The Crystallography and Computational Biosciences (CCB) Shared Resource serves as a portal for access to:
- expertise, consultation, and state-of-the-art instrumentation for X-ray crystallography and structure modeling and refinement; and
- computational resources and expertise to handle the development of structural and force field models, deployment of quantum mechanical calculations, virtual screening of compound libraries, and molecular dynamics studies. Among other computational resources, CCBSR has access to supercomputing capabilities of the Wake Forest DEAC cluster. A key feature of CCBSR is expertise in virtual screening for small molecules, which complements recent high-throughput screening partnerships between SRs at WFBCCC and other academic centers in North Carolina.
The CCB meets the growing needs for structure determination and computational analysis of protein and DNA/RNA structure, function and dynamics for a diverse array of projects ranging from basic science questions to drug design.
The Crystallography and Computational Biosciences Core supports areas of basic science research with an emphasis on biological processes related to cancer such as:
- Cell signaling
- Transcriptional regulation
- DNA damage and repair
- Lipid metabolism
Basic science research to understand the normal and pathophysiological function of proteins and their interactions with a myriad of biological partners and functional modifiers (e.g., other proteins, DNA, RNA, cofactors, drugs, post-translational modifications, mutations) is the foundation on which to build all progress towards these objectives. We provide access to cutting-edge modeling and simulation methods. The information from these complementary approaches can be used to develop novel therapies.
The CCB also provides support for ongoing projects and the development of new projects through the collection of preliminary data for funding applications.
Below is a workflow overview in the CCBSR: progression from project prioritization and initial development to advanced development and translation.
TREX1 as a Novel Immunotherapeutic Target. Hemphill WO, Simpson SR, Liu M, Salsbury FR Jr, Hollis T, Grayson JM, Perrino FW. Front Immunol. 2021 Apr 1;12:660184. doi: 10.3389/fimmu.2021.660184. eCollection 2021. PMID: 33868310 Free PMC article.
Simulations suggest double sodium binding induces unexpected conformational changes in thrombin.
Wu D, Salsbury FR Jr. J Mol Model. 2022 Apr 13;28(5):120. doi: 10.1007/s00894-022-05076-0. PMID: 35419655 Free PMC article.
Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning.
Xiao J, Melvin RL, Salsbury FR Jr. J Biomol Struct Dyn. 2019 Mar;37(4):982-999. doi: 10.1080/07391102.2018.1445032. Epub 2018 Mar 2. PMID: 29471734 Free PMC article.
Na+-binding modes involved in thrombin's allosteric response as revealed by molecular dynamics simulations, correlation networks and Markov modeling.
Xiao J , Salsbury FR . Phys Chem Chem Phys. 2019 Feb 20;21(8):4320-4330. doi: 10.1039/c8cp07293k. PMID: 30724273 Free PMC article.
Melvin RL, Xiao J, Godwin RC, Berenhaut KS, Salsbury FR Jr. Visualizing correlated motion with HDBSCAN clustering. Protein Sci. 2018 Jan;27(1):62-75. doi: 10.1002/pro.3268. Epub 2017 Sep 6.
Xiao J, Melvin RL, Salsbury FR Jr. Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning. J Biomol Struct Dyn. 2018 Mar 2:1-18. doi: 10.1080/07391102.2018.1445032. [Epub ahead of print]
Rogers LC, Davis RR, Said N, Hollis T, Daniel LW. Blocking LPA-dependent signaling increases ovarian cancer cell death in response to chemotherapy. Redox Biol. 2018 May;15:380-386. doi: 10.1016/j.redox.2018.01.002. Epub 2018 Jan 4.
Mauney CH, Hollis T. SAMHD1: Recurring roles in cell cycle, viral restriction, cancer, and innate immunity. Autoimmunity. 2018 May;51(3):96-110. doi: 10.1080/08916934.2018.1454912. Epub 2018 Mar 27.
Guragain M, Jennings-Gee J, Cattelan N, Finger M, Conover MS, Hollis T, Deora R. The transcriptional regulator BpsR controls the growth of Bordetella bronchiseptica by repressing genes involved in nicotinic acid degradation. J Bacteriol. 2018 May 24;200(12). pii: e00712-17. doi: 10.1128/JB.00712-17. Print 2018 Jun 15.
Bolduc JA, Nelson KJ, Haynes AC, Lee J, Reisz JA, Graff AH, Clodfelter JE, Parsonage D, Poole LB, Furdui CM, Lowther WT. Novel hyperoxidation resistance motifs in 2-Cys peroxiredoxins. J Biol Chem. 2018 Jul 27;293(30):11901-11912. doi: 10.1074/jbc.RA117.001690. Epub 2018 Jun 8.
Akter S, Fu L, Jung Y, Conte ML, Lawson JR, Lowther WT, Sun R, Liu K, Yang J, Carroll KS. Chemical proteomics reveals new targets of cysteine sulfinic acid reductase. Nat Chem Biol. 2018 Nov;14(11):995-1004. doi: 10.1038/s41589-018-0116-2. Epub 2018 Sep 3.
Godwin RC, Macnamara LM, Alexander RW, Salsbury FR Jr. Structure and Dynamics of tRNAMet Containing Core Substitutions. ACS Omega. 2018 Sep 30;3(9):10668-10678. doi: 10.1021/acsomega.8b00280. Epub 2018 Sep 5.
Melvin RL, Xiao J, Berenhaut KS, Godwin RC, Salsbury FR. Using correlated motions to determine sufficient sampling times for molecular dynamics. Phys Rev E. 2018 Aug;98(2-1):023307. doi: 10.1103/PhysRevE.98.023307.
Mauney CH, Perrino FW, Hollis T. Identification of Inhibitors of the dNTP Triphosphohydrolase SAMHD1 Using a Novel and Direct High-Throughput Assay. Biochemistry. 2018 Nov 27;57(47):6624-6636. doi: 10.1021/acs.biochem.8b01038. Epub 2018 Nov 13.
Loberg MA, Hurtig JE, Graff AH, Allan KM, Buchan JA, Spencer MK, Kelly JE, Clodfelter JE, Morano KA, Lowther WT, West JD. Aromatic Residues at the Dimer-Dimer Interface in the Peroxiredoxin Tsa1 Facilitate Decamer Formation and Biological Function. Chem Res Toxicol. 2019 Feb 11. doi: 10.1021/acs.chemrestox.8b00346. [Epub ahead of print]
Forshaw TE, Holmila R, Nelson KJ, Lewis JE, Kemp ML, Tsang AW, Poole LB, Lowther WT, Furdui CM. Peroxiredoxins in Cancer and Response to Radiation Therapies. Antioxidants (Basel). 2019 Jan 1;8(1). pii: E11. doi: 10.3390/antiox8010011. Review.
Xiao J, Salsbury FR. Na+-binding modes involved in thrombin's allosteric response as revealed by molecular dynamics simulations, correlation networks and Markov modeling. Phys Chem Chem Phys. 2019 Feb 20;21(8):4320-4330. doi: 10.1039/c8cp07293k.
Xiao J, Melvin RL, Salsbury FR Jr. Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning. J Biomol Struct Dyn. 2019 Mar;37(4):982-999. doi: 10.1080/07391102.2018.1445032. Epub 2018 Mar 2.