Biology subjects

King, J. E.

Publications and source records attributed to King, J. E..

4 recordsLinked to original sources

bioRxiv2026

Origin-1: a generative AI platform for de novo antibody design against novel epitopes

0Generative artificial intelligence has advanced antibody discovery, yet de novo design of therapeutic antibodies against targets with "zero-prior" epitopes remains a fundamental challenge. We define "zero-prior" epitopes as target sites lacking structural data from any reported antibody-antigen or protein-protein complex involving the target. Here we present Origin-1, a generative AI platform that overcomes this by integrating epitope-conditioned all-atom structure generation, paired complementarity determining region sequence design, and a specialized co-folding-based scoring protocol to select antibody designs predicted to be high-confidence, specific binders with favorable developability. We evaluated Origin-1 on a panel of ten targets selected to have no available protein-protein complex structures and minimal homology ([≤]60% sequence identity) to proteins with known complexes, creating stringent design conditions. In fewer than one hundred design attempts per target, we identified developable, specific antibodies, validated across multiple biophysical and developability assays, for four targets: COL6A3, AZGP1, CHI3L2, and IL36RA, with functional inhibition demonstrated for IL36RA. Cryogenic electron microscopy confirmed the atomic accuracy of our designs, revealing complexes that closely matched the computational models with high structural fidelity (3.0-3.3 [A] resolution; 0.83-0.91 DockQ). Furthermore, we employed AI-guided affinity maturation to optimize a de novo antibody binder against IL36RA, producing functional antagonists with sub-nanomolar affinities and a top EC50 of 12.3 nM. These results demonstrate a framework for targeting epitopes without structural precedent, expanding the programmable therapeutic antibody landscape.

molecular biology↗

bioRxiv2023

Interpreting Molecular Dynamics Forces as DeepLearning Gradients Improves Quality Of PredictedProtein Structures

Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a combination of increased accuracy and physical intuition. We propose a new method to train deep learning protein structure prediction models using molecular dynamics force fields to work toward these goals. Our custom PyTorch loss function, OpenMM-Loss, represents the potential energy of a predicted structure. OpenMM-Loss can be applied to any all-atom representation of protein structure capable of mapping into our software package, SidechainNet. We demonstrate our methods efficacy by finetuning OpenFold. We show that subsequently predicted protein structures, both before and after a relaxation procedure, exhibit comparable accuracy while displaying lower potential energy and improved structural quality as assessed by MolProbity metrics. SIGNIFICANCEWe propose a novel framework to directly incorporate forces from molecular dynamics as gradients for training deep learning models like AlphaFold2. We implement our method as a PyTorch loss function, OpenMM-Loss, which frames the potential energy of predicted protein structures as a minimization objective. When applied to OpenFold, our method demonstrates improved structural quality, lower potential energy, and comparable accuracy relative to the OpenFold baseline. Our framework may enhance the ability of deep learning models to recapitulate fundamental biophysical principles, reducing the number of structural irregularities in their predictions and paving the way for more effective downstream applications.

bioinformatics↗

bioRxiv2018

Genome-wide abundance of 5-hydroxymethylcytosine in breast tissue reveals unique function in dynamic gene regulation and carcinogenesis

5-hydroxymethylcytosine (5hmC) is generated by oxidation of 5-methylcytosine (5mC), however little is understood regarding the distribution and functions of 5hmC in mammalian cells. We determined the genome-wide distribution of 5hmC and 5mC in normal breast tissue from disease-free women. Although less abundant than 5mC, 5hmC is differentially distributed, and consistently enriched among breast-specific enhancers and transcriptionally active chromatin. In contrast, regulatory regions associated with transcriptional inactivity were relatively depleted of 5hmC. Gene regions containing abundant 5hmC were significantly associated with lactate oxidation, immune cell function, and prolactin signaling pathways. In independent data sets, normal breast tissue 5hmC was significantly enriched among CpG loci demonstrated to have altered methylation in pre-invasive breast cancer and invasive breast tumors. Our findings provide a genome-wide map of nucleotide-level 5hmC in normal breast tissue and demonstrate that 5hmC is positioned to contribute to gene regulatory functions which protect against carcinogenesis.

genomics↗

bioRxiv2017

DNA methylation differences at regulatory elements are associated with the cancer risk factor age in normal breast tissue

BackgroundThe underlying biological mechanisms through which epidemiologically defined breast cancer risk factors contribute to disease risk remain poorly understood. Identification of the molecular changes associated with cancer risk factors in normal tissues may aid in determining the earliest events of carcinogenesis and informing cancer prevention strategies.\n\nResultsHere we investigated the impact cancer risk factors have on the normal breast epigenome by analyzing DNA methylation genome-wide (Infinium 450K array) in cancer-free women from the Susan G. Komen Tissue Bank (n = 100). We tested the relation of established breast cancer risk factors: age, body mass index, parity, and family history of disease with DNA methylation adjusting for potential variation in cell-type proportions. We identified 787 CpG sites that demonstrated significant associations (Q-value < 0.01) with subject age. Notably, DNA methylation was not strongly associated with the other evaluated breast cancer risk factors. Age-related DNA methylation changes are primarily increases in methylation enriched at breast epithelial cell enhancer regions (P = 7.1E-20), and binding sites of chromatin remodelers (MYC and CTCF). We validated the age-related associations in two independent populations of normal breast tissue (n = 18) and normal-adjacent to tumor tissue (n = 97). The genomic regions classified as age-related were more likely to be regions altered in cancer in both pre-invasive (n = 40, P=3.0E-03) and invasive breast tumors (n = 731, P=1.1E-13).\n\nConclusionsDNA methylation changes with age occur at regulatory regions, and are further exacerbated in cancer suggesting that age influences breast cancer risk in part through its contribution to epigenetic dysregulation in normal breast tissue.

epidemiology↗