Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the the number of confirmed hits. In contrast, “utility-aware” methods are informed by models of screeners’ preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims—by changing which hits are sent for confirmatory testing—to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than three-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.

The reduction of plasma low-density lipoprotein levels by HMG-CoA reductase inhibitors, or statins, has had a revolutionary impact in medicine, but muscle-related side effects remain a dose-limiting toxicity in many patients. We describe a chemical epistasis approach that can be useful in refining the mechanism of statin muscle toxicity, as well as in screening for agents that suppress muscle toxicity while preserving the ability of statins to increase the expression of the low-density lipoprotein receptor. Using this approach, we identified one compound that attenuates the muscle side effects in both cellular and animal models of statin toxicity, likely by influencing Rab prenylation. Our proof-of-concept screen lays the foundation for truly high-throughput screens that could help lead to the development of clinically useful adjuvants that can one day be co-administered with statins.

Motivation: In high-throughput screens (HTS) of small molecules for activity in an in vitro assay, it is common to search for active scaffolds, with at least one example successfully confirmed as an active. The number of active scaffolds better reflects the success of the screen than the number of active molecules. Many existing algorithms for deciding which hits should be sent for confirmatory testing neglect this concern.

Results: We derived a new extension of a recently proposed economic framework, diversity-oriented prioritization (DOP), that aims—by changing which hits are sent for confirmatory testing—to maximize the number of scaffolds with at least one confirmed active. In both retrospective and prospective experiments, DOP accurately predicted the number of scaffold discoveries in a batch of confirmatory experiments, improved the rate of scaffold discovery by 8–17%, and was surprisingly robust to the size of the confirmatory test batches. As an extension of our previously reported economic framework, DOP can be used to decide the optimal number of hits to send for confirmatory testing by iteratively computing the cost of discovering an additional scaffold, the marginal cost of discovery.

Contact: swamidass@wustl.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Pancreatic beta-cell apoptosis is a critical event during the development of type-1 diabetes. The identification of small molecules capable of preventing cytokine-induced apoptosis could lead to avenues for therapeutic intervention. We developed a set of phenotypic cell-based assays designed to identify such small-molecule suppressors. Rat INS-1E cells were simultaneously treated with a cocktail of inflammatory cytokines and a collection of 2,240 diverse small molecules, and screened using an assay for cellular ATP levels. Forty-nine top-scoring compounds included glucocorticoids, several pyrazole derivatives, and known inhibitors of glycogen synthase kinase-3β. Two compounds were able to increase cellular ATP levels, reduce caspase-3 activity and nitrite production, and increase glucose-stimulated insulin secretion in the presence of cytokines. These results indicate that small molecules identified by this screening approach may protect beta cells from autoimmune attack, and may be good candidates for therapeutic intervention in early stages of type-1 diabetes.

Despite considerable efforts, description of molecular shape is still largely an unresolved problem. Given the importance of molecular shape in the description of spatial interactions in crystals or ligand-target complexes, this is not a satisfying state. In the current work, we propose a novel application of alpha shapes to the description of the shapes of small molecules. Alpha shapes are parameterized generalizations of the convex hull. For a specific value of α, the alpha shape is the geometric dual of the space-filling model of a molecule, with the parameter α allowing description of shape in varying degrees of detail. To date, alpha shapes have been used to find macromolecular cavities and to estimate molecular surface areas and volumes. We developed a novel methodology for computing molecular shape characteristics from the alpha shape. In this work, we show that alpha-shape descriptors reveal aspects of molecular shape that are complementary to other shape descriptors, and that accord well with chemists’ intuition about shape. While our implementation of alpha-shape descriptors is not computationally trivial, we suggest that the additional shape characteristics they provide can be used to improve and complement shape-analysis methods in domains such as crystallography and ligand-target interactions. In this communication, we present a unique methodology for computing molecular shape characteristics from the alpha shape. We first describe details of the alpha-shape calculation, an outline of validation experiments performed, and a discussion of the advantages and challenges we found while implementing this approach. The results show that, relative to known shape calculations, this method provides a high degree of shape resolution with even small changes in atomic coordinates.

How many hits from a high-throughput screen should be sent for confirmatory experiments? Analytical answers to this question are derived from statistics alone and aim to fix, for example, the false-discovery rate at a predetermined tolerance. These methods, however, neglect local economic context and consequently lead to irrational experimental strategies. In contrast, we argue that this question is essentially economic, not statistical, and is amenable to an economic analysis that admits an optimal solution. This solution, in turn, suggests a novel tool for deciding the number of hits to confirm, the marginal cost of discovery, which meaningfully quantifies the local economic trade-off between true and false positives, yielding an economically optimal experimental strategy. Validated with retrospective simulations and prospective experiments, this strategy identified 157 additional actives which had been erroneously labeled inactive in at least one real-world screening experiment.

Background

To elucidate metabolic changes that occur in diabetes, obesity, and cancer, it is important to understand cellular energy metabolism pathways and their alterations in various cells.

Methodology and Principal Findings

Here we describe a technology for simultaneous assessment of cellular energy metabolism pathways. The technology employs a redox dye chemistry specifically coupled to catabolic energy-producing pathways. Using this colorimetric assay, we show that human cancer cell lines from different organ tissues produce distinct profiles of metabolic activity. Further, we show that murine white and brown adipocyte cell lines produce profiles that are distinct from each other as well as from precursor cells undergoing differentiation.

Conclusions

This technology can be employed as a fundamental tool in genotype-phenotype studies to determine changes in cells from shared lineages due to differentiation or mutation.

Discovering small-molecule modulators for thousands of gene products requires multiple stages of biological testing, specificity evaluation, and chemical optimization. Many cellular profiling methods, including cellular sensitivity, gene-expression, and cellular imaging, have emerged as methods to assess the functional consequences of biological perturbations. Cellular profiling methods applied to small-molecule science provide opportunities to use complex phenotypic information to prioritize and optimize small-molecule structures simultaneously against multiple biological endpoints. As throughput increases and cost decreases for such technologies, we see an emerging paradigm of using more information earlier in probe- and drug-discovery efforts. Moreover, increasing access to public datasets makes possible the construction of “virtual” profiles of small-molecule performance, even when multiplexed measurements were not performed or when multidimensional profiling was not the original intent. We review some key conceptual advances in small-molecule phenotypic profiling, emphasizing connections to other information, such as protein-binding measurements, genetic perturbations, and cell states. We argue that to maximally leverage these measurements in probe and drug discovery requires a fundamental connection to synthetic chemistry, allowing the consequences of synthetic decisions to be described in terms of changes in small-molecule profiles. Mining such data in the context of chemical structure and synthesis strategies can inform decisions about chemistry procurement and library development, leading to optimal small-molecule screening collections.

Diversity-oriented organic synthesis (DOS) is a strategy to make compound collections to probe biological systems1-7. Designing better DOS libraries requires having methods to assess the consequences of different synthesis decisions on the biological performance of resulting library members8. Since we are particularly interested in how stereochemistry affects performance in biological assays, we prepared a disaccharide library containing systematic stereochemical variations, assayed the library for different biological effects, and developed methods to assess the similarity of performance between members across multiple assays. These methods allow us to ask which subsets of stereochemical features best predict similarity in patterns of biological performance between individual members and which features produce the greatest variation of outcomes. We anticipate that the data-analysis approach presented here can be generalized to other sets of biological assays and other chemical descriptors. Methods to assess which structural features of library members produce the greatest similarity in performance for a given set of biological assays should help prioritize synthesis decisions in second-generation library development targeting the underlying cell-biological processes. Methods to assess which structural features of library members produce the greatest variation in performance should help guide decisions about what synthetic methods need to be developed to make optimal small-molecule screening collections.

Summary

In vivo imaging reveals how proteins and cells function as part of complex regulatory networks in intact organisms, and thereby contributes to a systems-level understanding of biological processes. However, the development of novel in vivo imaging probes remains challenging. Most probes are directed against a limited number of pre-specified protein targets; cell-based screens for imaging probes have shown promise, but raise concerns over whether in vitro surrogate cell models recapitulate in vivo phenotypes. Here, we rapidly profile the in vitro binding of nanoparticle imaging probes in multiple samples of defined target vs. background cell types, using primary cell isolates. This approach selects for nanoparticles that show desired targeting effects across all tested members of a class of cells, and decreases the likelihood that an idiosyncratic cell line will unduly skew screening results. To adjust for multiple hypothesis testing, we use permutation methods to identify nanoparticles that best differentiate between the target and background cell classes. (This approach is conceptually analogous to one used for high-dimensionality datasets of genome-wide gene expression, e.g. to identify gene expression signatures that discriminate subclasses of cancer.) We apply this approach to the identification of nanoparticle imaging probes that bind endothelial cells, and validate our in vitro findings in human arterial samples, and by in vivo intravital microscopy in mice. Overall, this work presents a generalizable approach to the unbiased discovery of in vivo imaging probes, and may guide the further development of novel endothelial imaging probes.

ChemBank (http://chembank.broad.harvard.edu/) is a public, web-based informatics environment developed through a collaboration between the Chemical Biology Program and Platform at the Broad Institute of Harvard and MIT. This knowledge environment includes freely available data derived from small molecules and small-molecule screens and resources for studying these data. ChemBank is unique among small-molecule databases in its dedication to the storage of raw screening data, its rigorous definition of screening experiments in terms of statistical hypothesis testing, and its metadata-based organization of screening experiments into projects involving collections of related assays. ChemBank stores an increasingly varied set of measurements derived from cells and other biological assay systems treated with small molecules. Analysis tools are available and are continuously being developed that allow the relationships between small molecules, cell measurements, and cell states to be studied. Currently, ChemBank stores information on hundreds of thousands of small molecules and hundreds of biomedically relevant assays that have been performed at the Broad Institute by collaborators from the worldwide research community. The goal of ChemBank is to provide life scientists unfettered access to biomedically relevant data and tools heretofore available primarily in the private sector.

Background

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks.

Results

Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors).

Conclusion

SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request.

An efficient synthetic pathway to the possible stereoisomers of skeletally diverse heterocyclic small molecules is presented. The change in shape brought about by different intramolecular cyclizations of diastereoisomeric amino propargylic alcohols is quantified using principal moment-of-inertia (PMI) shape analysis.

We show that natural products target proteins with a high number of protein−protein functional interactions (high biological network connectivity) and that these protein targets have higher network connectivity than disease genes. This feature may facilitate disruption of essential biological pathways, resulting in competitor death. This result also suggests that additional sources of small molecules will be required to discover drugs targeting the root causes of human disease in the future.

A short and modular synthetic pathway using intramolecular 1,3-dipolar cycloaddition reactions and yielding functionalized isoxazoles, isoxazolines, and isoxazolidines is described. The change in shape of previous compounds and those in this study is quantified and compared using principal moment-of-inertia shape analysis.