrate its superior performance over existing state-of-the-art methods in predicting molecular properties through extensive evaluations on the MoleculeNet benchmark.
arXiv Detail & Related papers (2023-12-12T09:33:54Z) - Leveraging Side Information for Ligand Conformation Generation using
Diffusion-Based Approaches [12.71967232020327]
Ligand molecule conformation generation is a critical challenge in drug discovery.
Deep learning models have been developed to tackle this problem.
These models often generate conformations that lack meaningful structure and randomness due to the absence of essential side information.
arXiv Detail & Related papers (2023-08-02T09:56:47Z) - Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates.
Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
arXiv Detail & Related papers (2023-06-20T14:21:58Z) - State-specific protein-ligand complex structure prediction with a
multi-scale deep generative model [68.28309982199902]
We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures.
Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
arXiv Detail & Related papers (2022-09-30T01:46:38Z) - From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning [40.83037811977803]
Dynaformer is a graph-based deep learning model developed to predict protein-ligand binding affinities.
It exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset.
In a virtual screening on heat shock protein 90 (HSP90), 20 candidates are identified and their binding affinities are experimentally validated.
arXiv Detail & Related papers (2022-08-19T14:55:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.