The Use of Informer Sets in Screening: Perspectives on an Efficient Strategy to Identify New Probes

Small-molecule discovery typically involves large-scale screening campaigns, spanning multiple compound collections. However, such activities can be cost- or time-prohibitive, especially when using complex assay systems, limiting the number of compounds tested. Further, low hit rates can make the process inefficient. Sparse coverage of chemical structure or biological activity space can lead to limited success in a primary screen and represents a missed opportunity by virtue of selecting the "wrong" compounds to test. Thus, the choice of screening collections becomes of paramount importance. In this perspective, we discuss the utility of generating "informer sets" for small-molecule discovery, and how this strategy can be leveraged to prioritize probe candidates. While many researchers may assume that informer sets are focused on particular targets (e.g., kinases) or processes (e.g., autophagy), efforts to assemble informer sets based on historical bioactivity or successful human exposure (e.g., repurposing collections) have shown promise as well. Another method for generating informer sets is based on chemical structure, particularly when the compounds have unknown activities and targets. We describe our efforts to screen an informer set representing a collection of 100,000 small molecules synthesized through diversity-oriented synthesis (DOS). This process enables researchers to identify activity early and more extensively screen only a few chemical scaffolds, rather than the entire collection. This elegant and economic outcome is a goal of the informer set approach. Here, we aim not only to shed light on this process, but also to promote the use of informer sets more widely in small-molecule discovery projects.

Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling

Anti-cancer uses of non-oncology drugs have occasionally been found, but such discoveries have been serendipitous. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. We used PRISM, a molecular barcoding method, to screen drugs against cell lines in pools. An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the cell lines' molecular features. Our findings include compounds that killed by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing depended on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which killed cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, which killed via the multi-drug resistance protein ABCB1. The PRISM drug repurposing resource (https://depmap.org/repurposing) is a starting point to develop new oncology therapeutics, and more rarely, for potential direct clinical translation.

Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling

Anti-cancer uses of non-oncology drugs have occasionally been found, but such discoveries have been serendipitous. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. We used PRISM, a molecular barcoding method, to screen drugs against cell lines in pools. An unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines in a manner predictable from the cell lines' molecular features. Our findings include compounds that killed by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing depended on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which killed cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, which killed via the multi-drug resistance protein ABCB1. The PRISM drug repurposing resource (https://depmap.org/repurposing) is a starting point to develop new oncology therapeutics, and more rarely, for potential direct clinical translation.

High Throughput Screen Identifies Interferon γ-Dependent Inhibitors of Toxoplasma gondii Growth

Toxoplasma gondii is an obligate intracellular parasite capable of causing severe disease due to congenital infection and in patients with compromised immune systems. Control of infection is dependent on a robust Th1 type immune response including production of interferon gamma (IFN-γ), which is essential for control. IFN-γ activates a variety of antimicrobial mechanisms in host cells, which are then able to control intracellular parasites such as T. gondii. Despite the effectiveness of these pathways in controlling acute infection, the immune system is unable to eradicate chronic infections that can persist for life. Similarly, while antibiotic treatment can control acute infection, it is unable to eliminate chronic infection. To identify compounds that would act synergistically with IFN-γ, we performed a high-throughput screen of diverse small molecule libraries to identify inhibitors of T. gondii. We identified a number of compounds that inhibited parasite growth in vitro at low μM concentrations and that demonstrated enhanced potency in the presence of a low level of IFN-γ. A subset of these compounds act by enhancing the recruitment of light chain 3 (LC3) to the parasite-containing vacuole, suggesting they work by an autophagy-related process, while others were independent of this pathway. The pattern of IFN-γ dependence was shared among the majority of analogs from 6 priority scaffolds, and analysis of structure activity relationships for one such class revealed specific stereochemistry associated with this feature. Identification of these IFN-γ-dependent leads may lead to development of improved therapeutics due to their synergistic interactions with immune responses.

High-Throughput Screens To Identify Autophagy Inducers That Function by Disrupting Beclin 1/Bcl-2 Binding

Autophagy, a lysosomal degradation pathway, plays a crucial role in cellular homeostasis, development, immunity, tumor suppression, metabolism, prevention of neurodegeneration, and lifespan extension. Thus, pharmacological stimulation of autophagy may be an effective approach for preventing or treating certain human diseases and/or aging. We sought to establish a method for developing new chemical compounds that specifically induce autophagy. To do this, we developed two assays to identify compounds that target a key regulatory node of autophagy induction-specifically, the binding of Bcl-2 (a negative regulator of autophagy) to Beclin 1 (an allosteric modulator of the Beclin 1/VPS34 lipid kinase complex that functions in autophagy initiation). These assays use either a split-luciferase assay to measure Beclin 1/Bcl-2 binding in cells or an AlphaLISA assay to directly measure direct Beclin 1/Bcl-2 binding in vitro. We screened two different chemical compound libraries, comprising ∼300 K compounds, to identify small molecules that disrupt Beclin 1/Bcl-2 binding and induce autophagy. Three novel compounds were identified that directly inhibit Beclin 1/Bcl-2 interaction with an IC₅₀ in the micromolar range and increase autophagic flux. These compounds do not demonstrate significant cytotoxicity, and they exert selectivity for disruption of Bcl-2 binding to the BH3 domain of Beclin 1 compared with the BH3 domain of the pro-apoptotic Bcl-2 family members, Bax and Bim. Thus, we have identified candidate molecules that serve as lead templates for developing potent and selective Beclin 1/Bcl-2 inhibitors that may be clinically useful as autophagy-inducing agents.

A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay

Background

Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications.

Findings

This microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at “The Cell Image Library” (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied.

Conclusions

Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.

Identification of Highly Specific Diversity-Oriented Synthesis-Derived Inhibitors of Clostridium difficile

In 2013, the Centers for Disease Control highlighted Clostridium difficile as an urgent threat for antibiotic-resistant infections, in part due to the emergence of highly virulent fluoroquinolone-resistant strains. Limited therapeutic options currently exist, many of which result in disease relapse. We sought to identify molecules specifically targeting C. difficile in high-throughput screens of our diversity-oriented synthesis compound collection. We identified two scaffolds with apparently novel mechanisms of action that selectively target C. difficile while having little to no activity against other intestinal anaerobes; preliminary evidence suggests that compounds from one of these scaffolds target the glutamate racemase. In vivo efficacy data suggest that both compound series may provide lead optimization candidates.

Correlating chemical sensitivity and basal gene expression reveals mechanism of action

Changes in cellular gene expression in response to small-molecule or genetic perturbations have yielded signatures that can connect unknown mechanisms of action (MoA) to ones previously established. We hypothesized that differential basal gene expression could be correlated with patterns of small-molecule sensitivity across many cell lines to illuminate the actions of compounds whose MoA are unknown. To test this idea, we correlated the sensitivity patterns of 481 compounds with ∼19,000 basal transcript levels across 823 different human cancer cell lines and identified selective outlier transcripts. This process yielded many novel mechanistic insights, including the identification of activation mechanisms, cellular transporters and direct protein targets. We found that ML239, originally identified in a phenotypic screen for selective cytotoxicity in breast cancer stem-like cells, most likely acts through activation of fatty acid desaturase 2 (FADS2). These data and analytical tools are available to the research community through the Cancer Therapeutics Response Portal.

Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

Identifying genetic alterations that prime a cancer cell to respond to a particular therapeutic agent can facilitate the development of precision cancer medicines. Cancer cell-line (CCL) profiling of small-molecule sensitivity has emerged as an unbiased method to assess the relationships between genetic or cellular features of CCLs and small-molecule response. Here, we developed annotated cluster multidimensional enrichment analysis to explore the associations between groups of small molecules and groups of CCLs in a new, quantitative sensitivity dataset. This analysis reveals insights into small-molecule mechanisms of action, and genomic features that associate with CCL response to small-molecule treatment. We are able to recapitulate known relationships between FDA-approved therapies and cancer dependencies and to uncover new relationships, including for KRAS-mutant cancers and neuroblastoma. To enable the cancer community to explore these data, and to generate novel hypotheses, we created an updated version of the Cancer Therapeutic Response Portal (CTRP v2).

SIGNIFICANCE

We present the largest CCL sensitivity dataset yet available, and an analysis method integrating information from multiple CCLs and multiple small molecules to identify CCL response predictors robustly. We updated the CTRP to enable the cancer research community to leverage these data and analyses.

Advancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes

Small-molecule probes can illuminate biological processes and aid in the assessment of emerging therapeutic targets by perturbing biological systems in a manner distinct from other experimental approaches. Despite the tremendous promise of chemical tools for investigating biology and disease, small-molecule probes were unavailable for most targets and pathways as recently as a decade ago. In 2005, the NIH launched the decade-long Molecular Libraries Program with the intent of innovating in and broadening access to small-molecule science. This Perspective describes how novel small-molecule probes identified through the program are enabling the exploration of biological pathways and therapeutic hypotheses not otherwise testable. These experiences illustrate how small-molecule probes can help bridge the chasm between biological research and the development of medicines but also highlight the need to innovate the science of therapeutic discovery.

Target-based identification of whole-cell active inhibitors of biotin biosynthesis in Mycobacterium tuberculosis

Biotin biosynthesis is essential for survival and persistence of Mycobacterium tuberculosis (Mtb) in vivo. The aminotransferase BioA, which catalyzes the antepenultimate step in the biotin pathway, has been established as a promising target due to its vulnerability to chemical inhibition. We performed high-throughput screening (HTS) employing a fluorescence displacement assay and identified a diverse set of potent inhibitors including many diversity-oriented synthesis (DOS) scaffolds. To efficiently select only hits targeting biotin biosynthesis, we then deployed a whole-cell counterscreen in biotin-free and biotin-containing medium against wild-type Mtb and in parallel with isogenic bioA Mtb strains that possess differential levels of BioA expression. This counterscreen proved crucial to filter out compounds whose whole-cell activity was off target as well as identify hits with weak, but measurable whole-cell activity in BioA-depleted strains. Several of the most promising hits were cocrystallized with BioA to provide a framework for future structure-based drug design efforts.

BioAssay Research Database (BARD): chemical biology and probe-development enabled by structured metadata and result types

BARD, the BioAssay Research Database (https://bard.nih.gov/) is a public database and suite of tools developed to provide access to bioassay data produced by the NIH Molecular Libraries Program (MLP). Data from 631 MLP projects were migrated to a new structured vocabulary designed to capture bioassay data in a formalized manner, with particular emphasis placed on the description of assay protocols. New data can be submitted to BARD with a user-friendly set of tools that assist in the creation of appropriately formatted datasets and assay definitions. Data published through the BARD application program interface (API) can be accessed by researchers using web-based query tools or a desktop client. Third-party developers wishing to create new tools can use the API to produce stand-alone tools or new plug-ins that can be integrated into BARD. The entire BARD suite of tools therefore supports three classes of researcher: those who wish to publish data, those who wish to mine data for testable hypotheses, and those in the developer community who wish to build tools that leverage this carefully curated chemical biology resource.

Evolving BioAssay Ontology (BAO): modularization, integration and applications

The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.

An Overview of the Challenges in Designing, Integrating, and Delivering BARD: A Public Chemical-Biology Resource and Query Portal for Multiple Organizations, Locations, and Disciplines

Recent industry-academic partnerships involve collaboration among disciplines, locations, and organizations using publicly funded "open-access" and proprietary commercial data sources. These require the effective integration of chemical and biological information from diverse data sources, which presents key informatics, personnel, and organizational challenges. The BioAssay Research Database (BARD) was conceived to address these challenges and serve as a community-wide resource and intuitive web portal for public-sector chemical-biology data. Its initial focus is to enable scientists to more effectively use the National Institutes of Health Roadmap Molecular Libraries Program (MLP) data generated from the 3-year pilot and 6-year production phases of the Molecular Libraries Probe Production Centers Network (MLPCN), which is currently in its final year. BARD evolves the current data standards through structured assay and result annotations that leverage BioAssay Ontology and other industry-standard ontologies, and a core hierarchy of assay definition terms and data standards defined specifically for small-molecule assay data. We initially focused on migrating the highest-value MLP data into BARD and bringing it up to this new standard. We review the technical and organizational challenges overcome by the interdisciplinary BARD team, veterans of public- and private-sector data-integration projects, who are collaborating to describe (functional specifications), design (technical specifications), and implement this next-generation software solution.

An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules

The high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene β-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.

ML212: A small-molecule probe for investigating fluconazole resistance mechanisms in Candida albicans

The National Institutes of Health Molecular Libraries and Probe Production Centers Network (NIH-MLPCN) screened >300,000 compounds to evaluate their ability to restore fluconazole susceptibility in resistant Candida albicans isolates. Additional counter screens were incorporated to remove substances inherently toxic to either mammalian or fungal cells. A substituted indazole possessing the desired bioactivity profile was selected for further development, and initial investigation of structure–activity relationships led to the discovery of ML212.

Automatically detecting workflows in PubChem

Public databases that store the data from small-molecule screens are a rich and untapped resource of chemical and biological information. However, screening databases are unorganized, which makes interpreting their data difficult. We propose a method of inferring workflow graphs--which encode the relationships between assays in screening projects--directly from screening data and using these workflows to organize each project's data. On the basis of four heuristics regarding the organization of screening projects, we designed an algorithm that extracts a project's workflow graph from screening data. Where possible, the algorithm is evaluated by comparing each project's inferred workflow to its documentation. In the majority of cases, there are no discrepancies between the two. Most errors can be traced to points in the project where screeners chose additional molecules to test based on structural similarity to promising molecules, a case our algorithm is not yet capable of handling. Nonetheless, these workflows accurately organize most of the data and also provide a method of visualizing a screening project. This method is robust enough to build a workflow-oriented front-end to PubChem and is currently being used regularly by both our lab and our collaborators. A Python implementation of the algorithm is available online, and a searchable database of all PubChem workflows is available at http://swami.wustl.edu/flow.

Overcoming fluconazole resistance in Candida albicans clinical isolates with tetracyclic indoles

Continuing efforts to discover novel means of combating fluconazole resistance in Candida albicans have identified an indole derivative that sensitizes strains demonstrating resistance to fluconazole. This tetracycle (3, ML229) does not appear to act through established Hsp90 or calcineurin pathways to chemosensitize C. albicans, as determined in Saccharomyces cerevisiae models, and may be a useful probe to uncover alternative resistance pathways.

High-Throughput RT-PCR for small-molecule screening assays

Quantitative measurement of the levels of mRNA expression using real-time reverse transcription polymerase chain reaction (RT-PCR) has long been used for analyzing expression differences in tissue or cell lines of interest. This method has been used somewhat less frequently to measure the changes in gene expression due to perturbagens such as small molecules or siRNA. The availability of new instrumentation for liquid handling and real-time PCR analysis as well as the commercial availability of start-to-finish kits for RT-PCR has enabled the use of this method for high-throughput small-molecule screening on a scale comparable to traditional high-throughput screening (HTS) assays. This protocol focuses on the special considerations necessary for using quantitative RT-PCR as a primary small-molecule screening assay, including the different methods available for mRNA isolation and analysis.

Utility-aware screening with clique-oriented prioritization

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 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 3-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.

Development of small-molecule probes that selectively kill cells induced to express mutant RAS

Synthetic lethal screening is a chemical biology approach to identify small molecules that selectively kill oncogene-expressing cell lines with the goal of identifying pathways that provide specific targets against cancer cells. We performed a high-throughput screen of 303,282 compounds from the National Institutes of Health-Molecular Libraries Small Molecule Repository (NIH-MLSMR) against immortalized BJ fibroblasts expressing HRAS(G12V) followed by a counterscreen of lethal compounds in a series of isogenic cells lacking the HRAS(G12V) oncogene. This effort led to the identification of two novel molecular probes (PubChem CID 3689413, ML162 and CID 49766530, ML210) with nanomolar potencies and 4-23-fold selectivities, which can potentially be used for identifying oncogene-specific pathways and targets in cancer cells.

Enhancing the rate of scaffold discovery with diversity-oriented prioritization

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.

An economic framework to prioritize confirmatory tests after a high-throughput screen

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, the authors 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 and 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 that had been erroneously labeled inactive in at least one real-world screening experiment.

Functional dissection of sRNA translational regulators by nonhomologous random recombination and in vivo selection

Small nontranslated RNAs (sRNAs) regulate a variety of biological processes. DsrA and OxyS are two E. coli sRNAs that regulate the translation of rpoS, which encodes a protein sigma factor. Due to their structural complexity, the functional dissection of sRNAs solely by designing and assaying mutants can be challenging. Here, we present a complementary approach to the study of functional RNAs, in which highly diversified RNA libraries are generated by nonhomologous random recombination (NRR) and processed efficiently by in vivo selections that link RNA activities to cell survival. When applied to DsrA and OxyS, this approach rapidly identified essential and nonessential regions of both sRNAs. Resulting hypotheses about DsrA and OxyS structure-function relationships were tested and further refined experimentally. Our findings demonstrate an efficient, unbiased approach to the functional dissection of nucleic acids.

Directed evolution of protein enzymes using nonhomologous random recombination

We recently reported the development of nonhomologous random recombination (NRR) as a method for nucleic acid diversification and applied NRR to the evolution of DNA aptamers. Here, we describe a modified method, protein NRR, that enables proteins to access diversity previously difficult or impossible to generate. We investigated the structural plasticity of protein folds and the ability of helical motifs to function in different contexts by applying protein NRR and in vivo selection to the evolution of chorismate mutase (CM) enzymes. Functional CM mutants evolved using protein NRR contained many insertions, deletions, and rearrangements. The distribution of these changes was not random but clustered in certain regions of the protein. Topologically rearranged but functional enzymes also emerged from these studies, indicating that multiple connectivities can accommodate a functional CM active site and demonstrating the ability to generate new domain connectivities through protein NRR. Protein NRR was also used to randomly recombine CM and fumarase, an unrelated but also alpha-helical protein. Whereas the resulting library contained fumarase fragments in many contexts before functional selection, library members surviving selection for CM activity invariably contained a CM core with fumarase sequences found only at the termini or in one loop. These results imply that internal helical fragments cannot be swapped between these proteins without the loss of nearly all CM activity. Our findings suggest that protein NRR will be useful in probing the functional requirements of enzymes and in the creation of new protein topologies.

Nucleic acid evolution and minimization by nonhomologous random recombination

We have developed a simple method for exploring nucleic acid sequence space by nonhomologous random recombination (NRR) that enables DNA fragments to randomly recombine in a length-controlled manner without the need for sequence homology. We compared the results of using NRR and error-prone PCR to evolve DNA aptamers that bind streptavidin. Starting with two parental sequences of modest avidin affinity, evolution using NRR resulted in aptamers with 15- to 20-fold higher affinity than the highest-affinity aptamers evolved using error-prone PCR, and 27- or 46-fold higher affinities than parental sequences derived using systematic evolution of ligands by exponential enrichment (SELEX). NRR also facilitates the identification of functional regions within evolved sequences. Inspection of a small number of NRR-evolved clones identified a 40-base DNA sequence, present in multiple copies in each clone, that binds streptavidin. Our findings suggest that NRR may enhance the effectiveness of nucleic acid evolution and the ease of identifying structure-activity relationships among evolved sequences.

Recent advances in the in vitro evolution of nucleic acids

Molecular evolution allows chemists and biologists to generate nucleic acids with tailor-made binding or catalytic activities. Recent examples of nucleic acid evolution in vitro provide insights into natural ribozyme evolution and also demonstrate potential applications of evolved DNA and RNA molecules. Efforts to expand the scope of nucleic acid evolution are also underway, including the development of novel methods for exploring nucleic acid sequence-space and the incorporation of non-natural chemical functionality into nucleic acid libraries.