Combined QSAR-based virtual screening and fluorescence binding assay to identify natural product mediators of Interferon Regulatory Factor 7 (IRF-7) in pulmonary infection

References

• R. Lu and P.M. Pitha, Monocyte differentiation to macrophage requires interferon regulatory factor 7, J. Biol. Chem. 276 (2001), pp. 45491–45496.
   PubMed Web of Science ®Google Scholar
• S. Ning, J.S. Pagano, and G.N. Barber, IRF7: Activation, regulation, modification and function, Genes Immun. 12 (2011), pp. 399–414.
   PubMed Web of Science ®Google Scholar
• S. Hippenstiel, B. Opitz, B. Schmeck, and N. Suttorp, Lung epithelium as a sentinel and effector system in pneumonia — molecular mechanisms of pathogen recognition and signal transduction, Respir. Res. 7 (2006), p. 97.
   PubMed Web of Science ®Google Scholar
• M.J. Holtzman, J.W. Tyner, E.Y. Kim, M.S. Lo, A.C. Patel, L.P. Shornick, E. Agapov, and Y. Zhang, Acute and chronic airway responses to viral infection: Implications for asthma and chronic obstructive pulmonary disease, Proc. Am. Thorac. Soc. 2 (2005), pp. 132–140.
   PubMedGoogle Scholar
• I. Musmuca, S. Simeoni, A. Caroli, and R. Ragno, Small-molecule interferon inducers. Toward the comprehension of the molecular determinants through ligand-based approaches, J. Chem. Inf. Model. 49 (2009), pp. 1777–1786.
   PubMed Web of Science ®Google Scholar
• F. McNab, K. Mayer-Barber, A. Sher, A. Wack, and A. O'Garra, Type I interferons in infectious disease, Nat. Rev. Immunol. 15 (2015), pp. 87–103.
   PubMed Web of Science ®Google Scholar
• D.J. Newman and G.M. Cragg, Natural products as sources of new drugs over the 30 years from 1981 to 2010, J. Nat. Prod. 75 (2012), pp. 311–335.
   PubMed Web of Science ®Google Scholar
• P.S. Haddad, G.A. Azar, S. Groom, and M. Boivin, Natural health products, modulation of immune function and prevention of chronic diseases, Evid. Based Complement. Alternat. Med. 2 (2005), pp. 513–520.
   PubMed Web of Science ®Google Scholar
• J.E. Talmadge, Natural product derived immune-regulatory agents, Int. Immunopharmacol. 37 (2016), pp. 5–15.
   PubMed Web of Science ®Google Scholar
• O. Obrezanova and M.D. Segall, Gaussian processes for classification: QSAR modeling of ADMET and target activity, J. Chem. Inf. Model. 50 (2010), pp. 1053–1061.
   PubMed Web of Science ®Google Scholar
• L. You, V. Brusic, M. Gallagher, and M. Bodén, Using Gaussian process with test rejection to detect T-cell epitopes in pathogen genomes, IEEE/ACM Trans. Comput. Biol. Bioinform. 7 (2010), pp. 741–751.
   PubMed Web of Science ®Google Scholar
• C.E. Rasmussen and C.K.I. Williams, Gaussian Processes for Machine Learning, MIT Press, MA, 2006.
   Google Scholar
• A. Marrel, B. Iooss, F. Van Dorpe, and E. Volkova, An efficient methodology for modeling complex computer codes with Gaussian processes, Comput. Stat. Data Anal. 52 (2008), pp. 4731–4744.
   Web of Science ®Google Scholar
• P. Zhou, X. Chen, Y. Wu, and Z. Shang, Gaussian process: An alternative approach for QSAM modeling of peptides, Amino Acids 38 (2010), pp. 199–212.
   PubMed Web of Science ®Google Scholar
• P. Wolfe, Convergence conditions for ascent methods, SIAM Rev. 11 (1969), pp. 226–235.
   Web of Science ®Google Scholar
• R. Wang, X. Fang, Y. Lu, and S. Wang, The PDBbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures, J. Med. Chem. 47 (2004), pp. 2977–2980.
   PubMed Web of Science ®Google Scholar
• H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, and P.E. Bourne, The protein data bank, Nucleic Acids Res. 28 (2000), pp. 235–242.
   PubMed Web of Science ®Google Scholar
• Y. Cao and L. Li, Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model, Bioinformatics 30 (2014), pp. 1674–1680.
   PubMed Web of Science ®Google Scholar
• X. Cheng, Y. Mei, X. Ji, Q. Xue, and D. Chen, Molecular mechanism of the susceptibility difference between HLA-B*27:02/04/05 and HLA-B*27:06/09 to ankylosing spondylitis: Substitution analysis, MD simulation, QSAR modelling, and in vitro assay, SAR QSAR Environ. Res. 27 (2016), pp. 409–425.
   PubMed Web of Science ®Google Scholar
• S. Salentin, S. Schreiber, V.J. Haupt, M.F. Adasme, and M. Schroeder, PLIP: Fully automated protein-ligand interaction profiler, Nucleic Acids Res. 43 (2015), pp. W443–W447.
   PubMed Web of Science ®Google Scholar
• M.F. Sanner, A.J. Olson, and J.C. Spehner, Reduced surface: An efficient way to compute molecular surfaces, Biopolymers 38 (1996), pp. 305–320.
   PubMed Web of Science ®Google Scholar
• M.S. Till and G.M. Ullmann, McVol — a program for calculating protein volumes and identifying cavities by a Monte Carlo algorithm, J. Mol. Model. 16 (2010), pp. 419–429.
   PubMed Web of Science ®Google Scholar
• W.J. Allen, T.E. Balius, S. Mukherjee, S.R. Brozell, D.T. Moustakas, P.T. Lang, D.A. Case, I.D. Kuntz, and R.C. Rizzo, DOCK6: Impact of new features and current docking performance, J. Comput. Chem. 36 (2015), pp. 1132–1156.
   PubMed Web of Science ®Google Scholar
• J.J. Irwin and B.K. Shoichet, ZINC — a free database of commercially available compounds for virtual screening, J. Chem. Inf. Model. 45 (2005), pp. 177–182.
   PubMed Web of Science ®Google Scholar
• P. De Ioannes, C.R. Escalante, and A.K. Aggarwal, Structures of apo IRF-3 and IRF-7 DNA binding domains: Effect of loop L1 on DNA binding, Nucleic Acids Res. 39 (2011), pp. 7300–7307.
   PubMed Web of Science ®Google Scholar
• D. Kozakov, L.E. Grove, D.R. Hall, T. Bohnuud, S.E. Mottarella, L. Luo, B. Xia, D. Beglov, and S. Vajda, The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins, Nat. Protoc. 10 (2015), pp. 733–755.
   PubMed Web of Science ®Google Scholar
• G. Mocz and J.A. Ross, Fluorescence techniques in analysis of protein-ligand interactions, Methods Mol. Biol. 2013 (1008), pp. 169–210.
   Google Scholar
• A. Golbraikh and A. Tropsha, Beware of q2!, J. Mol. Graph. Model. 20 (2002), pp. 269–276.
   PubMed Web of Science ®Google Scholar
• A. Tropsha, P. Gramatica, and V.K. Gombar, The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci. 22 (2003), pp. 69–77.
   Google Scholar
• D. Xu and Y. Zhang, Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization, Biophys. J. 101 (2011), pp. 2525–2534.
   PubMed Web of Science ®Google Scholar
• H. Gong, Z. Yuan, and L. Zhan, High-throughput screening against ~6.1 million structurally diverse, lead-like compounds to discover novel ROCK inhibitors for cerebral injury recovery, Mol. Divers. 20 (2016), pp. 537–549.
   PubMed Web of Science ®Google Scholar
• B. Chen, P. Greenside, H. Paik, M. Sirota, D. Hadley, and A.J. Butte, Relating chemical structure to cellular response: An integrative analysis of gene expression, bioactivity, and structural data across 11,000 compounds, CPT Pharmacom. Syst. Pharmacol. 4 (2015), pp. 576–584.
   PubMedGoogle Scholar