Biology subjects

Tropsha, A.

Publications and source records attributed to Tropsha, A..

13 recordsLinked to original sources

bioRxiv2026

AI-Driven Discovery and BSL-4 Validation of Cross-Filovirus Ebola-Marburg Inhibitors and their Synergistic Combinations

Filovirus outbreaks caused by Ebola virus (EBOV) and Marburg virus (MARV), pose severe global health threats characterized by high rates of fatal hemorrhagic fever. While species-specific vaccines and therapeutic monoclonal antibodies are approved for Zaire ebolavirus, broadly-active therapeutics remain unavailable, leaving populations vulnerable to MARV and other pathogenic Ebola species, such as Bundibugyo (BDBV) and Sudan (SUDV) ebolaviruses. Here we report a computationally guided, infectious virus validated screening platform for the rapid discovery of broad-spectrum filovirus antivirals. By leveraging quantitative structure-activity relationship (QSAR) models, we screened 142,382 compounds in silico to prioritize 125 high-potential candidates. Subsequent dose-response and viability profiling identified 23 compounds exhibiting potent, low-micromolar pan-filovirus activity and favorable cytotoxicity profiles. Molecular docking indicates these compounds target conserved structural and functional domains--primarily the VP35 and L proteins--which may disrupt essential viral replication and immune antagonism. Furthermore, systematic combinatorial screening revealed three highly synergistic compound pairs, notably NCGC00113249-01 and NCGC00118008-01, demonstrating robust cross-species efficacy. By targeting conserved vulnerabilities across the filovirus family, this integrated in silico and in vitro pipeline provides a scalable framework to rapidly nominate and optimize synergistic therapeutic regimens against both endemic and emerging viral threats including BDBV. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/737586v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@531939org.highwire.dtl.DTLVardef@19d1454org.highwire.dtl.DTLVardef@1a1c9e3org.highwire.dtl.DTLVardef@cd861b_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Conserved Filovirus Proteins as Targets of Broad-Spectrum Antivirals

Filoviruses are enveloped, non-segmented, negative-strand RNA viruses belonging to the Filoviridae family, which includes five genera: Ebolavirus, Marburgvirus, Cuevavirus, Striavirus, and Thamnovirus. Members of this family cause severe and, often, fatal hemorrhagic fevers in humans and non-human primates, with high mortality rates. To date, only two filoviruses, Ebola virus (EBOV) and Marburg virus (MARV), are known to infect humans and are listed as priority pathogens by the World Health Organization due to their potential for re-emergence and the current lack of effective vaccines and antiviral treatments. In this study, we identify and characterize conserved binding sites within key filoviral proteins to support the development of broad-spectrum, direct-acting antiviral agents. We validated the significance of these conserved regions for drug discovery using existing experimental data. Our analysis revealed notably high sequence similarity among proteins from filoviruses capable of infecting humans (EBOV, TAFV, BDBV, SUDV, MARV, and RAVV) compared to those from non-zoonotic species, with the highest conservation observed in the L and VP40 proteins--both critical for viral genome transcription and replication. Furthermore, we compiled and analyzed available experimental data on known antiviral compounds targeting these proteins, identifying several agents with cross-filovirus activity, including Galidesivir, Remdesivir, and Favipiravir. The integrated approach described here--combining sequence and structural conservation analysis with chemical structure and antiviral activity data--demonstrates a strategy that could be extended to the development of broad-spectrum therapeutics across multiple viral families. HIGHLIGHTSO_LIConserved filovirus sites targeted for broad-spectrum antivirals. C_LIO_LIStructural modeling identifies key antiviral binding sites. C_LIO_LIViral internal proteins are crucial targets for inhibition. C_LIO_LIRemdesivir validates conserved polymerase as a druggable target. C_LIO_LIStudy highlights need for pan-filovirus drug screening C_LI TOC GRAPHIC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/678902v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@4fd25eorg.highwire.dtl.DTLVardef@7c4076org.highwire.dtl.DTLVardef@16c0961org.highwire.dtl.DTLVardef@1dbd8a0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Novel Computational Pipeline to Identify Target Sites for Broad Spectrum Antiviral Drugs

Emerging viruses pose an ongoing threat to human health. While certain viral families are common sources of outbreaks, predicting the specific virus within a family that will cause the next outbreak or pandemic is not possible, creating an urgent need for broad spectrum antiviral drugs that are effective against an array of related viral pathogens. However, broad spectrum drug development is hindered by the lack of detailed knowledge of compound binding sites that are structurally and functionally conserved between viral family members and are essential for virus replication. To overcome this limitation, we developed an in silico approach that combines AI-driven protein structure prediction, computational fragment soaking, multiple sequence alignment, and protein stability calculations to identify highly conserved target sites that are both solvent-accessible and conserved. We applied this approach to the Togaviridae family, which includes emerging pandemic disease threats such as chikungunya and Venezuelan equine encephalitis virus for which there are currently no approved antiviral therapies. Our analysis identified multiple solvent accessible and structurally conserved pockets in the alphavirus non-structural protein 2 (nsP2) protease domain, which is essential for processing of the viral replicase proteins. Mutagenesis of key solvent accessible and conserved residues identified novel pockets that are essential for protease activity and the replication of multiple alphaviruses, validating these pockets as potential antiviral target sites for nsP2 inhibitors. These findings highlight the potential of artificial intelligence-informed modeling for revealing functionally conserved, accessible pockets as a means of identifying potential target binding sites for broadly active direct acting antivirals. Significance StatementHere we present a novel integrative computational approach to identify novel target sites for broadly acting antiviral drugs. We used this technique to identify multiple functionally and structurally conserved protein surface pockets within the alphavirus nsP2 protease and methyl-transferase-like domain. Mutagenesis of these pockets identified that they are essential for protease activity and replication of a genetically diverse group of alphaviruses, validating these sites as potential targets for broadly active small molecule alphavirus inhibitors. This integrative AI-driven approach thus provides an important tool in developing antivirals essential for pandemic preparedness.

microbiology↗

bioRxiv2025

Adaptive Machine Learning Framework enables Unprecedented Yield and Purity of Adeno-Associated Viral Vectors for Gene Therapy

Adeno-associated viral (AAV) vectors for gene therapy are becoming integral to modern medicine, providing therapeutic options for diseases once deemed incurable. Currently, optimizing viral vector purification is a critical bottleneck in the gene therapy industry, impacting product efficacy and safety as well as accessibility and cost to patients. Traditional optimization methods are resource-intensive and often fail to adjust the purification process parameters to maximize the resulting product yield and quality. To address this challenge, we developed a machine learning framework that leverages Bayesian optimization to systematically refine affinity chromatography parameters (sample load, flow rate, and the formulation of chromatographic media) to improve AAV purification. The efficiency of this closed-loop workflow in iteratively optimizing the vectors yield, purity, and transduction efficiency was demonstrated by purifying clinically-relevant serotypes AAV2, AAV5, and AAV9 from HEK293 cell lysates using the affinity adsorbent AAVidity. We show that three cycles of Bayesian optimization elevated yields from a baseline of 70% to 99%, while reducing host-cell impurities by 230-to-400-fold across all serotypes. The optimized parameters consistently produced vectors with high purity and preserved high transduction activity, essential for therapeutic efficacy and safety, demonstrating serotype versatility - a key challenge in AAV manufacturing. By streamlining parameter optimization and enhancing productivity, our adaptive machine learning framework accelerates process development and reduces costs, advancing the accessibility and clinical translation of AAV-based gene therapies.

molecular biology↗

bioRxiv2025

TARRAGON: Therapeutic Target Applicability Ranking and Retrieval-Augmented Generation Over Networks

The identification of therapeutic protein targets is fundamental to the success of drug development and repurposing. Traditional approaches for target selection require extensive preclinical evaluation for toxicity and efficacy, making the process time-intensive and resource-heavy. Computational tools that efficiently prioritize and validate novel targets are needed to streamline drug discovery workflows. To address this gap, we developed TARRAGON: Therapeutic Target Applicability Ranking and Retrieval-Augmented Generation Over Networks, a computational framework that integrates data mining and machine learning to identify, rank, and assess target-disease relationships to nominate new therapeutic targets. TARRAGON mines knowledge graphs to uncover meta-paths, or rules of graph traversal, linking potential therapeutic targets to diseases. It employs a classification model to rank target-disease hypotheses based on evidence patterns and utilizes a retrieval-augmented generation workflow to prompt a large language model for generating feasibility reports on prioritized targets. Using TARRAGON, we prioritized potential drug targets for non-muscle invasive urinary bladder cancer. Top-ranked candidates were validated using CRISPR gene effect and expression data from the Broad Institute DepMap portal. We further proposed chemical modulators for these targets to inform combination drug screening alongside approved bladder cancer therapeutics. TARRAGON introduces a novel, interpretable computational pipeline for therapeutic target discovery and pharmaceutical candidate nomination, offering the potential to accelerate drug development across diverse disease areas.

bioinformatics↗

bioRxiv2025

Breaking the Phalanx: Overcoming Bacterial Drug Resistance with Quorum Sensing Inhibitors that Enhance Therapeutic Activity of Antibiotics

Antibiotic-resistant bacterial infections loom over humanity as an increasingly deadly threat. There exists a dire need for new treatments, especially those that synergize with our existing arsenal of antibiotic drugs, to help overcome the gap in antibiotic efficacy and attenuate the development of new antibiotic resistance in the most dangerous pathogens. Quorum-sensing systems in bacteria drive the formation of biofilms, increase surface motility, and enhance other virulence factors, making these systems attractive targets for the discovery of novel antibacterials. Quorum-sensing inhibitors (QSIs) are hypothesized to synergize with existing antibiotics, making bacteria more sensitive to the effects of these drugs. In this study, we aimed to find the synergistic combinations between the QSIs and known antibiotics to combat the two deadliest hospital infections - Pseudomonas aeruginosa and Acinetobacter baumannii. We mined biochemical activity databases and literature to identify known, high-efficacy QSIs against these bacteria. We used these data to develop and validate a Quantitative Structure-Activity Relationship (QSAR) model for predicting QSI activity and then employed this model to identify new potential QSIs from the Inxight database of approved and investigational drugs. We then tested binary mixtures of the identified QSIs with 11 existing antibiotics using a combinatorial matrix screening approach with ten (five of each) clinical isolates of P. aeruginosa and A. baumannii. Amongst explored drug combinations, 22 exhibited a synergistic effect. Although no mixture inhibiting all the strains was found, piperacillin combined with ketoprofen, indomethacin, and piroxicam demonstrated the broadest antimicrobial action. We anticipate that further preclinical investigation of these combinations of novel repurposed QSIs with a known antibiotic may lead to novel clinical candidates. Table of Content Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/633658v3_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@17ee291org.highwire.dtl.DTLVardef@14cc242org.highwire.dtl.DTLVardef@65a9e2org.highwire.dtl.DTLVardef@743ac9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Heli-SMACC: Helicase-targeting SMAll Molecule Compound Collection

Helicases have emerged as promising targets for the development of antiviral drugs; however, the family remains largely undrugged. To support the focused development of viral helicase inhibitors we identified, collected, and integrated all chemogenomics data for all available helicases from the ChEMBL database. After thoroughly curating and enriching the data with relevant annotations we have created a derivative database of helicase inhibitors which we dubbed Heli-SMACC (Helicase-targeting SMAll Molecule Compound Collection). The current version of Heli-SMACC contains 20,432 bioactivity entries for viral, human, and bacterial helicases. We have selected 30 compounds with promising viral helicase activity and tested them in a SARS-CoV-2 NSP13 ATPase assay. Twelve compounds demonstrated ATPase inhibition and a consistent dose-response curve. The Heli-SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel helicase inhibitors. Heli-SMACC is publicly available at https://smacc.mml.unc.edu. HighlightsO_LIWe created a curated Helicase-Targeting SMAll Molecule Compound Collection (Heli-SMACC). C_LIO_LIHeli-SMACC covers 29 human, viral, and bacterial helicases. C_LIO_LITwelve of thirty selected compounds demonstrated inhibitory activity in a SARS-CoV-2 NSP13 ATPase Assay. C_LIO_LIHeli-SMACC is freely available online at https://smacc.mml.unc.edu. C_LI TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/602122v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@8e098forg.highwire.dtl.DTLVardef@115cb2borg.highwire.dtl.DTLVardef@1cd9da3org.highwire.dtl.DTLVardef@2870a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

HIV Pharmacology Data Repository (PDR): Setting the New Information Sharing Standard for Pharmacokinetic Studies

Clinical therapeutics are becoming increasingly reliant on data science and computational data analytical tools to predict drug behavior in unobserved conditions or populations. These in silico approaches, collectively termed "pharmacometrics", derive biological meaning from the analysis of pooled drug concentration vs. time (CvT) datasets. However, the field lacks standardization for pharmacokinetic (PK) data description and sharing, instead requiring deep dives into the literature and expert data annotation for aggregate data mining. Here we introduce a minimum information sharing standard for PK studies composed of three categories (Intervention, System, and Effects) and implement this standard in the development of a web-based PK database: the HIV Pharmacology Data Repository (PDR). We demonstrate the utility of the HIV PDR by computational modeling of aggregated and curated CvT data extracted from the HIV PDR.

pharmacology and toxicology↗

bioRxiv2022

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses costs thousands if not millions of human lives and trillions of dollars in damage to the global economy. Despite the rapid development of vaccines for SARS-CoV-2, the lack of small molecule antiviral drugs that work against multiple viral families (broad-spectrum antivirals; BSAs) has left the entire worlds human population vulnerable to the infection between the beginning of the outbreak and the widespread availability of vaccines. Developing BSAs is an attractive, yet challenging, approach that could prevent the next, inevitable, viral outbreak from becoming a global catastrophe. To explore whether historical medicinal chemistry efforts suggest the possibility of discovering novel BSAs, we (i) identified, collected, curated, and integrated all chemical bioactivity data available in ChEMBL for molecules tested in respective assays for 13 emerging viruses that, based on published literature, hold the greatest potential threat to global human health; (ii) identified and solved the challenges related to data annotation accuracy including assay description ambiguity, missing cell or target information, and incorrect BioAssay Ontology (BAO) annotations; (iii) developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic (21,392 entries) and target-based (11,123 entries) assays for these viruses; and (iv) identified a subset of compounds showing BSA activity. For the latter task, we eliminated inconclusive and annotated duplicative entries by checking the concordance between multiple assay results and identified eight compounds active against 3-4 viruses from the phenotypic data, 16 compounds active against two viruses from the target-based data, and 35 compounds active in at least one phenotypic and one target-based assay. The pilot version of our SMACC (Small Molecule Antiviral Compound Collection) database contains over 32,500 entries for 13 viruses. Our analysis indicates that previous research yielded very small number of BSA compounds. We posit that focused and coordinated efforts strategically targeting the discovery of such agents must be established and maintained going forward. The SMACC database publicly available at https://smacc.mml.unc.edu may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks.

bioinformatics↗

bioRxiv2022

Allosteric binders of ACE2 are promising anti-SARS-CoV-2 agents

The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for an acute treatment for the disease. We investigate whether compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2s natural enzymatic function. Initial compounds were screened for binding to ACE2 but little interruption of ACE2 enzymatic activity. This set of compounds was extended by application of quantitative structure-activity analysis, which resulted in 512 virtual hits for further confirmatory screening. A subsequent SARS-CoV-2 replication assay revealed that five of these compounds inhibit SARS-CoV-2 replication in human cells. Further effort is required to completely determine the antiviral mechanism of these compounds, but they serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/484484v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@173d7c9org.highwire.dtl.DTLVardef@5c0021org.highwire.dtl.DTLVardef@c9caaorg.highwire.dtl.DTLVardef@18d23_HPS_FORMAT_FIGEXP M_FIG TOC Graphic: Overall study design. C_FIG

bioinformatics↗

bioRxiv2021

Efficient Design Of Peptide-Binding Polymers Using Active Learning Approaches

Active learning (AL) has become a subject of active recent research both in industry and academia as an efficient approach for rapid design and discovery of novel chemicals, materials, and polymers. The key advantages of this approach relate to its ability to (i) employ relatively small datasets for model development, (ii) iterate between model development and model assessment using small external datasets that can be either generated in focused experimental studies or formed from subsets of the initial training data, and (iii) progressively evolve models toward increasingly more reliable predictions and the identification of novel chemicals with the desired properties. Herein, we first compared various AL protocols for their effectiveness in finding biologically active molecules using synthetic datasets. We have investigated the dependency of AL performance on the size of the initial training set, the relative complexity of the task, and the choice of the initial training dataset. We found that AL techniques as applied to regression modeling offer no benefits over random search, while AL used for classification tasks performs better than models built for randomly selected training sets but still quite far from perfect. Using the best performing AL protocol, we have assessed the applicability of AL for the discovery of polymeric micelle formulations for poorly soluble drugs. Finally, the best performing AL approach was employed to discover and experimentally validate novel binding polymers for a case study of asialoglycoprotein receptor (ASGPR).

pharmacology and toxicology↗

bioRxiv2021

Crowdsourced Identification of Multi-Target Kinase Inhibitors for RET- and TAU- Based Disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology. Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.

pharmacology and toxicology↗

bioRxiv2019

A decoy heterotrimeric Gα protein has substantially reduced nucleotide binding but retains nucleotide-independent interactions with its cognate RGS protein and Gβγ dimer

Plants uniquely have a family of proteins called extra-large G proteins (XLG) that share homology in their C-terminal half with the canonical G subunits; we carefully detail here that Arabidopsis XLG2 lacks critical residues requisite for nucleotide binding and hydrolysis which is consistent with our quantitative analyses. Based on microscale thermophoresis, Arabidopsis XLG2 binds GTP{gamma}S with an affinity 100-1000 times lower than that to canonical G subunits. This means that given the concentration range of guanine nucleotide in plant cells, XLG2 is not likely bound by GTP in vivo. Homology modeling and molecular dynamics simulations provide a plausible mechanism for the poor nucleotide binding affinity of XLG2. Simulations indicate substantially stronger salt bridge networks formed by several key amino-acid residues of AtGPA1 which are either misplaced or missing in XLG2. These residues in AtGPA1 not only maintain the overall shape and integrity of the apoprotein cavity but also increase the frequency of favorable nucleotide-protein interactions in the nucleotide-bound state. Despite this loss of nucleotide dependency, XLG2 binds the RGS domain of AtRGS1 with an affinity similar to the Arabidopsis AtGPA1 in its apo-state and about 2 times lower than AtGPA1 in its transition state. In addition, XLG2 binds the G{beta}{gamma} dimer with an affinity similar to that of AtGPA1. XLG2 likely acts as a dominant negative G protein to block G protein signaling. We propose that XLG2, independent of guanine nucleotide binding, regulates the active state of the canonical G protein pathway directly by sequestering G{beta}{gamma} and indirectly by promoting heterodimer formation.

biochemistry↗