Abstract
Introduction: Acetyl-CoA Carboxylases (ACC) have been an important target for the therapy of metabolic syndrome, such as obesity, hepatic steatosis, insulin resistance, dyslipidemia, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), type 2 diabetes (T2DM), and some other diseases.
Methods: In this study, virtual screening strategy combined with Bayesian categorization modeling, molecular docking and binding site analysis with protein ligand interaction fingerprint (PLIF) was adopted to validate some potent ACC inhibitors. First, the best Bayesian model with an excellent value of Area Under Curve (AUC) value (training set AUC: 0.972, test set AUC: 0.955) was used to screen compounds of validation library. Then the compounds screened by best Bayesian model were further screened by molecule docking again.
Results: Finally, the hit compounds evaluated with four percentages (1%, 2%, 5%, 10%) were verified to reveal enrichment rates for the compounds. The combination of the ligandbased Bayesian model and structure-based virtual screening resulted in the identification of top four compounds which exhibited excellent IC 50 values against ACC in top 1% of the validation library.
Conclusion: In summary, the whole strategy is of high efficiency, and would be helpful for the discovery of ACC inhibitors and some other target inhibitors.
Keywords: ACC inhibitors, bayesian classification model, machine learning, PLIF, virtual screening strategy, enrichment analysis.
Graphical Abstract
[1]
Henao-Mejia, J.; Elinav, E.; Jin, C.C.; Hao, L.M.; Mehal, W.Z.; Strowig, T.; Thaiss, C.A.; Kau, A.L.; Eisenbarth, S.C.; Jurczak, M.J.; Camporez, J.P.; Gerald, I.S.; Shulman, G.I.; Gordon, J.I.; Hoffman, H.M.; Flavell, R.A. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature, 2012, 482, 179-184.
[2]
Harriman, G.; Greenwood, J.; Bhat, S.; Huang, X.; Wang, R.; Paul, D.; Tong, L.; Saha, A.K.; Westin, W.F.; Kapeller, R.; Harwood, H.J. Acetyl-CoA carboxylase inhibition by ND-630 reduces hepatic steatosis, improves insulin sensitivity and modulates dyslipidemia in rats. Proc. Natl. Acad. Sci. USA, 2016, 113, 1796-1805.
[3]
Luo, D.X.; Tong, D.J.; Rajput, S.; Wang, C.; Liao, D.F.; Cao, D.; Maser, E. Targeting Acetyl- CoA carboxylases: Small molecular inhibitors and their therapeutic potential. Recent Patents Anticancer Drug Discov., 2012, 7, 168-184.
[4]
Glund, S.; Schoelch, C.; Thomas, L.; Niessen, H.G.; Stiller, D.; Roth, G.J. Inhibition of acetyl-CoA carboxylase 2 enhances skeletal muscle fatty acid oxidation and improves whole- body glucose homeostasis in db/db mice. Diabetologia, 2012, 55, 2044-2053.
[5]
Boone, A.N.; Rodrigues, B.; Brownsey, R.W. Multiple-site phosphorylation of the 280kDa isoform of acetyl-CoA carboxylase in rat cardiac myocytes: Evidence that cAMP- dependent protein kinase mediates effects of beta-adrenergic stimulation. J. Biochem., 1999, 341, 347-354.
[6]
Gurvitz, A. Physiological function of mycobacterial mtFabD, an essential malonyl-CoA: AcpM transacylase of type 2 fatty acid synthase FASII, in yeast mct1Delta cells. Comp. Funct. Genomics, 2009, 1, 1-4.
[7]
Arthur, C.J.; Williams, C.; Pottage, K.; Ploskon, E.; Findlow, S.C.; Burston, S.G.; Simpson, T.J.; Crump, M.P.; Crosby, J. Structure and malonyl CoA-ACP transacylase binding of Streptomyces coelicolor fatty acid synthase acyl carrier protein. ACS Chem. Biol., 2009, 4, 625-636.
[8]
Huang, Y.S.; Ge, J.; Zhang, H.M.; Lei, J.Q.; Zhang, X.L.; Wang, H.H. Purification and characterization of the Mycobacterium tuberculosis FabD2, a novel malonyl-CoA:AcpM transacylase of fatty acid synthase. Protein Expr. Purif., 2006, 45, 393-399.
[9]
Thupari, J.N.; Pinn, M.L.; Kuhajda, F.P. Fatty acid synthase inhibition in human breast cancer cells leads to malonyl-CoA-induced inhibition of fatty acid oxidation and cytotoxicity. Biochem. Biophys. Res. Commun., 2001, 285, 217-223.
[10]
Bourbeau, M.P.; Siegmund, A.; Allen, J.G.; Shu, H. Christopher, F.; Barthberger, M.D.; Kim, K.W.; Komorowski, R.; Graham, M.; Busby, J.; Wang, M.H.; Meyer, J.; Xu, Y.; Salyers, K.; Fielden, M.; Veniant, M.M.; Gu, W. Piperazine Oxadiazole Inhibitors of acetyl-CoA carboxylase. J. Med. Chem., 2013, 56, 10132-10141.
[11]
Munday, M.R.; Campbell, D.G.; Carling, D.; Hardie, D.G. Identification by amino acid sequencing of three major regulatory phosphorylation sites on rat acetylCoA carboxylase. Eur. J. Biochem., 1989, 175, 331-338.
[12]
Ventura, F.V.; Costa, C.G.; IJlst, L.; Dorland, L.; Duran, M.; Jakobs, C. Broad specificity of carnitine palmitoyltransferase II towards long-chain acyl-CoA beta-oxidation intermediates and its practical approach to the synthesis of various long-chain acylcarnitines. J. Inherit. Metab. Dis., 1997, 20, 423-426.
[13]
Corbett, J.W. Review of recent Acetyl-CoA carboxylase inhibitor patents: Mid-2007-2008. Expert Opin. Ther. Pat., 2009, 19, 943-956.
[14]
Gu, Y.G.; Weitzberg, M.; Clark, R.F.; Xu, X.D.; Li, Q.; Lubbers, N.L.; Yang, Y.; Beno, D.W.A.; Widomski, D.L.; Zhang, T.Y.; Hansen, T.M.; Keyes, R.F.; Waring, J.F.; Carroll, S.L.; Wang, X.J.; Wang, R.Q.; Healan-Greenberg, C.H.; Blomme, E.A.; Beutel, B.A.; Sham, H.L.; Camp, H.S.N. -3-[2-(4-Alkoxyphenoxy)thiazol-5-yl]-1-methylprop-2-ynyl carboxy derivatives as acetyl-CoA carboxylase inhibitorss improvement of cardiovascular and neurological liabilities via structural modifications. J. Med. Chem., 2007, 50, 1078-1082.
[15]
Keil, S.; Muller, M.; Zoller, G.; Haschke, G.; Schroeter, K.; Glien, M.; Ruf, S.; Focken, I.; Herling, A.W.; Schmoll, D. Identification and synthesis of novel inhibitors of Acetyl-CoA carboxylase with in vitro and in vivo efficacy on fat oxidation. J. Med. Chem., 2010, 53, 8679-8687.
[16]
Freeman-Cook, K.D.; Amor, P.; Bader, S.; Buzon, L.M.; Coffey, S.B.; Corbett, J.W.; Dirico, K.J.; Doran, S.D.; Elliott, R.L.; Esler, W.; Guzman-Perez, A.; Henegar, K.E.; Houser, J.A.; Jones, C.S.; Limberakis, C.; Loomis, K.; McPherson, K.; Murdande, K.; Nelson, K.L.; Phillion, D.; Pierce, B.S.; Song, W.; Sugarman, E.; Tapley, S.; Tu, M.H.; Zhao, Z.R. Maximizing lipophilic efficiency: The use of free-wilson analysis in the design of inhibitors of acetyl-coa carboxylase. J. Med. Chem., 2012, 55, 935-942.
[17]
Griffith, D.A.; Dow, R.L.; Huard, K.; Edmonds, D.J.; Bagley, S.W.; Polivkova, J.; Zeng, D.X.; Garcia-Irizarry, C.N.; Southers, J.A.; Esler, W.; Amor, P.; Loomis, K.; McPherson, K.; Bahnck, K.B.; Preville, C.; Banks, T.; Moore, D.E.; Mathiowetz, A.M.; Menhaji-Klotz, E.; Smith, A.C.; Doran, S.D.; Beebe, D.A.; Dunn, M.F. Spirolactam-Based Acetyl-CoA carboxylase inhibitors: Toward improved metabolic stability of a chromanone lead structure. J. Med. Chem., 2013, 56, 7110-7119.
[18]
Bourbeau, M.P.; Bartberger, M.D. Recent advances in the development of Acetyl-CoA Carboxylase (ACC) inhibitors for the treatment of metabolic disease. J. Med. Chem., 2015, 58, 525-536.
[19]
Robert, U.S.; Parker, S.J.; Eichner, L.J.; Kolar, M.J.; Wallace, M.; Brun, S.N.; Lombardo, P.S.; Nostrand, J.L.V.; Hutchins, A.H.; Vera, L.; Gerken, L.; Greenwood, J.; Bhat, S.; Harriman, G.; Westlin, W.F.; Harwood, H.J.; Saghatelian, A.; Kapeller, R.; Metallo, C.M.; Shaw, R.J. Inhibition of acetyl-CoA carboxylase suppresses fatty acid synthesis and tumor growth of non-small-cell lung cancer in preclinical models. Nat. Med., 2016, 22, 1108-1119.
[20]
Griffith, D.A.; Kung, D.W.; Esler, W.P.; Amor, P.A.; Bagley, S.W.; Beysen, C. Carvajal- Gonzalez, S.; Doran, S.D.; Limberakis, C.; Mathiowetz, A.M.; McPherson, K.; Price, D.A.; Ravussin, E.; Sonnenberg, G.E.; Southers, J.A.; Sweet, L.J.; Turner, S.M.; Vajdos, F.F. Decreasing the rate of metabolic ketone reduction in the discovery of a clinical Acetyl- CoA carboxylase inhibitor for the treatment of diabetes. J. Med. Chem., 2014, 57, 10512-10526.
[21]
Zhuang, S.L.; Wang, H.F.; Ding, K.K.; Wang, J.Y.; Pan, L.M.; Lu, Y.L.; Liu, Q.J.; Zhang, C.L. Interactions of benzotriazole UV stabilizers with human serum albumin: Atomic insights revealed by biosensors, spectroscopies and molecular dynamics simulations. Chemosphere, 2016, 144, 1050-1059.
[22]
Gao, J.; Sun, J.; Wang, T.; Sheng, S.; Huang, T.H. Combined 3D-QSAR modeling and molecular docking study on spiro-derivatives as inhibitors of acetyl-CoA carboxylase. Med. Chem. Res., 2017, 26, 361-371.
[23]
Xiong, X.; Yuan, H.L.; Zhang, Y.M.; Xu, J.X.; Ran, T.; Liu, H.C.; Lu, S.; Xu, A.Y.; Li, H.M.; Jiang, Y.L.; Lu, T.; Chen, Y.D. Protein flexibility oriented virtual screening strategy for JAK2 inhibitors. J. Mol. Struct., 2015, 1097, 136-144.
[24]
Xia, X.Y.; Maliski, E.G.; Gallant, P.; Rogers, D. Classification of kinase inhibitors using a bayesian model. J. Med. Chem., 2004, 47, 4463-4470.
[25]
Rogers, D.; Brown, R.D.; Hahn, M. Using extended connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J. Biomol. Screen., 2005, 10, 682-686.
[26]
Singh, N.; Chaudhury, S.; Liu, R.F. AbdulHameed, M.D.M.; Tawa, G.; Wallqvist, A. QSAR classification model for antibacterial compounds and its use in virtual screening. J. Chem. Inf. Model., 2012, 52, 2559-2569.
[27]
Prathipati, P.; Ma, N.L.; Keller, T.H. Global bayesian models for the prioritization of antitubercular agents. J. Chem. Inf. Model., 2008, 48, 2362-2370.
[28]
Fang, J.S.; Yang, R.Y.; Gao, L.; Zhou, D.; Yang, S.Q.; Liu, A.L.; Du, G.H. Predictions of BuChE inhibitors using support vector machine and naive bayesian classification techniques in drug discovery. J. Chem. Inf. Model., 2013, 53, 3009-3020.
[29]
Bourbeau, M.P.; Allen, J.G.; Gu, W. Recent advances in AcetylCoA carboxylase inhibitors. Annu. Rep. Med. Chem., 2012, 45, 95-108.
[30]
Vijayan, R.S.K.; Bera, I.; Prabu, M.; Saha, S.; Ghoshal, N. Combinatorial library enumeration and lead hopping using comparative interaction fingerprint analysis and classical 2D QSAR methods for seeking novel GABAA α3 modulators. J. Chem. Inf. Model., 2009, 49, 2498-2511.
[31]
Lipkus, A.H. A proof of the triangle inequality for the Tanimoto distance. J. Math. Chem., 1999, 26, 263-265.
[32]
Mpamhanga, C.P.; Chen, B.; McLay, I.M.; Willett, P. Knowledge based interaction fingerprint scoring: A simple method for improving the effectiveness of fast scoring functions. J. Chem. Inf. Model., 2006, 46, 686-698.
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