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  • American Association for Cancer Research (AACR)  (4)
  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 73, No. 8_Supplement ( 2013-04-15), p. 2473-2473
    Abstract: Replication Protein A (RPA) is a heterotrimeric protein that binds to and protects ssDNA and plays an integral role in initiating the cellular response to DNA damage. This response is mediated via protein-protein interactions between a basic cleft on the RPA70N subunit and a number of protein partners, including ATRIP, Rad9, Mre11, and p53. RNAi against RPA has shown an expected toxicity against cancer cell lines, possibly due to abrogation of the ssDNA binding function of RPA. Specific disruption of the protein-protein interactions between the RPA07N subunit and its binding partners has the potential to produce a more selective cytotoxic response in cancer cells. To more accurately dissect the therapeutic relevance of disrupting only the protein-protein interaction functions of RPA, we sought to discover potent small molecule probes that bind to the basic cleft of RPA70N. Inhibition of protein-protein interactions is considered a difficult task. An NMR-based fragment screen has identified more than 130 fragment molecules that bind to the RPA70N protein-protein interaction cleft with affinities that range from 500 μM to 2 mM and corresponding ligand efficiencies from 0.18 to 0.30. Selected fragments, representing several distinct chemotypes, have been optimized for binding to the protein. Using X-ray crystallography, the binding modes of these fragments have been defined. Fragments were found to bind primarily to two main sites within the basic cleft of RPA70N. Additional structure-guided optimizations have been carried out and ternary co-crystal structures have been generated to guide fragment linking strategies. Together, these activities have led to the creation of multiple lead series of inhibitors of the RPA:ATRIP interaction, with binding affinities improved by several fold over the initial fragments. The SAR and biological activities of the fragments and lead compounds will be discussed. Citation Format: Alex G. Waterson, James D. Patrone, J. Phillip Kennedy, Nicholas F. Pelz, Andreas O. Frank, Bhavatarini Vandgamudi, Michael D. Feldkamp, Elaine M. Souza-Fagundes, Olivia W. Rossanese, Walter J. Chazin, Stephen W. Fesik. Fragment-based discovery of inhibitors of replication protein A protein-protein interactions. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2473. doi:10.1158/1538-7445.AM2013-2473
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2013
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 74, No. 19_Supplement ( 2014-10-01), p. 3232-3232
    Abstract: Replication Protein A (RPA) is a major regulator of checkpoint activation and enhanced DNA repair in cancer cells. In response to genotoxic stress, the RPA complex binds to and protects ssDNA while serving as a scaffold to recruit critical checkpoint and DNA-damage response proteins through the N-terminal region of the 70 kDa subunit of RPA (RPA70N). Specific disruption of the protein-protein interactions mediated by the RPA70N domain has the potential to produce selective killing of cancer cells without the risk of cytotoxicity due to interference in the ssDNA-binding function. Stapled helix peptides can serve as useful tools for inhibiting protein-protein interactions. However, their utility can be limited due to difficulties often encountered during attempts to improve the binding affinity to the target. Here, we report the discovery and optimization of a potent stapled helix peptide probe, derived from the endogenous RPA binding partner ATRIP (ATR-interacting protein), that binds to and inhibits the RPA70N protein-protein interaction surface. Alanine scanning, charge abrogation, and rational sequence optimization resulted in a peptide with a 100-fold potency gain over the native sequence and improved physical characteristics. In addition to the application of these traditional strategies, we describe a novel approach for efficiently designing peptides containing unnatural amino acids. This method involves the incorporation of an unnatural amino acid inspired by the structure activity relationships of small molecules that bind to the same site on the protein. Use of this approach produced stapled peptides with dramatic increases in binding affinity to RPA70N relative to aooIn al peptide containing only natural amino acids. The optimized peptides are cell penetrant, able to enter the nucleus, and co-localize with RPA in the nucleus at sites of DNA damage. Such a peptide may serve as a probe molecule to explore both the effects of RPA inhibition on the DNA damage response and the therapeutic potential of RPA inhibition as a cancer target. Citation Format: Alex G. Waterson, Andreas O. Frank, Bhavatarini Vandgamudi, Michael D. Feldkamp, Elaine M. Souza-Fagundes, Jessica W. Luzwick, David Cortez, Edward T. Olejniczak, Olivia W. Rossanese, Walter J. Chazin, Stephen W. Fesik. Optimization of a potent stapled helix peptide that binds to Replication Protein A. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3232. doi:10.1158/1538-7445.AM2014-3232
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2014
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 15_Supplement ( 2015-08-01), p. 3695-3695
    Abstract: Replication Protein A (RPA) is a major regulator of checkpoint activation and enhanced DNA repair in cancer cells. In response to genotoxic stress, the RPA complex binds to and protects ssDNA while serving as a scaffold to recruit critical checkpoint and DNA-damage response proteins through the N-terminal region of the 70 kDa subunit of RPA (RPA70N). RNAi against RPA has shown an expected toxicity against cancer cell lines. However, specific disruption of the RPA protein-protein interactions mediated by the RPA70N domain has the potential to produce selective killing of cancer cells without of cytotoxicity due to interference with its ssDNA-binding function. In order to accurately examine the therapeutic relevance of the inhibition of RPA function, we have sought to discover potent probe molecules that disrupt the interactions between RPA70N and its binding partners. Here we describe the discovery of molecules to probe RPA function using complementary fragment-based and traditional high-throughput screening techniques. SAR studies and structure-based design concepts used to optimize the lead series of interest will be discussed along with the biochemical and cellular results obtained with the compounds. Citation Format: Alex G. Waterson, Phillip Kennedy, James D. Patrone, Nicholas F. Pelz, Andreas O. Frank, Bhavatarini Vangamudi, DeMarco V. Camper, Elaine M. Souza-Fagundes, Michael D. Feldkamp, Edward T. Olejniczak, Olivia W. Rossanese, Walter J. Chazin, Stephen W. Fesik. Discovery of probes to evaluate the disruption of the protein-protein interactions mediated by RPA70N. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3695. doi:10.1158/1 538-7445.AM2015-3695
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
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    Online Resource
    American Association for Cancer Research (AACR) ; 2022
    In:  Cancer Research Vol. 82, No. 15 ( 2022-08-03), p. 2704-2715
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 15 ( 2022-08-03), p. 2704-2715
    Abstract: For precision medicine to reach its full potential for treatment of cancer and other diseases, protein variant effect prediction tools are needed to characterize variants of unknown significance (VUS) in a patient's genome with respect to their likelihood to influence treatment response and outcomes. However, the performance of most variant prediction tools is limited by the difficulty of acquiring sufficient training and validation data. To overcome these limitations, we applied an iterative active learning approach starting from available biochemical, evolutionary, and functional annotations. With active learning, VUS that are most challenging to classify by an initial machine learning model are functionally evaluated and then reincorporated with the phenotype information in subsequent iterations of algorithm training. The potential of active learning to improve variant interpretation was first demonstrated by applying it to synthetic and deep mutational scanning datasets for four cancer-relevant proteins. The utility of the approach to guide interpretation and functional validation of tumor VUS was then probed on the nucleotide excision repair (NER) protein xeroderma pigmentosum complementation group A (XPA), a potential biomarker for cancer therapy sensitivity. A quantitative high-throughput cell-based NER activity assay was used to validate XPA VUS selected by the active learning strategy. In all cases, active learning yielded a significant improvement in variant effect predictions over traditional learning. These analyses suggest that active learning is well suited to significantly improve interpretation of VUS and cancer patient genomes. Significance: A novel machine learning approach predicts the impact of tumor mutations on cellular phenotypes, overcomes limited training data, minimizes costly functional validation, and advances efforts to implement cancer precision medicine.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Library Location Call Number Volume/Issue/Year Availability
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