Biology subjects

Diolaiti, M. E.

Publications and source records attributed to Diolaiti, M. E..

7 recordsLinked to original sources

bioRxiv2026

De novo design of a protein fold for small-molecule binding through aromatic π stacking

The de novo design of proteins that bind chemically complex small molecules has broad chemical and biological implications, but strategies typically rely on a small set of protein scaffolds and require extensive experimental screening. Here, we computationally designed proteins around a minimal aromatic {pi}-stacking motif to bind the anthracycline anticancer drug doxorubicin. Experimental characterization of twelve proteins revealed a {micro}M doxorubicin binder; two additional design cycles improved scaffold stability and binding affinity to yield an 85-residue protein that binds doxorubicin with a dissociation constant of 85 nM. An X-ray crystal structure of the protein-drug complex confirmed the accuracy of the designed {pi}-{pi} stacking interactions. The designed protein could act to protect cultured cells from doxorubicin-induced cytotoxicity. Unlike previous ligand-binding protein designs based on repeat proteins or naturally occurring folds, the designed protein adopts a previously unobserved 5-helix globular fold, indicating that a broader space of folded, functional proteins exists even for compact tertiary structures smaller than 100 residues. These results demonstrate that motif-guided generative protein design can discover compact de novo protein folds capable of high-affinity recognition of chemically complex small molecules.

biochemistry↗

bioRxiv2025

Large scale prospective evaluation of co-folding across 557 Mac1-ligand complexes and three virtual screens

Accurate prediction of ligand-bound protein complexes and ranking them by affinity are central problems in drug discovery. While deep learning co-folding methods can help address these challenges, their evaluation has been hampered by the difficulties in assessing independence from training data and insufficiently large test sets. Here we test the ability of co-folding methods to predict the structures of 551 ligands, 489 of which are of sufficient quality for evaluation, bound to the SARS-CoV-2 NSP3 macrodomain (Mac1) that were determined after the training cut-off dates. AlphaFold3 (AF3), Boltz-2, and Chai-1 each reproduced >50% of the Mac1 ligand poses to better than 2 [A] RMSD of experiment. Despite the potential for co-folding to describe protein conformational changes that stabilize ligand binding, we did not find that common conformational rearrangements, including peptide flip and a large loop opening, were recapitulated by the co-folding prediction. For AF3 and Chai-1, ligand pose prediction confidence weakly, but significantly, tracked experimental potency, while DOCK3.7 energies were only weakly correlated. Boltz-2 affinity predictions showed the strongest correlation with measured potency and, after calibration, achieved lower mean absolute error than a baseline predictor. We next assessed whether co-folding scores could rescore docking hit-lists to distinguish true ligands from non-binders among hundreds of molecules prospectively experimentally tested against AmpC {beta}-lactamase, the dopamine D4 and the {sigma}2 receptors. AF3 ligand pose confidence values did not separate true ligands from high-scoring false-positives as effectively as docking scores or Boltz-2 affinity predictions did. Taken together, the modest, but independent correlations of docking score and co-folding confidence or affinity suggests that integrating physics-based and deep-learning approaches may help with hit prioritization and subsequent optimization in structure-based ligand discovery.

biophysics↗

bioRxiv2025

Discovery of AVI-6451, a Potent and Selective Inhibitor of the SARS-CoV-2 ADP-Ribosylhydrolase Mac1 with Oral Efficacy in vivo

The COVID-19 pandemic made plain the need for effective antivirals acting on novel antiviral targets, among which viral macrodomains have attracted considerable attention. We recently described AVI-4206 (1), a potent and selective inhibitor of the SARS-CoV-2 ADP-ribosylhydrolase Mac1 based on a 9H-pyrimido[4,5-b]indole core, the first Mac1 inhibitor to demonstrate antiviral efficacy in mouse models of SARS-CoV-2 infection, but requiring IP administration and frequent dosing. Herein we describe an extensive, structurally enabled medicinal chemistry effort to identify orally bioavailable Mac1 inhibitors by addressing permeability and efflux liabilities of 1 and many of its analogs. Multiple strategies were pursued to overcome these issues, including replacing a urea function to reduce hydrogen bond donor count. While heterocyclic urea mimetics could deliver analogs like AVI-6318 (3) with potencies and ADME profiles similar to 1, abrogation of the P-gp liability was finally achieved with entirely non-polar substituents in place of urea. Thus, AVI-6451 (4) is a potent Mac1 inhibitor lead with low intrinsic clearance, high oral bioavailability, and antiviral efficacy with once-daily oral administration in a mouse model of SARS-CoV-2 infection.

pharmacology and toxicology↗

bioRxiv2025

Identification and Characterization of PLUTO-201, a Novel Long Non-Coding RNA Associated with Poor Outcomes in Prostate Cancer

Background: Despite extensive investigation, the factors promoting aggressive prostate cancer are poorly understood. In particular, despite a few prominent examples, the role of long non-coding RNAs (lncRNAs) is largely unknown. Methods: We performed a comprehensive analysis of whole-genome transcriptome data to identify differential expression across 1,567 patients with prostate cancer, then correlated differential expression with risk of metastatic recurrence. We characterized the lncRNA most associated with metastasis in vitro and in vivo to investigate the mechanism by which it promotes aggressive prostate cancer. Results: We have identified a novel lncRNA, Prostate Cancer Associated hnRNPK Interacting Transcript (PCAHIT), which is strongly associated with metastasis and poor overall survival in men with prostate cancer. We find that overexpression/knockdown of PCAHIT in pre-clinical models of prostate cancer modulates proliferation rates and markers of an aggressive phenotype through regulation of steroid biosynthesis and expression of the MHC class I complex, driving increased growth in androgen-depleted conditions and decreased susceptibility to T cell-mediated cytotoxicity. We further find that the heterogeneous nuclear ribonucleoprotein hnRNPK directly binds PCAHIT and is indispensable for its activity. Conclusions: Overall, our findings indicate that PCAHIT is a driver of aggressive prostate cancer phenotypes and poor clinical outcomes through suppression of the immune response and increased androgen-independent cancer growth. PCAHIT is a potential biomarker of aggressive disease.

cancer biology↗

bioRxiv2024

Histone demethylase enzymes KDM5A and KDM5B modulate immune response by suppressing transcription of endogenous retroviral elements

Epigenetic factors, including lysine-specific demethylases such as the KDM5 paralogs KDM5A and KDM5B have been implicated in cancer and the regulation of immune responses. Here, we performed a comprehensive multiomic study in cells lacking KDM5A or KDM5B to map changes in transcriptional regulation and chromatin organization. RNA-seq analysis revealed a significant decrease in the expression of Kruppel-associated box containing zinc finger (KRAB-ZNF) genes in KDM5A or KDM5B knockout cell lines, which was accompanied by changes ATAC-seq and H3K4me3 ChIP-seq. Pharmacological inhibition of KDM5A and KDM5B catalytic activity with a pan-KDM5 inhibitor, CPI-455, did not significantly change KRAB-ZNF expression, raising the possibility that regulation of KRAB-ZNF expression does not require KDM5A and KDM5B demethylase activity. KRAB-ZNF are recognized suppressors of the transcription of endogenous retroviruses (ERVs) and HAP1 cells with KDM5A or KDM5B gene inactivation showed elevated ERV expression, increased dsRNA levels and elevated levels of immune response genes. Acute degradation of KDM5A using a dTAG system in HAP1 cells led to increased ERV expression, demonstrating that de-repression of ERV genes occurs rapidly after loss of KDM5A. Co-immunoprecipitation of KDM5A revealed an interaction with the Nucleosome Remodeling and Deacetylase (NuRD) complex suggesting that KDM5A and NuRD may act together to regulate the expression of ERVs through KRAB-ZNFs. These findings reveal roles of KDM5A and KDM5B in modulating ERV expression and underscore the therapeutic potential of using degraders of KDM5A and KDM5B to modulate tumor immune responses. Author SummaryThe histone demethylases KDM5A and KDM5B are transcriptional repressors that play an important role in cancer and immune response, making them attractive drug targets. Unfortunately, small molecule inhibitors, including CPI-455, that block KDM5A and KDM5B enzymatic activity, have shown only limited effectiveness at suppressing cancer cell viability as single agents in vitro. In this study we undertook a multi-omics approach to map transcriptional and chromatin changes in KDM5A and KDM5B deficient cells compared to those treated with CPI-455. The datasets revealed that KDM5A and KDM5B modulate the expression of KRAB-ZNF genes and that loss of either gene was associated with increased expression of ERV genes and upregulation of immune response markers. Surprisingly, pharmacological inhibition of these enzymes did not phenocopy genetic ablation. In contrast, acute degradation of KDM5A using a dTAG system caused an increase in ERV expression, providing evidence that this immune modulation is independent of demethylase activity. Together with the limited success of small molecule inhibitors, our data provide strong rationale for the development of KDM5A and KDM5B degraders to modulate tumor immune responses.

genomics↗

bioRxiv2024

AcrIF11 is a potent CRISPR-specific ADP-ribosyltransferase encoded by phage and plasmid

Phage-encoded anti-CRISPR (Acr) proteins inhibit CRISPR-Cas systems to allow phage replication and lysogeny maintenance. Most of the Acrs characterized to date are stable stoichiometric inhibitors. While enzymatic Acrs have been characterized biochemically, little is known about their potency, specificity, and reversibility. Here, we examine AcrIF11, a widespread phage and plasmid-encoded ADP-ribosyltransferase (ART) that inhibits the Type I-F CRISPR-Cas system. We present an NMR structure of an AcrIF11 homolog that reveals chemical shift perturbations consistent with NAD (cofactor) binding. In experiments that model both lytic phage replication and MGE/lysogen stability under high targeting pressure, AcrIF11 is a highly potent CRISPR-Cas inhibitor and more robust to Cas protein level fluctuations than stoichiometric inhibitors. Furthermore, we demonstrate that AcrIF11 is remarkably specific, predominantly ADP-ribosylating Csy1 when expressed in P. aeruginosa. Given the reversible nature of ADP-ribosylation, we hypothesized that ADPr eraser enzymes (macrodomains) could remove ADPr from Csy1, a potential limitation of PTM-based CRISPR inhibition. We demonstrate that diverse macrodomains can indeed remove the modification from Csy1 in P. aeruginosa lysate. Together, these experiments connect the in vitro observations of AcrIF11s enzymatic activity to its potent and specific effects in vivo, clarifying the advantages and drawbacks of enzymatic Acrs in the evolutionary arms race between phages and bacteria.

microbiology↗

bioRxiv2023

Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery

Pharmacodynamic models provide inroads to understanding key mechanisms of action and may significantly improve patient outcomes in cancer with improved ability to determine therapeutic benefit. Additionally, these models may also lead to insights into potential biomarkers that can be utilized for prediction in prognosis and therapeutic decisions. As an example of this potential, here we present an advanced computational Ordinary Differential Equation (ODE) model of PARP1 signalling and downstream effects due to its inhibition. The model has been validated experimentally and further evaluated through a global sensitivity analysis. The sensitivity analysis uncovered two model parameters related to protein synthesis and degradation rates that were also found to contribute the most variability to the therapeutic prediction. Because this variability may define cancer patient subpopulations, we interrogated genomic, transcriptomic, and clinical databases, to uncover a biomarker that may correspond to patient outcomes in the model. In particular, GSPT2, a GTPase with translation function, was discovered and if mutations serve to alter catalytic activity, its presence may explain the variability in the models parameters. This work offers an analysis of ODE models, inclusive of model development, sensitivity analysis, and ensuing experimental data analysis, and demonstrates the utility of this methodology in uncovering biomarkers in cancer. Author summaryBecause biochemical reaction networks are complex, dynamic, and typically provide output that results from non-linear interactions, mathematical models of such offer insight into cell function. In the clinic, models including drug action further their usefulness in that they may predict therapeutic outcome and other useful markers such as those for prognosis. In this study, we report a model of drug action that targets a critical protein, that when inhibited, promotes tumor cell death and documented remissions. Because all patients do not respond to the described treatment, a means to find cancer patient subpopulations that might benefit continues to be a challenge. Therefore, we analyzed the pharmacodynamic model by defining the parameters of the greatest variability and interrogated genomic, transcriptomic, and clinical cohort databases with this information and discovered a novel biomarker associated with prognosis in some ovarian and uterine cancer patients and separately, associated with the potential to respond to treatment.

bioinformatics↗