DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms

The Drug-Gene Interaction Database (DGIdb, https://dgidb.org) is a publicly accessible resource that aggregates genes or gene products, drugs and drug-gene interaction records to drive hypothesis generation and discovery for clinicians and researchers. DGIdb 5.0 is the latest release and includes substantial architectural and functional updates to support integration into clinical and drug discovery pipelines. The DGIdb service architecture has been split into separate client and server applications, enabling consistent data access for users of both the application programming interface (API) and web interface. The new interface was developed in ReactJS, and includes dynamic visualizations and consistency in the display of user interface elements. A GraphQL API has been added to support customizable queries for all drugs, genes, annotations and associated data. Updated documentation provides users with example queries and detailed usage instructions for these new features. In addition, six sources have been added and many existing sources have been updated. Newly added sources include ChemIDplus, HemOnc, NCIt (National Cancer Institute Thesaurus), Drugs@FDA, HGNC (HUGO Gene Nomenclature Committee) and RxNorm. These new sources have been incorporated into DGIdb to provide additional records and enhance annotations of regulatory approval status for therapeutics. Methods for grouping drugs and genes have been expanded upon and developed as independent modular normalizers during import. The updates to these sources and grouping methods have resulted in an improvement in FAIR (findability, accessibility, interoperability and reusability) data representation in DGIdb.

Abstract 1197: Refining the drug-gene interaction database for precision medicine pipelines

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a publicly accessible resource that aggregates 102,426 gene records and 57,498 drug records from 40 drug-gene interaction data sources to aid both researchers and clinicians in identifying associations between genes of interest and available drugs and therapeutics. By using peer-reviewed data sources and publications, DGIdb represents a stand-alone resource with over 100,000 drug-gene interaction claims across 30 interaction types to drive hypothesis generation in precision medicine and interpretation pipelines. The background process that normalizes drugs to a harmonized ontological concept has been upgraded. These improvements have increased concept normalization for drugs by 20% and are now available as a stand-alone service for use (https://normalize.cancervariants.org/therapy/). Leveraging our platform’s ability to find relationships between disease-critical genes and available therapeutics, DGIdb has been used in clinical interpretation pipelines to find drugs for specific diseases with an emphasis on regulatory approval status. DGIdb now uses annotations from Drugs@FDA as an additional source to provide more accurate descriptors for market and maturity status of drugs, when available. Lastly, to enhance the annotation potential for DGIdb in precision medicine pipelines, we have updated our druggable gene category sources with an additional curated list of 2,217 genes. Used alone or in combination with existing categories-such as the heavily-utilized ‘clinically actionable’ category-this additional source will give precision medicine and interpretation pipelines the power to find concise, actionable annotations for specific diseases including pediatric cancers and epilepsy. These lists are managed and maintained as a publicly-available resource to provide up-to-date annotations on disease-associated genes as they become available.

Citation Format: Matthew Cannon, James Stevenson, Kori Kuzma, Colin O'Sullivan, Katherine Miller, Olivia Grischow, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Dorian Morrissey, Kelsy Cotto, Obi Griffith, Malachi Griffith, Alex Wagner. Refining the drug-gene interaction database for precision medicine pipelines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1197.

Abstract 210: Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration

Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer.

Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kesserwan A. Chimene, Laura Fuqua, Lisa Dyer, Huiling Xu, Jeffrey Jean, Laveniya Satgunaseelan, Liying Zhang, Ted W. Laetsch, Donald W. Parsons, Ryan Schmidt, Lynn M. Schriml, Kristen L. Sund, Shashikant Kulkarni, Subha Madhavan, Xinjie Xu, Rashmi Kanagal-Shamana, Marian Harris, Yasmine Akkari, Nurit Paz Yacov, Panieh Terraf, Malachi Griffith, Obi L. Griffith, Gordana Raca. Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 210.

Abstract 206: CIViC knowledgebase adapts to field experts and community input

CIViC (civicdb.org) is an open access, expertly moderated knowledgebase for crowdsourcing Clinical Interpretations of Variants in Cancer. Stakeholders globally-including those in government, academia, industry and medicine-use CIViC to find and curate actionable interpretations of genomic variants in their therapeutic, prognostic, predisposing, diagnostic and functional contexts. Through engagement with curators and leaders in the field, CIViC has implemented several features including Assertions, Organizations and expanded help documentation.

The foundational unit of CIViC is the Evidence Item, which describes the clinical relevance of a specific variant curated from a single published source within peer-reviewed literature or ASCO abstract. Assertions aggregate Evidence Items for a given variant-disease or variant-disease-therapy combination. In response to the 2017 AMP-ASCO-CAP guidelines and collaborations with ClinGen, Assertions were modified to integrate ACMG variant pathogenicity classifications, AMP-ASCO-CAP tier designations and associations with NCCN guidelines and FDA approvals to provide a ‘state of the field' interpretation. At present, 12 Assertions spanning eight variants have been submitted by CIViC to ClinVar with one-star submitter status (Submitter ID: 506594), and CIViC has been cited as supporting information in two variants. Assertions exemplify CIViC's responsiveness to new field guidelines, expert collaborators' recommendations and its contributions to other resources.

To enhance community involvement, CIViC created Organization-attributed actions. Each action performed by a curator is tagged with their Organization. Curators may switch between Organizations if they belong to more than one. Currently, nine Organizations are recognized in CIViC, the largest being ClinGen with 79 members, 4 sub-organizations and over 24,000 actions. Organizations enable groups to prominently display and track their submissions, activity, and users.

CIViC's wide adoption has necessitated the development of robust educational material. CIViC has created nine YouTube videos, one of which is linked by the NIH ITCR homepage. CIViC has migrated help documents to a stand alone site (civic.readthedocs.io) and has made over 60 page modifications since 2019. Help documentation expansion was fueled by user feedback via the CIViC interface, collaborator meetings and in-person events (Curation Jamborees). Improved documentation allows CIViC to grow at scale, unhindered by the need for direct training.

CIViC's rapid adaptation to the needs of the community is derived from its open access nature, commitment to data provenance, active connection with users, and abundance of educational material. CIViC rapidly integrates the guidelines, regulatory standards and community recommendations in a freely accessible resource that is flexible enough to evolve with the dynamic field of cancer genomics.

Citation Format: Lana M. Sheta, Arpad M. Danos, Jason Saliba, Kilannin Krysiak, Alex H. Wagner, Erica K. Barnell, Susanna Kiwala, Joshua F. McMichael, Adam Coffman, Shahil Pema, Lynzey Kujan, Kelsy C. Cotto, Cody Ramirez, Zachary L. Skidmore, Cameron J. Grisdale, Shruti Rao, Subha Madhaven, Malachi Griffith, Obi L. Griffith. CIViC knowledgebase adapts to field experts and community input [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 206.

Abstract 208: Development of Evidence Statement curation algorithms to aid cancer variant interpretation

The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open access, centralized hub for structured, community curated and expertly moderated relationships between genomic variants and cancer. Evidence is curated from peer-reviewed, published literature and is classified into one of five Types: Predisposing, Diagnostic, Prognostic, Predictive (therapeutic), or Functional. The robustness of the Evidence is conveyed through the assignment of Levels with the first three derived from patient studies (Validated, Clinical, Case Study), Preclinical, generated from in vivo or in vitro data, and Inferential, which describes indirect associations.

Each Evidence Item requires an Evidence Statement written in the curator's own words summarizing the source's results regarding the variant's clinical impact. Collaborations with groups like ClinGen have generated a significant influx of new curators, increasing the demand for detailed principles regarding data prioritization in the Evidence Statement in order to streamline the curation process. The curation community would benefit from simpler, visual guides through the complex decisions needed to appropriately and consistently curate Evidence Items. We are devoting significant effort to continue the development of straightforward Evidence curation algorithms (decision trees) similar to those used in clinical molecular testing labs to aid CIViC curators.

Previously published guidelines on development of these statements are the basis of our Evidence algorithms. Obvious inflection points for curators are clearly identified with specific details noted for each to optimize decision efficiency. As the predominant Evidence Type comprising 57% of all CIViC submissions, 58% of referenced patient trials, and 92% of Preclinical submissions, Predictive Evidence is the initial focus of our pilot guidelines with Diagnostic and Prognostic to follow. Within the Predictive Evidence Type, clinical trials, case studies, and preclinical Levels each require vastly different Evidence Statement details and ultimately the creation of three separate, uniquely modeled algorithms.

The implementation of these algorithms will assist in streamlining both curation and the expert review process. Notably, a template is not being created, as the preservation of curator style and voice is important to maintain the community feel of the database. To ensure the highest level of clarity, our team is utilizing specific novice and experienced curators to assist with the development process. As these algorithms pass the pilot phase, they are being tested as curator training tools. Ultimately, these guidelines will be used to encourage independence in curators and to enhance the Evidence already contained in CIViC.

Citation Format: Jason Saliba, Lana Sheta, Kilannin Krysiak, Arpad Danos, Alex Marr, Erica Barnell, Shahil Pema, Wan-Hsin Lin, Panieh Terraf, Joshua F. McMichael, Cameron J. Grisdale, Shruti Rao, Susanna Kiwala, Adam Coffman, Alex Wagner, Obi L. Griffith, Malachi Griffith. Development of Evidence Statement curation algorithms to aid cancer variant interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 208.

Abstract 1096: Harmonization standards from the Variant Interpretation for Cancer Consortium

The use of clinical gene sequencing is now commonplace, and genome analysts and molecular pathologists are often tasked with the labor-intensive process of interpreting the clinical significance of large numbers of tumor variants. Numerous independent knowledgebases have been constructed to alleviate this manual burden, however these knowledgebases are non-interoperable. As a result, the analyst is left with a difficult tradeoff: for each knowledgebase used the analyst must understand the nuances particular to that resource and integrate its evidence accordingly when generating the clinical report, but for each knowledgebase omitted there is increased potential for missed findings of clinical significance.The Variant Interpretation for Cancer Consortium (VICC; cancervariants.org) was formed as a driver project of the Global Alliance for Genomics and Health (GA4GH; ga4gh.org) to address this concern. VICC members include representatives from several major somatic interpretation knowledgebases including CIViC, OncoKB, Jax-CKB, the Weill Cornell PMKB, the IRB-Barcelona Cancer Biomarkers Database, and others. Previously, the VICC built and reported on a harmonized meta-knowledgebase of 19,551 biomarker associations of harmonized variants, diseases, drugs, and evidence across the constituent resources.In that study, we analyzed the frequency with which the tumor samples from the AACR Project GENIE cohort would match to harmonized associations. Variant matches increased dramatically from 57% to 86% when broader matching to regions describing categorical variants were allowed. Unlike precise sequence variants with specified alternate alleles, categorical variants describe a collection of potential variants with a common feature, such as “V600” (non-valine alleles at the 600 residue), “Exon 20 mutations” (all non-silent mutations in exon 20), or “Gain-of-function” (hypermorphic alterations that activate or amplify gene activity). However, matching observed sequence variants to categorical variants is challenging, as the latter are typically only described as unstructured text. Here we describe the expressive and computational GA4GH Variation Representation specification (vr-spec.readthedocs.io), which we co-developed as members of the GA4GH Genomic Knowledge Standards work stream. This specification provides a schema for common, precise forms of variation (e.g. SNVs and Indels) and the method for computing identifiers from these objects. We highlight key aspects of the specification and our work to apply it to the characterization of categorical variation, showcasing the variant terminology and classification tools developed by the VICC to support this effort. These standards and tools are free, open-source, and extensible, overcoming barriers to standardized variant knowledge sharing and search.

Citation Format: Alex H. Wagner, Reece K. Hart, Larry Babb, Robert R. Freimuth, Adam Coffman, Yonghao Liang, Beth Pitel, Angshumoy Roy, Matthew Brush, Jennifer Lee, Anna Lu, Thomas Coard, Shruti Rao, Deborah Ritter, Brian Walsh, Susan Mockus, Peter Horak, Ian King, Dmitriy Sonkin, Subha Madhavan, Gordana Raca, Debyani Chakravarty, Malachi Griffith, Obi L. Griffith. Harmonization standards from the Variant Interpretation for Cancer Consortium [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1096.

Abstract 1290: Expanding the CIViC variant to complex combinations of regions in the cancer genome

The Clinical Interpretations of Variants in Cancer (CIViC) database was designed as a fully open-access resource specifically focusing on the subset of cancer variants with well-defined clinical information associated to them and targeting a broad user base in cancer, including clinicians, researchers and patient advocates. CIViC follows a crowd-sourced curation model with expert moderation, and emphasizes transparency in that all changes, approvals, and discussion of content are tracked and displayed in the interface. Evidence items (EIDs) make up the fundamental unit of CIViC. EIDs connect a predictive, prognostic, diagnostic or predisposing clinical significance to a variant in the context of a specific disease, and each EID links back to specific published and peer-reviewed evidence. Multiple EIDs of high value can be used to make clinical assertions. While the CIViC variant is intentionally broad and can capture specific SNVs and Indels, while also admitting umbrella variants such as “EGFR mutation,” it is also anchored to a single gene entity (pulled directly from the NCBI Entrez database). Two drawbacks result from this strict association. First, clinical information associated with multiple genes is difficult to implement in knowledgebases like CIViC. In cases where two variants co-occur on the same gene, such as the erlotinib-sensitizing EGFR L858R and resistance mutation T790M, CIViC allows a “L858R and T790M” variant to be created under the EGFR gene. In other cases, such as FLT3 internal tandem duplication and DNMT3A mutation in AML, there is no CIViC variant that can capture this combination. In addition, non-gene entities important in cancer such as viral oncogene, microsatellite instability, or loss of chromosome region are not possible to represent. To address this, we are preparing CIViC V2, which will be implemented via modification of the existing database and web interface. The architecture around the EID as fundamental unit of CIViC will remain unchanged, but the CIViC variant will be re-envisioned, introducing the concepts of region and genotype. A variant will link to one or more regions, and region will be drawn from a list of types—genome, chromosome, rearrangement, gene and pathogen—which can be expanded when needed. The notion of complex genotype will be realized as a combination of variants drawn from multiple regions in either an additive manner (HER2 overexpression AND PIK3CA mutation) or employing more complex variant combinations using OR and NOT. Multicomponent genotypes will point back to EID collections associated with each individual component, while also enabling users to write EIDs unique to that genotype, which can, in turn, be used to create genotype clinical assertions. This new scheme will be presented via refactoring of the UI, and allow for a highly flexible concept of region and genotype reflecting the growing understanding of the combinatorial nature of variants in cancer.

Citation Format: Arpad Danos, Kilannin Krysiak, Alex Wagner, Susanna Kiwala, Joshua McMichael, Adam Coffman, Erica Barnell, Yang-Yang Feng, Benjamin Ainscough, Cody Ramirez, Malachi Griffith, Obi Griffith. Expanding the CIViC variant to complex combinations of regions in the cancer genome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1290.

Cancer Immunogenomics: Computational Neoantigen Identification and Vaccine Design

The application of modern high-throughput genomics to the study of cancer genomes has exploded in the past few years, yielding unanticipated insights into the myriad and complex combinations of genomic alterations that lead to the development of cancers. Coincident with these genomic approaches have been computational analyses that are capable of multiplex evaluations of genomic data toward specific therapeutic end points. One such approach is called “immunogenomics” and is now being developed to interpret protein-altering changes in cancer cells in the context of predicted preferential binding of these altered peptides by the patient’s immune molecules, specifically human leukocyte antigen (HLA) class I and II proteins. One goal of immunogenomics is to identify those cancer-specific alterations that are likely to elicit an immune response that is highly specific to the patient’s cancer cells following stimulation by a personalized vaccine. The elements of such an approach are outlined herein and constitute an emerging therapeutic option for cancer patients.

Abstract PR01: Identifying clinically important somatic mutations through a knowledge-based approach

Large-scale tumor sequencing projects, like The Cancer Genome Atlas (TCGA), have implicated thousands of somatic mutations in cancer. These initiatives have incentivized many improvements in somatic variant detection. However, we have observed that important pathogenic variants are often missed due to stringent filtering, tumor heterogeneity, tumor contamination of normal, low tumor purity, alignment challenges, and other issues. These idiosyncrasies can impede variant detection algorithms from reliably calling even the most clinically relevant variants. To rescue this missed variation we devised a knowledge based variant identification strategy. We mined the literature and other variant databases for pathogenic variation and assembled them into an integrated Database of Curated Mutations (DoCM - www.docm.info). The DoCM contains 488 variants across 63 genes implicated in 34 cancer types. We developed an algorithm to identify any pathogenic variant signal, for all variants in the DoCM, in aligned sequence data. As a proof of principle, we applied this approach to four cancer types sequenced by TCGA: acute myeloid leukemia (AML), breast cancer, ovarian carcinoma, and uterine corpus endometrial carcinoma. Obvious sequencing and alignment errors, like variants in homopolymer runs, were excluded from subsequent analysis by manual review. Across these four TCGA projects, which includes 1,840 individuals, 1,757 clinically relevant variants were identified, 1,223 of which had not been previously reported in TCGA studies. To validate this approach, custom capture probes were designed for all of the DoCM variants, new libraries constructed and deep sequencing performed on 96 tumor and matched normal samples from the AML and breast cancer TCGA projects. Following this strategy, we were able to confirm the rescue of clinically relevant somatic mutations that were missed in the original TCGA analysis. We propose a knowledge-driven variant detection approach be considered as standard practice to avoid false-negative calls of events likely to be clinically relevant

Citation Format: Benjamin J. Ainscough, Malachi Griffith, Jason Kunisaki, Adam Coffman, Joshua F. McMichael, James M. Eldred, Jason R. Walker, Robert S. Fulton, Richard K. Wilson, Obi L. Griffith, Elaine R. Mardis. Identifying clinically important somatic mutations through a knowledge-based approach. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR01.

Abstract A2-42: Identifying clinically important somatic mutations through a knowledge-based approach

Large-scale tumor sequencing projects, like The Cancer Genome Atlas (TCGA), have implicated thousands of somatic mutations in cancer. These initiatives have incentivized many improvements in somatic variant detection. However, we have observed that important pathogenic variants are often missed due to stringent filtering, tumor heterogeneity, tumor contamination of normal, low tumor purity, alignment challenges, and other issues. These idiosyncrasies can impede variant detection algorithms from reliably calling even the most clinically relevant variants. To rescue this missed variation we devised a knowledge based variant identification strategy. We mined the literature and other variant databases for pathogenic variation and assembled them into an integrated Database of Curated Mutations(DoCM - www.docm.info). The DoCM contains 488 variants across 63 genes implicated in 34 cancer types. We developed an algorithm to identify any pathogenic variant signal, for all variants in the DoCM, in aligned sequence data. As a proof of principle, we applied this approach to four cancer types sequenced by TCGA: acute myeloid leukemia (AML), breast cancer, ovarian carcinoma, and uterine corpus endometrial carcinoma. Obvious sequencing and alignment errors, like variants in homopolymer runs, were excluded from subsequent analysis by manual review. Across these four TCGA projects, which includes 1,840 individuals, 1,757 clinically relevant variants were identified, 1,223 of which had not been previously reported in TCGA studies. To validate this approach, custom capture probes were designed for all of the DoCM variants, new libraries constructed and deep sequencing performed on 96 tumor and matched normal samples from the AML and breast cancer TCGA projects. Following this strategy, we were able to confirm the rescue of clinically relevant somatic mutations that were missed in the original TCGA analysis. We propose a knowledge-driven variant detection approach be considered as standard practice to avoid false-negative calls of events likely to be clinically relevant.

Citation Format: Benjamin J. Ainscough, Malachi Griffith, Jason Kunisaki, Adam Coffman, Joshua F. McMichael, James M. Eldred, Jason R. Walker, Robert S. Fulton, Richard K. Wilson, Obi L. Griffith, Elaine R. Mardis. Identifying clinically important somatic mutations through a knowledge-based approach. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-42.

Automated Scheduling for NASA's Deep Space Network

This article describes the DSN scheduling wngine (DSE) component of a new scheduling system being deployed for NASA's deep space network. The DSE provides core automation functionality for scheduling the network, including the interpretation of scheduling requirements expressed by users, their elaboration into tracking passes, and the resolution of conflicts and constraint violations. The DSE incorporates both systematic search and repair-based algorithms, used for different phases and purposes in the overall system. It has been integrated with a web application which provides DSE functionality to all DSN users through a standard web browser, as part of a peer-to-peer schedule negotiation process for the entire network. The system has been deployed operationally and is in routine use, and is in the process of being extended to support long-range planning and forecasting, and near-real-time scheduling.

Renaturation of rhodanese by translational elongation factor (EF) Tu. Protein refolding by EF-Tu flexing

The translation elongation factor (EF) Tu has chaperone-like capacity to promote renaturation of denatured rhodanese. This renaturation activity is greatly increased under conditions in which the factor can oscillate between the open and closed conformations that are induced by GDP and GTP, respectively. Oscillation occurs during GTP hydrolysis and subsequent replacement of GDP by EF-Ts which is then displaced by GTP. Renaturation of rhodanese and GTP hydrolysis by EF-Tu are greatly enhanced by the guanine nucleotide exchange factor EF-Ts. However, renaturation is reduced under conditions that stabilize EF-Tu in either the open or closed conformation. Both GDP and the nonhydrolyzable analog of GTP, GMP-PCP, inhibit renaturation. Kirromycin and pulvomycin, antibiotics that specifically bind to EF-Tu and inhibit its activity in peptide elongation, also strongly inhibit EF-Tu-mediated renaturation of denatured rhodanese to levels near those observed for spontaneous, unassisted refolding. Kirromycin locks EF-Tu in the open conformation in the presence of either GTP or GDP, whereas pulvomycin locks the factor in the closed conformation. The results lead to the conclusion that flexing of EF-Tu, especially as occurs between its open and closed conformations, is a major factor in its chaperone-like refolding activity.

Ribosomes and ribosomal RNA as chaperones for folding of proteins

BACKGROUND

Provocative recent reports indicate that the large subunits of either prokaryotic or eukaryotic ribosomes have the capacity to promote refolding of denatured enzymes.

RESULTS

Salt-washed Escherichia coli ribosomes are shown to promote refolding of denatured rhodanese. The ability of the ribosomes to carry out renaturation is a property of the 50S ribosomal subunit, specifically the 23S rRNA. Refolding and release of enzymatically active rhodanese leaves the ribosomes in an inactive state or conformation for subsequent rounds refolding. Inactive ribosomes can be activated by elongation factor G (EF-G) plus GTP or by cleavage of their 23S rRNA by alpha-sarcin. Activation by either mechanism is strongly inhibited by the EF-G.GDP.fusidic acid complex.

CONCLUSIONS

Large subunits of E. coli ribosomes, specifically 23S rRNA, have the capacity to mediate refolding of denatured rhodanese. Refolding activity is related to the state or conformation of ribosomes that is promoted by EF-G. Activation by either mechanism is strongly inhibited by the EF-G.GDP.fusidic acid complex.