Design and application of a knowledge network for automatic prioritization of drug mechanisms.

MOTIVATION: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. RESULTS: In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides. AVAILABILITY AND IMPLEMENTATION: The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-Omics Database Analysis of Aminoacyl-tRNA Synthetases in Cancer.

Aminoacyl-tRNA synthetases (aaRSs) are key enzymes in the mRNA translation machinery, yet they possess numerous non-canonical functions developed during the evolution of complex organisms. The aaRSs and aaRS-interacting multi-functional proteins (AIMPs) are continually being implicated in tumorigenesis, but these connections are often limited in scope, focusing on specific aaRSs in distinct cancer subtypes. Here, we analyze publicly available genomic and transcriptomic data on human cytoplasmic and mitochondrial aaRSs across many cancer types. As high-throughput technologies have improved exponentially, large-scale projects have systematically quantified genetic alteration and expression from thousands of cancer patient samples. One such project is the Cancer Genome Atlas (TCGA), which processed over 20,000 primary cancer and matched normal samples from 33 cancer types. The wealth of knowledge provided from this undertaking has streamlined the identification of cancer drivers and suppressors. We examined aaRS expression data produced by the TCGA project and combined this with patient survival data to recognize trends in aaRSs' impact on cancer both molecularly and prognostically. We further compared these trends to an established tumor suppressor and a proto-oncogene. We observed apparent upregulation of many tRNA synthetase genes with aggressive cancer types, yet, at the individual gene level, some aaRSs resemble a tumor suppressor while others show similarities to an oncogene. This study provides an unbiased, overarching perspective on the relationship of aaRSs with cancers and identifies certain aaRS family members as promising therapeutic targets or potential leads for developing biological therapy for cancer.

Mohawk is a transcription factor that promotes meniscus cell phenotype and tissue repair and reduces osteoarthritis severity.

Meniscus tears are common knee injuries and a major osteoarthritis (OA) risk factor. Knowledge gaps that limit the development of therapies for meniscus injury and degeneration concern transcription factors that control the meniscus cell phenotype. Analysis of RNA sequencing data from 37 human tissues in the Genotype-Tissue Expression database and RNA sequencing data from meniscus and articular cartilage showed that transcription factor Mohawk (MKX) is highly enriched in meniscus. In human meniscus cells, MKX regulates the expression of meniscus marker genes, OA-related genes, and other transcription factors, including Scleraxis (SCX), SRY Box 5 (SOX5), and Runt domain-related transcription factor 2 (RUNX2). In mesenchymal stem cells (MSCs), the combination of adenoviral MKX (Ad-MKX) and transforming growth factor-ß3 (TGF-ß3) induced a meniscus cell phenotype. When Ad-MKX-transduced MSCs were seeded on TGF-ß3-conjugated decellularized meniscus scaffold (DMS) and inserted into experimental tears in meniscus explants, they increased glycosaminoglycan content, extracellular matrix interconnectivity, cell infiltration into the DMS, and improved biomechanical properties. Ad-MKX injection into mouse knee joints with experimental OA induced by surgical destabilization of the meniscus suppressed meniscus and cartilage damage, reducing OA severity. Ad-MKX injection into human OA meniscus tissue explants corrected pathogenic gene expression. These results identify MKX as a previously unidentified key transcription factor that regulates the meniscus cell phenotype. The combination of Ad-MKX with TGF-ß3 is effective for differentiation of MSCs to a meniscus cell phenotype and useful for meniscus repair. MKX is a promising therapeutic target for meniscus tissue engineering, repair, and prevention of OA.

Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019 has triggered an ongoing global pandemic of the severe pneumonia-like disease coronavirus disease 2019 (COVID-19)1. The development of a vaccine is likely to take at least 12-18 months, and the typical timeline for approval of a new antiviral therapeutic agent can exceed 10 years. Thus, repurposing of known drugs could substantially accelerate the deployment of new therapies for COVID-19. Here we profiled a library of drugs encompassing approximately 12,000 clinical-stage or Food and Drug Administration (FDA)-approved small molecules to identify candidate therapeutic drugs for COVID-19. We report the identification of 100 molecules that inhibit viral replication of SARS-CoV-2, including 21 drugs that exhibit dose-response relationships. Of these, thirteen were found to harbour effective concentrations commensurate with probable achievable therapeutic doses in patients, including the PIKfyve kinase inhibitor apilimod2-4 and the cysteine protease inhibitors MDL-28170, Z LVG CHN2, VBY-825 and ONO 5334. Notably, MDL-28170, ONO 5334 and apilimod were found to antagonize viral replication in human pneumocyte-like cells derived from induced pluripotent stem cells, and apilimod also demonstrated antiviral efficacy in a primary human lung explant model. Since most of the molecules identified in this study have already advanced into the clinic, their known pharmacological and human safety profiles will enable accelerated preclinical and clinical evaluation of these drugs for the treatment of COVID-19.

Correcting the F508del-CFTR variant by modulating eukaryotic translation initiation factor 3-mediated translation initiation.

Inherited and somatic rare diseases result from >200,000 genetic variants leading to loss- or gain-of-toxic function, often caused by protein misfolding. Many of these misfolded variants fail to properly interact with other proteins. Understanding the link between factors mediating the transcription, translation, and protein folding of these disease-associated variants remains a major challenge in cell biology. Herein, we utilized the cystic fibrosis transmembrane conductance regulator (CFTR) protein as a model and performed a proteomics-based high-throughput screen (HTS) to identify pathways and components affecting the folding and function of the most common cystic fibrosis-associated mutation, the F508del variant of CFTR. Using a shortest-path algorithm we developed, we mapped HTS hits to the CFTR interactome to provide functional context to the targets and identified the eukaryotic translation initiation factor 3a (eIF3a) as a central hub for the biogenesis of CFTR. Of note, siRNA-mediated silencing of eIF3a reduced the polysome-to-monosome ratio in F508del-expressing cells, which, in turn, decreased the translation of CFTR variants, leading to increased CFTR stability, trafficking, and function at the cell surface. This finding suggested that eIF3a is involved in mediating the impact of genetic variations in CFTR on the folding of this protein. We posit that the number of ribosomes on a CFTR mRNA transcript is inversely correlated with the stability of the translated polypeptide. Polysome-based translation challenges the capacity of the proteostasis environment to balance message fidelity with protein folding, leading to disease. We suggest that this deficit can be corrected through control of translation initiation.

A genome-wide survey of mutations in the Jurkat cell line.

BACKGROUND: The Jurkat cell line has an extensive history as a model of T cell signaling. But at the turn of the 21st century, some expression irregularities were observed, raising doubts about how closely the cell line paralleled normal human T cells. While numerous expression deficiencies have been described in Jurkat, genetic explanations have only been provided for a handful of defects. RESULTS: Here, we report a comprehensive catolog of genomic variation in the Jurkat cell line based on whole-genome sequencing. With this list of all detectable, non-reference sequences, we prioritize potentially damaging mutations by mining public databases for functional effects. We confirm documented mutations in Jurkat and propose links from detrimental gene variants to observed expression abnormalities in the cell line. CONCLUSIONS: The Jurkat cell line harbors many mutations that are associated with cancer and contribute to Jurkat's unique characteristics. Genes with damaging mutations in the Jurkat cell line are involved in T-cell receptor signaling (PTEN, INPP5D, CTLA4, and SYK), maintenance of genome stability (TP53, BAX, and MSH2), and O-linked glycosylation (C1GALT1C1). This work ties together decades of molecular experiments and serves as a resource that will streamline both the interpretation of past research and the design of future Jurkat studies.

Quantitative Metaproteomics and Activity-Based Probe Enrichment Reveals Significant Alterations in Protein Expression from a Mouse Model of Inflammatory Bowel Disease.

Tandem mass spectrometry based shotgun proteomics of distal gut microbiomes is exceedingly difficult due to the inherent complexity and taxonomic diversity of the samples. We introduce two new methodologies to improve metaproteomic studies of microbiome samples. These methods include the stable isotope labeling in mammals to permit protein quantitation across two mouse cohorts as well as the application of activity-based probes to enrich and analyze both host and microbial proteins with specific functionalities. We used these technologies to study the microbiota from the adoptive T cell transfer mouse model of inflammatory bowel disease (IBD) and compare these samples to an isogenic control, thereby limiting genetic and environmental variables that influence microbiome composition. The data generated highlight quantitative alterations in both host and microbial proteins due to intestinal inflammation and corroborates the observed phylogenetic changes in bacteria that accompany IBD in humans and mouse models. The combination of isotope labeling with shotgun proteomics resulted in the total identification of 4434 protein clusters expressed in the microbial proteomic environment, 276 of which demonstrated differential abundance between control and IBD mice. Notably, application of a novel cysteine-reactive probe uncovered several microbial proteases and hydrolases overrepresented in the IBD mice. Implementation of these methods demonstrated that substantial insights into the identity and dysregulation of host and microbial proteins altered in IBD can be accomplished and can be used in the interrogation of other microbiome-related diseases.

A comprehensive and scalable database search system for metaproteomics.

BACKGROUND: Mass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations. RESULTS: Our approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed "Blazmass") to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy and allowing for a more in-depth characterization of the functional landscape of the samples. CONCLUSIONS: The combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomic search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.

OncoRep: an n-of-1 reporting tool to support genome-guided treatment for breast cancer patients using RNA-sequencing.

BACKGROUND: Breast cancer comprises multiple tumor entities associated with different biological features and clinical behaviors, making individualized medicine a powerful tool to bring the right drug to the right patient. Next generation sequencing of RNA (RNA-Seq) is a suitable method to detect targets for individualized treatment. Challenges that arise are i) preprocessing and analyzing RNA-Seq data in the n-of-1 setting, ii) extracting clinically relevant and actionable targets from complex data, iii) integrating drug databases, and iv) reporting results to clinicians in a timely and understandable manner. RESULTS: To address these challenges, we present OncoRep, an RNA-Seq based n-of-1 reporting tool for breast cancer patients. It reports molecular classification, altered genes and pathways, gene fusions, clinically actionable mutations and drug recommendations. It visualizes the data in an approachable html-based interactive report and a PDF clinical report, providing the clinician and tumor board with a tool to guide the treatment decision making process. CONCLUSIONS: OncoRep is free and open-source ( https://bitbucket.org/sulab/oncorep/ ), thereby offering a platform for future development and innovation by the community.

Omics Pipe: a community-based framework for reproducible multi-omics data analysis.

MOTIVATION: Omics Pipe (http://sulab.scripps.edu/omicspipe) is a computational framework that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole-Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas (TCGA). Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis. The goal of Omics Pipe is to democratize next-generation sequencing analysis by dramatically increasing the accessibility and reproducibility of best practice computational pipelines, which will enable researchers to generate biologically meaningful and interpretable results. RESULTS: Using Omics Pipe, we analyzed 100 TCGA breast invasive carcinoma paired tumor-normal datasets based on the latest UCSC hg19 RefSeq annotation. Omics Pipe automatically downloaded and processed the desired TCGA samples on a high throughput compute cluster to produce a results report for each sample. We aggregated the individual sample results and compared them to the analysis in the original publications. This comparison revealed high overlap between the analyses, as well as novel findings due to the use of updated annotations and methods. AVAILABILITY AND IMPLEMENTATION: Source code for Omics Pipe is freely available on the web (https://bitbucket.org/sulab/omics_pipe). Omics Pipe is distributed as a standalone Python package for installation (https://pypi.python.org/pypi/omics_pipe) and as an Amazon Machine Image in Amazon Web Services Elastic Compute Cloud that contains all necessary third-party software dependencies and databases (https://pythonhosted.org/omics_pipe/AWS_installation.html).

Oxidative stress diverts tRNA synthetase to nucleus for protection against DNA damage.

Tyrosyl-tRNA synthetase (TyrRS) is known for its essential aminoacylation function in protein synthesis. Here we report a function for TyrRS in DNA damage protection. We found that oxidative stress, which often downregulates protein synthesis, induces TyrRS to rapidly translocate from the cytosol to the nucleus. We also found that angiogenin mediates or potentiates this stress-induced translocalization. The nuclear-localized TyrRS activates transcription factor E2F1 to upregulate the expression of DNA damage repair genes such as BRCA1 and RAD51. The activation is achieved through direct interaction of TyrRS with TRIM28 to sequester this vertebrate-specific epigenetic repressor and its associated HDAC1 from deacetylating and suppressing E2F1. Remarkably, overexpression of TyrRS strongly protects against UV-induced DNA double-strand breaks in zebrafish, whereas restricting TyrRS nuclear entry completely abolishes the protection. Therefore, oxidative stress triggers an essential cytoplasmic enzyme used for protein synthesis to translocate to the nucleus to protect against DNA damage.

Organizing knowledge to enable personalization of medicine in cancer.

Interpretation of the clinical significance of genomic alterations remains the most severe bottleneck preventing the realization of personalized medicine in cancer. We propose a knowledge commons to facilitate collaborative contributions and open discussion of clinical decision-making based on genomic events in cancer.

The cure: design and evaluation of a crowdsourcing game for gene selection for breast cancer survival prediction.

BACKGROUND: Molecular signatures for predicting breast cancer prognosis could greatly improve care through personalization of treatment. Computational analyses of genome-wide expression datasets have identified such signatures, but these signatures leave much to be desired in terms of accuracy, reproducibility, and biological interpretability. Methods that take advantage of structured prior knowledge (eg, protein interaction networks) show promise in helping to define better signatures, but most knowledge remains unstructured. Crowdsourcing via scientific discovery games is an emerging methodology that has the potential to tap into human intelligence at scales and in modes unheard of before. OBJECTIVE: The main objective of this study was to test the hypothesis that knowledge linking expression patterns of specific genes to breast cancer outcomes could be captured from players of an open, Web-based game. We envisioned capturing knowledge both from the player's prior experience and from their ability to interpret text related to candidate genes presented to them in the context of the game. METHODS: We developed and evaluated an online game called The Cure that captured information from players regarding genes for use as predictors of breast cancer survival. Information gathered from game play was aggregated using a voting approach, and used to create rankings of genes. The top genes from these rankings were evaluated using annotation enrichment analysis, comparison to prior predictor gene sets, and by using them to train and test machine learning systems for predicting 10 year survival. RESULTS: Between its launch in September 2012 and September 2013, The Cure attracted more than 1000 registered players, who collectively played nearly 10,000 games. Gene sets assembled through aggregation of the collected data showed significant enrichment for genes known to be related to key concepts such as cancer, disease progression, and recurrence. In terms of the predictive accuracy of models trained using this information, these gene sets provided comparable performance to gene sets generated using other methods, including those used in commercial tests. The Cure is available on the Internet. CONCLUSIONS: The principal contribution of this work is to show that crowdsourcing games can be developed as a means to address problems involving domain knowledge. While most prior work on scientific discovery games and crowdsourcing in general takes as a premise that contributors have little or no expertise, here we demonstrated a crowdsourcing system that succeeded in capturing expert knowledge.

Stress-independent activation of XBP1s and/or ATF6 reveals three functionally diverse ER proteostasis environments.

The unfolded protein response (UPR) maintains endoplasmic reticulum (ER) proteostasis through the activation of transcription factors such as XBP1s and ATF6. The functional consequences of these transcription factors for ER proteostasis remain poorly defined. Here, we describe methodology that enables orthogonal, small-molecule-mediated activation of the UPR-associated transcription factors XBP1s and/or ATF6 in the same cell independent of stress. We employ transcriptomics and quantitative proteomics to evaluate ER proteostasis network remodeling owing to the XBP1s and/or ATF6 transcriptional programs. Furthermore, we demonstrate that the three ER proteostasis environments accessible by activating XBP1s and/or ATF6 differentially influence the folding, trafficking, and degradation of destabilized ER client proteins without globally affecting the endogenous proteome. Our data reveal how the ER proteostasis network is remodeled by the XBP1s and/or ATF6 transcriptional programs at the molecular level and demonstrate the potential for selective restoration of aberrant ER proteostasis of pathologic, destabilized proteins through arm-selective UPR activation.

Identification of serum-derived sphingosine-1-phosphate as a small molecule regulator of YAP.

Hippo signaling represents a tumor suppressor pathway that regulates organ size and tumorigenesis through phosphorylation and inhibition of the transcription coactivator YAP. Here, we show that serum deprivation dramatically induces YAP Ser127 phosphorylation and cytoplasmic retention, independent of cell-cell contact. Through chemical isolation and activity profiling, we identified serum-derived sphingosine-1-phosphate (S1P) and lysophosphatidic acid (LPA) as small molecule activators of YAP. S1P induces YAP nuclear localization through S1P(2) receptor, Rho GTPase activation, and F-actin polymerization, independent of the core Hippo pathway kinases. Bioinformatics studies also showed that S1P stimulation induces YAP target gene expression in mouse liver and human embryonic stem cells. These results revealed potent small molecule regulators of YAP and suggest that S1P and LPA might modulate cell proliferation and tumorigenesis through YAP activation.

CAFET algorithm reveals Wnt/PCP signature in lung squamous cell carcinoma.

We analyzed the gene expression patterns of 138 Non-Small Cell Lung Cancer (NSCLC) samples and developed a new algorithm called Coverage Analysis with Fisher's Exact Test (CAFET) to identify molecular pathways that are differentially activated in squamous cell carcinoma (SCC) and adenocarcinoma (AC) subtypes. Analysis of the lung cancer samples demonstrated hierarchical clustering according to the histological subtype and revealed a strong enrichment for the Wnt signaling pathway components in the cluster consisting predominantly of SCC samples. The specific gene expression pattern observed correlated with enhanced activation of the Wnt Planar Cell Polarity (PCP) pathway and inhibition of the canonical Wnt signaling branch. Further real time RT-PCR follow-up with additional primary tumor samples and lung cancer cell lines confirmed enrichment of Wnt/PCP pathway associated genes in the SCC subtype. Dysregulation of the canonical Wnt pathway, characterized by increased levels of ß-catenin and epigenetic silencing of negative regulators, has been reported in adenocarcinoma of the lung. Our results suggest that SCC and AC utilize different branches of the Wnt pathway during oncogenesis.

Transcriptional profiling of the effects of 25-hydroxycholesterol on human hepatocyte metabolism and the antiviral state it conveys against the hepatitis C virus.

BACKGROUND: Hepatitis C virus (HCV) infection is a global health problem. A number of studies have implicated a direct role of cellular lipid metabolism in the HCV life cycle and inhibitors of the mevalonate pathway have been demonstrated to result in an antiviral state within the host cell. Transcriptome profiling was conducted on Huh-7 human hepatoma cells bearing subgenomic HCV replicons with and without treatment with 25-hydroxycholesterol (25-HC), an inhibitor of the mevalonate pathway that alters lipid metabolism, to assess metabolic determinants of pro- and antiviral states within the host cell. These data were compared with gene expression profiles from HCV-infected chimpanzees. RESULTS: Transcriptome profiling of Huh-7 cells treated with 25-HC gave 47 downregulated genes, 16 of which are clearly related to the mevalonate pathway. Fewer genes were observed to be upregulated (22) in the presence of 25-HC and 5 genes were uniquely upregulated in the HCV replicon bearing cells. Comparison of these gene expression profiles with data collected during the initial rise in viremia in 4 previously characterized HCV-infected chimpanzees yielded 54 overlapping genes, 4 of which showed interesting differential regulation at the mRNA level in both systems. These genes are PROX1, INSIG-1, NK4, and UBD. The expression of these genes was perturbed with siRNAs and with overexpression vectors in HCV replicon cells, and the effect on HCV replication and translation was assessed. Both PROX1 and NK4 regulated HCV replication in conjunction with an antiviral state induced by 25-hydroxycholesterol. CONCLUSION: Treatment of Huh-7 cells bearing HCV replicons with 25-HC leads to the downregulation of many key genes involved in the mevalonate pathway leading to an antiviral state within the host cell. Furthermore, dysregulation of a larger subset of genes not directly related to the mevalonate pathway occurs both in 25-HC-treated HCV replicon harbouring cells as well as during the initial rise in viremia in infected chimpanzees. Functional studies of 3 of these genes demonstrates that they do not directly act as antiviral gene products but that they indirectly contribute to the antiviral state in the host cell. These genes may also represent novel biomarkers for HCV infection, since they demonstrate an outcome-specific expression profile.

The influence of cholesterol and lipid metabolism on host cell structure and hepatitis C virus replication.

The hepatitis C virus (HCV) replicates on a membrane protein complex composed of viral proteins, replicating RNA, and altered cellular membranes. Small-molecule inhibitors of cellular lipid-cholesterol metabolism such as 25-hydroxycholesterol, cerulenin, lovastatin, and GGTI-286 all show a negative effect on HCV replication. Perturbation of host cell lipid and cholesterol metabolism can disrupt replication complexes by altering membranous structures where replication occurs. Changes in cholesterol and (or) lipid composition can have a general effect on membrane structure. Alternatively, metabolic changes can exert a more subtle influence over replication complexes by altering localization of host proteins through alterations in lipid anchoring. Here, we use Huh-7 cells harboring subgenomic HCV replicons to demonstrate that 25-hydroxycholesterol, cerulenin, lovastatin, and GGTI-286 do not disrupt the membranous web where replication occurs, whereas cholesterol-depleting agents such as beta-cyclodextrin do. Cellular imaging suggests that the HCV RNA can remain associated with subcellular compartments connected with replication complexes in the presence of metabolic inhibitors. Therefore, at least 2 different molecular mechanisms are possible for the inhibition of HCV replication through the modulation of cellular lipid and cholesterol metabolism.

HierS: hierarchical scaffold clustering using topological chemical graphs.

An exhaustive ring-based algorithm, HierS, has been developed in order to provide an intuitive approach to compound clustering for analyzing high-throughput screening results. The recursive algorithm rapidly identifies all possible ring-delimited substructures within a set of compounds. Molecules are grouped by shared ring substructures (scaffolds) so that common scaffolds obtain higher membership. Once all of the scaffolds for a set of compounds are identified, the hierarchical structural relationships between the scaffold structures are established. The complex network of hierarchical relationships is then utilized to navigate compounds in a structurally directed fashion. When the scaffold hierarchy is traversed, over-represented structural features can be rapidly identified so that excess compounds that contain them can be removed without significantly impacting the structural diversity landscape of the compound set. Furthermore, the removed compounds can provide the opportunity to follow-up on active compounds that had previously been discarded because of practical limitations on follow-up capacity. A Web-based interface has been developed that incorporates this algorithm in order to allow for an interactive analysis. In addition, biological data are coupled to scaffolds by the inclusion of activity histograms, which indicate how the compounds in each scaffold class performed in previous high-throughput screening campaigns.