Biology subjects

Carpenter, K. A.

Publications and source records attributed to Carpenter, K. A..

7 recordsLinked to original sources

bioRxiv2026

Dishevelled 2 coordinates aortic Notch signaling through primitive myeloid and hemogenic endothelial cells during hematopoietic stem cell specification

Hematopoietic stem cells (HSCs) arise from the hemogenic endothelium in the dorsal aorta through the endothelial-to-hematopoietic transition, a tightly regulated developmental process requiring coordinated signaling from the developing hematopoietic niche. Wnt and Notch signaling are essential for HSC specification, with Wnt functioning upstream of Notch; however, the molecular mechanisms by which these pathways are coordinated during HSC development remain poorly understood. Here, we identify Disheveled 2 (Dvl2), a key intracellular Wnt scaffold protein, as an essential regulator of HSC identity specification in zebrafish. Loss of dvl2 impaired HSC specification and reduced Notch signaling in the dorsal aorta, despite normal canonical Wnt activity in the developing endothelium, suggesting that Dvl2 regulates HSC specification independently of endothelial beta-catenin signaling. Hemogenic endothelial-specific expression of the Notch intracellular domain rescued HSC defects in dvl2 morphants, demonstrating that Dvl2 functions upstream of Notch activation in the hemogenic endothelium during HSC specification. In addition, loss of dvl2 reduced primitive macrophages and neutrophil populations. Restoration of primitive myeloid cells through gata1a knockdown rescued both endothelial Notch signaling and HSC defects, demonstrating that Dvl2 is also required for the supportive immune cells that induce HSC identity. Together, these findings reveal that Dvl2 coordinates hemogenic endothelial and immune niche cues to control HSC identity specification.

developmental biology↗

bioRxiv2026

Drug-Target Interaction Prediction with PIGLET

Drug-target interaction (DTI) prediction is a key task for computed-aided drug development that has been widely approached by deep learning models. Despite extremely high reported performance, these models have yet to find widespread success in accelerating real-world drug discovery. In contrast with the most common approach of creating embeddings from one-dimensional or three-dimensional representations of the input drug and input target, we create a novel graph transformer method for DTI prediction that operates on a proteome-wide knowledge graph of binding pocket similarity, protein-protein interactions, drug similarity, and known binding relationships. We benchmark our method, named PIGLET, against existing DTI prediction models on the Human dataset. We assess performance with two different splitting strategies: the frequently-reported random split, and a novel, more rigorous drug-based split. All models perform similarly well on the random split, and PIGLET outperforms all models on the drug-based split. We highlight the utility of PIGLET through a real-world drug discovery case study.

bioinformatics↗

bioRxiv2025

The Human Omnibus of Targetable Pockets

Hundreds of computational methods for predicting ligand binding pockets exist, but the problem of finding druggable pockets throughout the human proteome persists. Different strategies for pocket-finding excel in different use cases. Ensemble models that leverage multiple different pocket-finding strategies can best capture diverse pockets at scale. Despite this, no publicly available human-proteome-wide datasets of pocket predictions from multiple pocket-finding methods exist. We present the Human Omnibus of Targetable Pockets (HOTPocket), a dataset of over 2.4 million predicted pockets over the entire human proteome that utilizes both experimentally-determined and computationally-predicted protein structures. We assembled this dataset by running seven diverse, established pocket-finding methods over all PDB and AlphaFold2 structures of the canonical human proteome. We created a novel pocket scoring method, hotpocketNN, which we used to filter candidate pockets and assemble the final proteome-wide dataset. Our hotpocketNN method is able to recover known ligand binding pockets, including those which are dissimilar from any pocket seen in its training set. The hotpocketNN method outperforms all constituent methods, including P2Rank and Fpocket, when assessing the DCCcriterion on the Astex Diverse Set and PoseBusters dataset. Additionally, hotpocketNN was able to identify recently-discovered druggable pockets on KRAS and the mu opioid receptor. We make both the HOTPocket dataset and the hotpocketNN method freely available. Scientific ContributionWe introduce HOTPocket, a human proteome-wide dataset of known and predicted binding pockets from a diverse assortment of pocket-finding algorithms. We also introduce hotpocketNN, a machine learning model that scores candidate pockets, which we used to filter and harmonize the HOTPocket dataset. Both the data and the model are made publicly available for scientific use.

bioinformatics↗

bioRxiv2025

Exposing the Molecular Reaction Blind Spots of LLMs with PathwayQA

Proteins mediate a large portion of cellular activity, and understanding protein pathways can yield novel biological insights. Large language models (LLMs) have become increasingly adept at performing inference tasks across different fields of science and engineering. These models could facilitate the analysis of protein networks and help generate hypotheses about protein interactions in a scalable and accessible manner. However, the performance of LLMs in inferring protein-mediated biochemical reactions remains understudied. Here, we evaluate nine LLMs in reasoning over protein pathways included in the curated Reactome database. We find that all nine models struggle to infer products of a reaction when given reactants and enzymes. GPT-4o mini performed the best with a median recovery score of 0.6667, but no model surpassed the baseline strategy of parroting reactants back as predicted products. Most LLMs also performed poorly when inferring whether a protein pathway is associated with a human disease, with an average accuracy of 0.5980. DeepSeek 7B Chat performed the best with an accuracy of 0.9100. This study highlights an area where LLMs still struggle to make correct inferences and provides an opportunity for further work in developing biological LLMs. We also provide a novel question-answer dataset, PathwayQA, which is based on the Reactome database. PathwayQA can be used to benchmark and improve model performance on reasoning over protein-interaction networks. PathwayQA is available at https://github.com/Helix-Research-Lab/PathwayQA.

bioinformatics↗

bioRxiv2025

Secreted Frizzled-Related Protein 1a regulates hematopoietic development in a dose-dependent manner

Hematopoietic stem and progenitor cells (HSPCs) arise only during embryonic development, and their identity specification, emergence from the floor of the dorsal aorta, and proliferation are all tightly regulated by molecular mechanisms such as signaling cues. Among these, Wnt signaling plays an important role in HSPC specification, differentiation, and self-renewal, requiring precise modulation for proper development and homeostasis. Wnt signaling is initiated when a Wnt ligand binds to cell surface receptors such as those encoded by the frizzled gene family, activating intracellular signaling pathways that regulate gene expression. Secreted frizzled-related proteins (Sfrps) are known modulators of Wnt signaling, acting as both agonists and antagonists of this pathway. Yet, in vivo functions of Sfrps in HSPC development remain incompletely understood. Here, we demonstrate that Sfrp1a regulates zebrafish HSPC development and differentiation in a dose-dependent manner. In Sfrp1a loss of function animals, we observe an increase in HSPCs, an upregulation of canonical Wnt signaling, and a decrease in differentiation into both lymphoid and myeloid lineages. Conversely, at low-dose sfrp1a overexpression, there is a decrease in HSPCs and an increase in lymphoid differentiation. High-dose sfrp1a overexpression phenocopies the loss of function animals, with an increase in HSPCs, increased canonical Wnt signaling, and decreased lymphoid and myeloid differentiation. These findings highlight the importance of dose-dependent modulation of Sfrps, paralleling what is observed in hematopoietic cancers where SFRP1 loss-of-function and gain-of-function variants can drive tumorigenesis. One sentence summarySfrp1a is required for hematopoietic stem cell development.

developmental biology↗

bioRxiv2022

EGFR-initiated endocytosis of Wnt9a and Fzd9b is required for β-catenin signaling

Cell to cell communication through secreted ligands like those encoded by the Wnt gene family is critical for development and homeostasis during organismal life. One of the bottlenecks in the Wnt field has been identifying specific ligand/receptor pairings and decoding the mechanisms for their downstream signals. We previously discovered that the Wnt9a ligand signals through the cell surface receptors Fzd9b, LRP5/6 and EGFR to promote early proliferation of hematopoietic stem cells during development. We used this exquisitely specific ligand/receptor complex as a platform to determine if Wnt9a requires endocytosis for signaling. Using fluorescently labeled, biologically active Wnt9a and Fzd9b fusion proteins, we demonstrate here that the Wnt9a receptor complex is rapidly endocytosed within one minute of contact with Fzd9b. Following this, the Wnt9a/Fzd9b complex is trafficked through the cell to early and late endosomes, lysosomes, and the endoplasmic reticulum; it is also recycled back to the membrane. Using small molecule inhibitors, genetic and siRNA approaches, we identified that mechanistically this endocytosis requires EGFR-mediated phosphorylation of the Fzd9b tail, followed by endocytosis through a caveolin and EPS15 dependent pathway. Specific modes of endocytosis and trafficking may represent one of the ways in which Wnt/Fzd specificity is established, since other Wnt ligands do not require endocytosis for activity.

cell biology↗

bioRxiv2021

Training data composition affects performance of protein structure analysis algorithms

The three-dimensional structures of proteins are crucial for understanding their molecular mechanisms and interactions. Machine learning algorithms that are able to learn accurate representations of protein structures are therefore poised to play a key role in protein engineering and drug development. The accuracy of such models in deployment is directly influenced by training data quality. The use of different experimental methods for protein structure determination may introduce bias into the training data. In this work, we evaluate the magnitude of this effect across three distinct tasks: estimation of model accuracy, protein sequence design, and catalytic residue prediction. Most protein structures are derived from X-ray crystallography, nuclear magnetic resonance (NMR), or cryo-electron microscopy (cryo-EM); we trained each model on datasets consisting of either all three structure types or of only X-ray data. We find that across these tasks, models consistently perform worse on test sets derived from NMR and cryo-EM than they do on test sets of structures derived from X-ray crystallography, but that the difference can be mitigated when NMR and cryo-EM structures are included in the training set. Importantly, we show that including all three types of structures in the training set does not degrade test performance on X-ray structures, and in some cases even increases it. Finally, we examine the relationship between model performance and the biophysical properties of each method, and recommend that the biochemistry of the task of interest should be considered when composing training sets.

bioinformatics↗