Biology subjects

Karanicolas, J.

Publications and source records attributed to Karanicolas, J..

13 recordsLinked to original sources

bioRxiv2026

Rescuing the Function of Missense-Mutated Tumor Suppressor VHL using Stabilizing Small Molecules

Somatic mutations in the VHL gene, coupled with VHL loss of heterozygosity, drive sporadic clear cell renal cell carcinoma (ccRCC). From structural considerations, we hypothesized that certain mutations in the VHL gene thermodynamically destabilize the folded protein product; these mutations would increase the ratio of unfolded:folded pVHL, and thus cause loss of function. To test this, we used computational structure-based screening followed by biophysical characterization and cellular assays to identify small molecules that bind to the folded (native) conformation of pVHL and stabilize it. These studies led to creation of an agent, CP4.29, that stabilizes the native folded structure of mutant pVHL: this in turn restores wild-type pVHL activities to cells that harbor mutant VHL. These compounds may serve as starting points for further development into an unprecedented new class of kidney cancer drugs. The approach described herein may also serve as a blueprint for developing agents to correct destabilized mutations underlying other human diseases. HighlightsO_LIDesigned small molecules that re-fold and re-activate destabilized mutant VHL C_LIO_LILead agent re-folds and re-activates multiple common VHL mutants C_LIO_LIThis approach may apply to other destabilized proteins that underlie human disease C_LI

biochemistry↗

bioRxiv2025

Variation at the R181 residue of p53 confers loss of p53 DNA binding cooperativity with the retention of mitochondrial-associated apoptosis

The p53 tumor suppressor binds DNA cooperatively as a tetramer, mediated by salt-bridge interactions between p53 residues E180 and R181. Variants at the R181 residue are one of the most identified TP53 pathogenic variants by germline genetic testing. We show that families with TP53 p.R181H and p.R181C variants have an attenuated cancer risk phenotype compared to patients with hotspot dominant negative loss of function TP53 variants. Despite this phenotype, we find that p53 R181H and R181C variants have significantly reduced ability to bind to p53 promoter/enhancer target sequences and transactivate p53 target genes, similar to null variants. However, p53 R181H and R181C retain wild-type p53 structure and tetramerization. In addition, R181-mutant cells undergo apoptosis through wild-type p53 activity at the mitochondria. These results indicate that retention of transcription-independent p53 tumor suppressor function results in a reduced penetrance cancer risk syndrome in humans. Statement of SignificanceInherited TP53 variants cause Li-Fraumeni Syndrome (LFS), which has significant phenotypic heterogeneity in lifetime cancer risk. Our study shows that retention of transcription-independent p53 functions, despite loss of p53 transactivation activity, results in a reduced penetrance phenotype. Classification of TP53 variants should incorporate assays of transcriptional and non-transcriptional functions.

cancer biology↗

bioRxiv2024

vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual Screening

Enthusiastic adoption of make-on-demand chemical libraries for virtual screening has highlighted the need for methods that deliver improved hit-finding discovery rates. Traditional virtual screening methods are often inaccurate, with most compounds nominated in a virtual screen not engaging the intended target protein to any detectable extent. Emerging machine learning approaches have made significant progress in this regard, including our previously-described tool vScreenML. Broad adoption of vScreenML was hindered by its challenging usability and dependencies on certain obsolete or proprietary software packages. Here, we introduce vScreenML 2.0 (https://github.com/gandrianov/vScreenML2) to address each of these limitations with a streamlined Python implementation. Through careful benchmarks, we show that vScreenML 2.0 outperforms other widely-used tools for virtual screening hit discovery.

bioinformatics↗

bioRxiv2024

PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold

Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.

bioinformatics↗

bioRxiv2023

Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors

The extreme surge of interest over the past decade surrounding the use of neural networks has inspired many groups to deploy them for predicting binding affinities of drug-like molecules to their receptors. A model that can accurately make such predictions has the potential to screen large chemical libraries and help streamline the drug discovery process. However, despite reports of models that accurately predict quantitative inhibition using protein kinase sequences and inhibitors SMILES strings, it is still unclear whether these models can generalize to previously unseen data. Here, we build a Convolutional Neural Network (CNN) analogous to those previously reported and evaluate the model over four datasets commonly used for inhibitor/kinase predictions. We find that the model performs comparably to those previously reported, provided that the individual data points are randomly split between the training set and the test set. However, model performance is dramatically deteriorated when all data for a given inhibitor is placed together in the same training/testing fold, implying that information leakage underlies the models performance. Through comparison to simple models in which the SMILES strings are tokenized, or in which test set predictions are simply copied from the closest training set data points, we demonstrate that there is essentially no generalization whatsoever in this model. In other words, the model has not learned anything about molecular interactions, and does not provide any benefit over much simpler and more transparent models. These observations strongly point to the need for richer structure-based encodings, to obtain useful prospective predictions of not-yet-synthesized candidate inhibitors.

bioinformatics↗

bioRxiv2023

Musashi-2 (MSI2) regulation of DNA damage response in lung cancer

Lung cancer is one of the most common types of cancers worldwide. Non-small cell lung cancer (NSCLC), typically caused by KRAS and TP53 driver mutations, represents the majority of all new lung cancer diagnoses. Overexpression of the RNA-binding protein (RBP) Musashi-2 (MSI2) has been associated with NSCLC progression. To investigate the role of MSI2 in NSCLC development, we compared the tumorigenesis in mice with lung-specific Kras-activating mutation and Trp53 deletion, with and without Msi2 deletion (KP versus KPM2 mice). KPM2 mice showed decreased lung tumorigenesis in comparison with KP mice what supports published data. In addition, using cell lines from KP and KPM2 tumors, and human NSCLC cell lines, we found that MSI2 directly binds ATM/Atm mRNA and regulates its translation. MSI2 depletion impaired DNA damage response (DDR) signaling and sensitized human and murine NSCLC cells to treatment with PARP inhibitors in vitro and in vivo. Taken together, we conclude that MSI2 supports lung tumorigenesis, in part, by direct positive regulation of ATM protein expression and DDR. This adds the knowledge of MSI2 function in lung cancer development. Targeting MSI2 may be a promising strategy to treat lung cancer. SignificanceThis study shows the novel role of Musashi-2 as regulator of ATM expression and DDR in lung cancer.

cancer biology↗

bioRxiv2023

Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry

RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.

biochemistry↗

bioRxiv2022

Secreted folate receptor-gamma drives fibrogenesis in nonalcoholic steatohepatitis by amplifying TGFβ signaling in hepatic stellate cells

Hepatic fibrosis is the primary determinant of mortality in nonalcoholic steatohepatitis (NASH) patients. Antagonism of transforming growth factor {beta} (TGF{beta}), a master profibrogenic cytokine, is a promising therapeutic target that has not yet been translated into an effective therapy, due in part to the lack of animal models resembling the human phenotype of NASH. Here we have identified that soluble secreted folate receptor gamma (FOLR3), expressed in humans but not rodents, is a secreted protein that is elevated in livers of NASH subjects but not in subjects with nonalcoholic fatty liver, type II diabetes, or healthy subjects. FOLR3, based on global proteomics, was the most highly expressed NASH-specific protein and positively correlated with increasing fibrosis stages, suggesting an impact on activated hepatic stellate cells (HSCs), the key fibrogenic cell in the liver. Exposure of stellate cells to exogenous FOLR3 led to elevated extracellular matrix (ECM) protein production, an effect synergistic with TGF{beta}1. Structurally, FOLR3 interacts with serine protease HTRA1, which downregulates TGF{beta} signaling through the degradation of its receptor TGFBR2. Administration of human FOLR3 to mice induced severe bridging fibrosis and an ECM pattern resembling human NASH. Our study uncovers a novel role of FOLR3 in enhancing fibrosis and identifies FOLR3 as a potential therapeutic target in NASH fibrosis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/500829v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1ac3ce3org.highwire.dtl.DTLVardef@d1945corg.highwire.dtl.DTLVardef@1632eb1org.highwire.dtl.DTLVardef@8e8d21_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging

In early stage drug discovery, the hit-to-lead optimization (or "hit expansion") stage entails starting from a newly-identified active compound, and improving its potency or other properties. Traditionally this process relies on synthesizing and evaluating a series of analogs to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogs with improved potency. Our protocol begins from an inhibitor of the target kinase, and generalizes the synthetic route used to access it. By searching for commercially-available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions - or billions - of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well-described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into a single new compounds, and which are not. Further, the use of the pairwise approximation allows interaction energies to be assigned to each compound in the library, without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFRT790M, and ACK1. In each example, the enumerated library includes additional analogs reported by the original study to have activity, and these analogs are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/446684v2_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1ccdd9aorg.highwire.dtl.DTLVardef@e8b7b8org.highwire.dtl.DTLVardef@75a4fcorg.highwire.dtl.DTLVardef@1a2da2c_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

A clinically-relevant polymorphism in the Na+/taurocholate cotransporting polypeptide (NTCP) occurs at a rheostat position

Conventionally, most amino acid substitutions at important protein positions are expected to abolish function. However, in several soluble-globular proteins, we identified a class of non-conserved positions for which various substitutions produced progressive functional changes; we consider these evolutionary “rheostats”. Here, we report a strong rheostat position in the integral membrane protein, Na+/taurocholate cotransporting polypeptide (NTCP), at the site of a pharmacologically-relevant polymorphism (S267F). Functional studies were performed for all 20 substitutions (“S267X”) with three substrates (taurocholate, estrone-3-sulfate and rosuvastatin). The S267X set showed strong rheostatic effects on overall transport, and individual substitutions showed varied effects on transport kinetics (Km and Vmax). However, the outcomes were substrate dependent, indicating altered specificity. To assess protein stability, we measured surface expression and used the Rosetta software suite to model structure and stability changes of S267X. Although buried near the substrate binding site, S267X substitutions were easily accommodated in the NTCP structure model. Across the modest range of changes, calculated stabilities correlated with surface-expression differences, but neither parameter correlated with altered transport. Thus, substitutions at rheostat position 267 had wide-ranging effects on the phenotype of this integral membrane protein. We further propose that polymorphic positions in other proteins might be locations of rheostat positions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2020

Comparative Modeling of CDK9 Inhibitors to Explore Selectivity and Structure-Activity Relationships

Cyclin-dependent kinase 9 (CDK9) plays a key role in transcription elongation, and more recently it was also identified as the molecular target of a series of diaminothiazole compounds that reverse epigenetic silencing in a phenotypic assay. To better understand the structural basis underlying these compounds activity and selectivity, we developed a comparative modeling approach that we describe herein. Briefly, this approach draws upon the strong structural conservation across the active conformation of all protein kinases, and their shared pattern of interactions with Type I inhibitors. Because of this, we hypothesized that the large collection of inhibitor/kinase structures available in the Protein Data Bank (PDB) would enable accurate modeling of this diaminothiazole series in complex with each CDK family member. We apply this new comparative modeling pipeline to build each of these structural models, and then demonstrate that these models provide retrospective rationale for the structure-activity relationships that ultimately guided optimization to the lead diaminothiazole compound MC180295 (14e). We then solved the crystal structure of the 14e/CDK9 complex, and found the resulting structure to be in excellent agreement with our corresponding comparative model. Finally, inspired by these models, we demonstrate how structural changes to 14e can be used to rationally tune this compounds selectivity profile. With the emergence of CDK9 as a promising target for therapeutic intervention in cancer, we anticipate that comparative modeling can provide a valuable tool to guide optimization of potency and selectivity of new inhibitors targeting this kinase.

cancer biology↗

bioRxiv2020

Rationalizing PROTAC-mediated ternary complex formation using Rosetta

PROTACs are molecules that combine a target-binding warhead with an E3 ligase-recruiting moiety; by drawing the target protein into a ternary complex with the E3 ligase, PROTACs induce target protein degradation. While PROTACs hold exciting potential as chemical probes and as therapeutic agents, development of a PROTAC typically requires synthesis of numerous analogs to thoroughly explore variations on the chemical linker; without extensive trial and error, it is unclear how to link the two protein-recruiting moieties to promote formation of a productive ternary complex. Here, we describe a structure-based computational method for evaluating suitability of a given linker for ternary complex formation. Our method uses Rosetta to dock the protein components, then builds the PROTAC from its component fragments into each binding mode; complete models of the ternary complex are then refined. We apply this approach to retrospectively evaluate multiple PROTACs from the literature, spanning diverse target proteins. We find that modeling ternary complex formation is sufficient to explain both activity and selectivity reported for these PROTACs, implying that other cellular factors are not key determinants of activity in these cases. We further find that interpreting PROTAC activity is best approached using an ensemble of structures of the ternary complex rather than a single static conformation, and that members of a structurally-conserved protein family can be recruited by the same PROTAC through vastly different binding modes. To encourage adoption of these methods and promote further analyses, we disseminate both the computational methods and the models of ternary complexes. Significance StatementRecent years have brought a flood of interest in developing compounds that selectively degrade protein targets in cells, as exemplified by PROTACs. Fully realizing the promise of PROTACs to transform chemical biology by delivering degraders of diverse and undruggable protein targets has been impeded, however, by the fact that designing a suitable chemical linker between the functional moieties requires extensive trial and error. Here, we describe a structure-based computational method to predict PROTAC activity. We envision that this approach will allow design and optimization of PROTACs for efficient target degradation, selection of E3 ligases best suited for pairing with a given target protein, and understanding the basis by which PROTACs can exhibit different target selectivity than their component warheads.

biochemistry↗

bioRxiv2020

Machine learning classification can reduce false positives in structure-based virtual screening

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discoverys search for active chemical matter. Modern virtual screening methods are still, however, plagued with high false positive rates: typically, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because none of the studies reporting new scoring methods have validated their model prospectively within the same study. Here, we report a new strategy for building a training dataset (D-COID) that aims to generate highly-compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework of gradient-boosted decision trees. In retrospective benchmarks, our new classifier shows outstanding performance relative to other scoring functions. We additionally evaluate the classifier in a prospective context, by screening for new acetylcholinesterase inhibitors. Remarkably, we find that nearly all compounds selected by vScreenML show detectable activity at 50 {micro}M, with 10 of 23 providing greater than 50% inhibition at this concentration. Without any medicinal chemistry optimization, the most potent hit from this initial screen has an IC50 of 280 nM, corresponding to a Ki value of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

bioinformatics↗