SCREENING INFORMATICS CORE (SI) SI Aims. SI will: 1) enhance collaboration within the Center, with assay providers, and MLPCN Specialty Chemistry Centers;2) provide support for QC and QA for assay and screening procedures;3) track compounds, track assays, track screening results;4) support data acquisition and analysis;5) hit analysis for Chemstry Centers;6) provide quick recovery of data after software or hardware failures;7) develop and implement visualization and analysis tools for multiplex data sets. SI Progress Report. A collaborative informatics infrastructure has been implemented to support data, information and knowledge management within the Center, focusing on integration and real-time collaboration among the Center cores and with external partners. The infrastructure was built for flexibility and adaptability using mostly open source software packages and web interfaces to the main server (Web server - http://nmmlsc.health.unm.edu, Wiki server - http://paprika.health.unm.edu/wiki, and the RoadRunner chemical database screening server - http://screening.health.unm.edu/rrnmmlsc/. and Data File server - http://anaheim.health.unm.edu). Two additional Linux clusters, with 32 and 96 processors, respectively, and a 32 GB high memory machine are used for high performance data mining jobs on large and very large data sets of hundreds of thousands or millions of compounds. Nightly incremental backup on an 8 terabyte DELL PowerVault MD1000 backup server and monthly full tape backup on a DELL ML6000 Library Series LTO-3 unit have been implemented for the main servers, with plans to add the backup service to the screening workstations. Data processing, QC and QA workflows for assay and HTS procedures have been implemented. Results were deposited into PubChem. During the MLSCN pilot phase, members of the SI also implemented advanced data mining and virtual screening techniques used for hit identification and optimization which will not be required for MLPCN since cheminformatics is to be carried out in Comprehensive Centers and Specialty Chemistry Centers.
| Palsuledesai, Charuta C; Surviladze, Zurab; Waller, Anna et al. (2018) Activation of Rho Family GTPases by Small Molecules. ACS Chem Biol 13:1514-1524 |
| Bredemeyer, Andrea L; Edwards, Bruce S; Haynes, Mark K et al. (2018) High-Throughput Screening Approach for Identifying Compounds That Inhibit Nonhomologous End Joining. SLAS Discov 23:624-633 |
| Hong, Lin; Chavez, Stephanie; Smagley, Yelena et al. (2016) Relationship of light scatter change and Cdc42-regulated actin status. Cytometry B Clin Cytom 90:499-505 |
| Sykes, David B; Kfoury, Youmna S; Mercier, François E et al. (2016) Inhibition of Dihydroorotate Dehydrogenase Overcomes Differentiation Blockade in Acute Myeloid Leukemia. Cell 167:171-186.e15 |
| Holmes, Ann R; Cardno, Tony S; Strouse, J Jacob et al. (2016) Targeting efflux pumps to overcome antifungal drug resistance. Future Med Chem 8:1485-501 |
| Wu, Yang; Stauffer, Shaun R; Stanfield, Robyn L et al. (2016) Discovery of Small-Molecule Nonfluorescent Inhibitors of Fluorogen-Fluorogen Activating Protein Binding Pair. J Biomol Screen 21:74-87 |
| Yang, Jeremy J; Ursu, Oleg; Lipinski, Christopher A et al. (2016) Badapple: promiscuity patterns from noisy evidence. J Cheminform 8:29 |
| Zahoránszky-K?halmi, Gergely; Bologa, Cristian G; Oprea, Tudor I (2016) Impact of similarity threshold on the topology of molecular similarity networks and clustering outcomes. J Cheminform 8:16 |
| Schreiber, Stuart L; Kotz, Joanne D; Li, Min et al. (2015) Advancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes. Cell 161:1252-65 |
| Agola, Jacob O; Sivalingam, Daniel; Cimino, Daniel F et al. (2015) Quantitative bead-based flow cytometry for assaying Rab7 GTPase interaction with the Rab-interacting lysosomal protein (RILP) effector protein. Methods Mol Biol 1298:331-54 |
Showing the most recent 10 out of 113 publications
