Biology subjects

Hancock, M.

Publications and source records attributed to Hancock, M..

8 recordsLinked to original sources

bioRxiv2026

Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets

The complement of tumor cell surface proteins, or "surfaceome", is a rich source of potential immunotherapy targets. To move beyond expression-based target discovery, we previously described "structural surfaceomics," combining crosslinking mass spectrometry (XL-MS) with surface protein biotinylation to identify conformation-selective targets. In our prior work, we applied this method to a single model of acute myeloid leukemia (AML), identifying active integrin beta-2 as a promising target. Here, we expand structural surfaceomics to identify additional immunotherapy targets and surface protein biology across additional models of AML, multiple myeloma, and prostate cancer, as well as donor peripheral blood mononuclear cells. Utilizing these models and different chemical crosslinkers, we compile an extensive database of 5,209 crosslinks. We characterize both shared and unique crosslink-based features, identifying 1,612 disease model-specific crosslinks, including 212 potentially defining tumor-specific conformations based on distance constraint violations relative to AlphaFold predictions. We further implement a suite of emerging modeling tools to predict tumor-specific protein structures. We probe crosslinking patterns suggesting multiple myeloma-specific CD48 and AML-specific integrin 1/{beta}4 heterodimer conformations. This work establishes a resource for cancer structural biology by implementation of structural surfaceomics. Our findings also point toward more realistic protein design models, potentially enabling systematic detection of targetable cancer-specific epitopes for next-generation immunotherapies.

cancer biology↗

bioRxiv2025

Bayesian multi-state multi-condition modeling of a protein structure based on X-ray crystallography data

1An atomic structure model of a protein can be computed from a diffraction pattern of its crystal. While most crystallographic studies produce a single set of atomic coordinates, the billions of protein molecules in a crystal sample many conformational modes during data collection. As a result, a "multi-state" model that depicts these conformations could reproduce the X-ray data better than a single conformation, and thus likely be more accurate. Computing such a multistate model is challenging due to a lower data-to-parameter ratio than that for single-state modeling. To address this challenge, additional information could be considered, such as X-ray datasets collected for the same system under distinct experimental conditions (eg, temperature, ligands, mutations, and pressure). Here, we develop, benchmark, and illustrate MultiXray: Bayesian multi-state multi-condition modeling for X-ray crystallography. The input information is several X-ray datasets collected under distinct conditions and a molecular mechanics force field. The model consists of an independent coordinate set for each of several states and the weight of each state under each condition. A Bayesian posterior model density quantifies the match of the model with all X-ray datasets and the force field. A sample of models is drawn from the posterior model density using biased molecular dynamics (MD) simulations. We benchmark MultiXray on simulated CypA X-ray data. Using a second X-ray dataset improves the Rfree from 0.105 to 0.089. We then demonstrate MultiXray on experimental temperature-dependent data for SARS-CoV-2 Mpro. Using multiple X-ray datasets improves Rfree of the PDB-deposited structure from 0.253 to 0.237. MultiXray is implemented in our open-source Integrative Modeling Platform (IMP) software, relying on integration with Phenix, thus making it easily applicable to many studies.

biophysics↗

bioRxiv2025

Benchmarking of automated cancer cell annotation methods for scRNA-seq data reveals Consensus annotation as the preferred method

Targeted cancer therapies have shown therapeutic advantages due to tumor-specific drug activity. Single-cell RNA-sequencing has been widely used in cancer studies to define different cellular identities. However, accurate identification of tumor vs other normal cells is essential to define novel tumor-specific targets. Recent methods have been developed to perform the task of tumor cell annotation, which can be divided into two categories: CNV-based methods, which use transcriptome measurements to infer copy number variations and identify cells with alterations as tumor, and Reference-based methods which train a classifier using previously annotated tumor data and use it to annotate new datasets. We benchmarked the state-of-the-art method of each category, SCEVAN and scATOMIC, respectively, together with Consensus annotation method where a cell is considered tumor if both methods agree on that. Across 20 cancer datasets spanning 9 cancer types with a total of 379 samples, the Consensus annotation outperformed other methods in terms of precision score. SCEVAN works well when clear CNVs are detected, otherwise cells are randomly split between normal and tumor producing many false positives. While scATOMIC efficiently detects all normal cells except normal epithelial cells, where all epithelial cells (normal and malignant) are considered cancerous. The Consensus annotation method overcomes the limitations of both methods, being able to detect normal epithelial cells together with other normal cell types as non-tumor, also annotating malignant epithelial cells as tumor even with weak CNVs. This produces overall higher precision with the least number of false positives, leading to confident tumor-specific potential therapeutic targets. Implementation is available in the MACE R package through GitLab (https://gitlab.com/genmab-public/mace/).

bioinformatics↗

bioRxiv2023

Deep autoencoder-based behavioral pattern recognition outperforms standard statistical methods in high-dimensional zebrafish studies

Zebrafish have become an essential tool in screening for developmental neurotoxic chemicals and their molecular targets. The success of zebrafish as a screening model is partially due to their physical characteristics including their relatively simple nervous system, rapid development, experimental tractability, and genetic diversity combined with technical advantages that allow for the generation of large amounts of high-dimensional behavioral data. These data are complex and require advanced machine learning and statistical techniques to comprehensively analyze and capture spatiotemporal responses. To accomplish this goal, we have trained semi-supervised deep autoencoders using behavior data from unexposed larval zebrafish to extract quintessential "normal" behavior. Following training, our network was evaluated using data from larvae shown to have significant changes in behavior (using a traditional statistical framework) following exposure to toxicants that include nanomaterials, aromatics, per- and polyfluoroalkyl substances (PFAS), and other environmental contaminants. Further, our model identified new chemicals (Perfluoro-n-octadecanoic acid, 8-Chloroperfluorooctylphosphonic acid, and Nonafluoropentanamide) as capable of inducing abnormal behavior at multiple chemical-concentrations pairs not captured using distance moved alone. Leveraging this deep learning model will allow for better characterization of the different exposure-induced behavioral phenotypes, facilitate improved genetic and neurobehavioral analysis in mechanistic determination studies and provide a robust framework for analyzing complex behaviors found in higher-order model systems. Author SummaryWe demonstrate that a deep autoencoder using raw behavioral tracking data from zebrafish toxicity screens outperforms conventional statistical methods, resulting in a comprehensive evaluation of behavioral data. Our models can accurately distinguish between normal and abnormal behavior with near-complete overlap with existing statistical approaches, with many chemicals detectable at lower concentrations than with conventional statistical tests; this is a crucial finding for the protection of public health. Our deep learning models enable the identification of new substances capable of inducing aberrant behavior, and we generated new data to demonstrate the reproducibility of these results. Thus, neurodevelopmentally active chemicals identified by our deep autoencoder models may represent previously undetectable signals of subtle individual response differences. Our method elegantly accounts for the high degree of behavioral variability associated with the genetic diversity found in a highly outbred population, as is typical for zebrafish research, thereby making it applicable to multiple laboratories. Utilizing the vast quantities of control data generated during high-throughput screening is one of the most innovative aspects of this study and to our knowledge is the first study to explicitly develop a deep autoencoder model for anomaly detection in large-scale toxicological behavior studies.

pharmacology and toxicology↗

bioRxiv2023

Human Cytomegalovirus UL138 Interaction with USP1 Activates STAT1 in infection

Innate immune responses are crucial for limiting virus infection. However, viruses often hijack our best defenses for viral objectives. Human Cytomegalovirus (HCMV) is a beta herpesvirus which establishes a life-long latent infection. Defining the virus-host interactions controlling latency and reactivation is vital to the control of viral disease risk posed by virus reactivation. We defined an interaction between UL138, a pro-latency HCMV gene, and the host deubiquintase complex, UAF1-USP1. UAF1 is a scaffold protein pivotal for the activity of ubiquitin specific peptidases (USP), including USP1. UAF1-USP1 sustains an innate immune response through the phosphorylation and activation of signal transducer and activator of transcription-1 (pSTAT1), as well as regulates the DNA damage response. After the onset of viral DNA synthesis, pSTAT1 levels are elevated and this depends upon UL138 and USP1. pSTAT1 localizes to viral centers of replication, binds to the viral genome, and influences UL138 expression. Inhibition of USP1 results in a failure to establish latency, marked by increased viral genome replication and production of viral progeny. Inhibition of Jak-STAT signaling also results in increased viral genome synthesis in hematopoietic cells, consistent with a role for USP1-mediated regulation of STAT1 signaling in the establishment of latency. These findings demonstrate the importance of the UL138-UAF1-USP1 virus-host interaction in regulating HCMV latency establishment through the control of innate immune signaling. It will be important going forward to distinguish roles of UAF1-USP1 in regulating pSTAT1 relative to its role in the DNA damage response in HCMV infection. ImportanceHuman cytomegalovirus (HCMV) is one of nine herpesviruses that infect humans. Following a primary infection, HCMV establishes a life-long latent infection that is marked by sporadic, and likely frequent reactivation events. While these reactivation events are asymptomatic in the immune competent host, they pose important disease risks for the immune compromised, including solid organ or stem cell transplant recipients. Its complex interactions with host biology and deep coding capacity make it an excellent model for defining mechanisms important for viral latency and reactivation. Here we define an interaction with host proteins that commandeer typically antiviral innate immune signaling for the establishment of latency.

microbiology↗

bioRxiv2021

Human cytomegalovirus blocks canonical TGFβ signaling during lytic infection to limit induction of type I interferons

Human cytomegalovirus (HCMV) microRNAs (miRNAs) significantly rewire host signaling pathways to support the viral lifecycle and regulate host cell responses. Here we show that SMAD3 expression is regulated by HCMV miR-UL22A and contributes to the IRF7-mediated induction of type I IFNs and IFN-stimulated genes (ISGs) in human fibroblasts. Addition of exogenous TGF{beta} interferes with the replication of a miR-UL22A mutant virus in a SMAD3-dependent manner in wild type fibroblasts, but not in cells lacking IRF7, indicating that downregulation of SMAD3 expression to limit IFN induction is important for efficient lytic replication. These findings uncover a novel interplay between SMAD3 and innate immunity during HCMV infection and highlight the role of viral miRNAs in modulating these responses. Author SummaryCells trigger the interferon (IFN) response to induce the expression of cellular genes that limit virus replication. In turn, viruses have evolved numerous countermeasures to avoid the effects of IFN signaling. Using a microRNA (miRNA) mutant virus we have uncovered a novel means of regulating the IFN response during human cytomegalovirus (HCMV) infection. Lytic HCMV infection induces the production of TGF{beta}, which binds to the TGF{beta} receptor and activates the receptor-associated SMAD SMAD3. SMAD3, together with IRF7, induces the expression of IFN{beta} and downstream IFN-stimulated genes in human fibroblasts. To counteract this, HCMV miR-UL22A, along with other HCMV gene products, directly targets SMAD3 for downregulation. Infection of fibroblasts with a miR-UL22A mutant virus results in enhanced type I IFN production in a SMAD3-dependent manner and the virus is impaired for growth in the presence of TGF{beta}, but only when both SMAD3 and IRF7 are present, highlighting the unique interaction between TGF{beta} and innate immune signaling.

microbiology↗

bioRxiv2020

Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

Target evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. In addition, we showed that the entire protein regions or individual structural subsets can be profiled using our derived fi-score based on amino acid dihedral angle and B-factor distribution. We further described a method to assess the structural profile and extract information on sites of importance using machine learning Gaussian mixture models. In combination, these biophysical analytical methods could potentially help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites which would greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets.

biophysics↗

bioRxiv2020

Spt5 phosphorylation and the Rtf1 Plus3 domain promote Rtf1 function through distinct mechanisms

Rtf1 is a conserved RNA polymerase II (RNAPII) elongation factor that promotes co-transcriptional histone modification, RNAPII transcript elongation, and mRNA processing. Rtf1 function requires phosphorylation of Spt5, an essential RNAPII processivity factor. Spt5 is phosphorylated within its C-terminal domain (CTD) by cyclin-dependent kinase 9 (Cdk9), catalytic component of positive transcription elongation factor b (P-TEFb). Rtf1 recognizes phosphorylated Spt5 (pSpt5) through its Plus3 domain. Since Spt5 is a unique target of Cdk9, and Rtf1 is the only known pSpt5-binding factor, the Plus3/pSpt5 interaction is thought to be a key Cdk9-dependent event regulating RNAPII elongation. Here we dissect Rtf1 regulation by pSpt5 in the fission yeast Schizosaccharomyces pombe. We demonstrate that the Plus3 domain of Rtf1 (Prf1 in S. pombe) and pSpt5 are functionally distinct, and that they act in parallel to promote Prf1 function. This alternate Plus3 domain function involves an interface that overlaps with the pSpt5 binding site and that can interact with single-stranded nucleic acid or with the Polymerase Associated Factor (PAF) Complex in vitro. We further show that the C-terminal region of Prf1, which also interacts with PAF, has a similar parallel function with pSpt5. Our results elucidate unexpected complexity underlying Cdk9-dependent pathways that regulate transcription elongation.

molecular biology↗