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Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

Research Article

Inhibition of α-amylase Activity by Zn2+: Insights from Spectroscopy and Molecular Dynamics Simulations

Author(s): Si-Ming Liao, Nai-Kun Shen, Ge Liang, Bo Lu, Zhi-Long Lu, Li-Xin Peng, Feng Zhou, Li-Qin Du, Yu-Tuo Wei, Guo-Ping Zhou* and Ri-Bo Huang*

Volume 15, Issue 5, 2019

Page: [510 - 520] Pages: 11

DOI: 10.2174/1573406415666181217114101

Price: $65

Abstract

Background: Inhibition of α-amylase activity is an important strategy in the treatment of diabetes mellitus. An important treatment for diabetes mellitus is to reduce the digestion of carbohydrates and blood glucose concentrations. Inhibiting the activity of carbohydrate-degrading enzymes such as α-amylase and glucosidase significantly decreases the blood glucose level. Most inhibitors of α-amylase have serious adverse effects, and the α-amylase inactivation mechanisms for the design of safer inhibitors are yet to be revealed.

Objective: In this study, we focused on the inhibitory effect of Zn2+ on the structure and dynamic characteristics of α-amylase from Anoxybacillus sp. GXS-BL (AGXA), which shares the same catalytic residues and similar structures as human pancreatic and salivary α-amylase (HPA and HSA, respectively).

Methods: Circular dichroism (CD) spectra of the protein (AGXA) in the absence and presence of Zn2+ were recorded on a Chirascan instrument. The content of different secondary structures of AGXA in the absence and presence of Zn2+ was analyzed using the online SELCON3 program. An AGXA amino acid sequence similarity search was performed on the BLAST online server to find the most similar protein sequence to use as a template for homology modeling. The pocket volume measurer (POVME) program 3.0 was applied to calculate the active site pocket shape and volume, and molecular dynamics simulations were performed with the Amber14 software package.

Results: According to circular dichroism experiments, upon Zn2+ binding, the protein secondary structure changed obviously, with the α-helix content decreasing and β-sheet, β-turn and randomcoil content increasing. The structural model of AGXA showed that His217 was near the active site pocket and that Phe178 was at the outer rim of the pocket. Based on the molecular dynamics trajectories, in the free AGXA model, the dihedral angle of C-CA-CB-CG displayed both acute and planar orientations, which corresponded to the open and closed states of the active site pocket, respectively. In the AGXA-Zn model, the dihedral angle of C-CA-CB-CG only showed the planar orientation. As Zn2+ was introduced, the metal center formed a coordination interaction with H217, a cation-π interaction with W244, a coordination interaction with E242 and a cation-π interaction with F178, which prevented F178 from easily rotating to the open state and inhibited the activity of the enzyme.

Conclusion: This research may have uncovered a subtle mechanism for inhibiting the activity of α-amylase with transition metal ions, and this finding will help to design more potent and specific inhibitors of α-amylases.

Keywords: α-amylase, active site pocket, circular dichroism spectrum, molecular dynamics simulations, inhibition, zinc ions.

Graphical Abstract

[1]
Gin, H.; Rigalleau, V. Post-prandial hyperglycemia. Post-prandial hyperglycemia and diabetes. Diabetes Metab., 2000, 26(4), 265-272.
[2]
Chou, K.C. Molecular therapeutic target for type-2 diabetes. J. Proteome Res., 2004, 3(6), 1284-1288.
[3]
Puranik, N.V.; Puntambekar, H.M.; Srivastava, P. Antidiabetic potential and enzyme kinetics of benzothiazole derivatives and their non-bonded interactions with alpha-glucosidase and alpha-amylase. Med. Chem. Res., 2016, 25(4), 805-816.
[4]
Liu, X.Y.; Wang, R.L.; Xu, W.R.; Tang, L.D.; Wang, S.Q.; Chou, K.C. Docking and molecular dynamics simulations of peroxisome proliferator activated receptors interacting with pan agonist sodelglitazar. Protein Pept. Lett., 2011, 18(10), 1021-1027.
[5]
Ma, Y.; Wang, S.Q.; Xu, W.R.; Wang, R.L.; Chou, K.C. Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach. PLoS One, 2012, 7(6)e38546
[6]
Liu, L.; Ma, Y.; Wang, R.L.; Xu, W.R.; Wang, S.Q.; Chou, K.C. Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics. Drug Des. Devel. Ther., 2013, 7, 279-287.
[7]
Hamdan, I.I.; Afifi, F.; Taha, M.O. In vitro alpha amylase inhibitory effect of some clinically-used drugs. Pharmazie, 2004, 59(10), 799-801.
[8]
Bale, A.T.; Khan, K.M.; Salar, U.; Chigurupati, S.; Fasina, T.; Ali, F. Kanwal; Wadood, A.; Taha, M.; Nanda, S.S.; Ghufran, M.; Perveen, S. Chalcones and bis-chalcones: As potential alpha-amylase inhibitors; synthesis, in vitro screening, and molecular modelling studies. Bioorg. Chem., 2018, 79, 179-189.
[9]
Wickramaratne, M.N.; Punchihewa, J.C.; Wickramaratne, D.B.M. In-vitro alpha amylase inhibitory activity of the leaf extracts of Adenanthera pavonina. BMC. Complem. Altern. M., 2016, 16(1), 466.
[10]
Xiao, X.; Min, J.L.; Wang, P.; Chou, K.C. Predict drug-protein interaction in cellular networking. Curr. Top. Med. Chem., 2013, 13(14), 1707-1712.
[11]
Xiao, X.; Wang, P.; Chou, K.C. Recent progresses in identifying nuclear receptors and their families. Curr. Top. Med. Chem., 2013, 13(10), 1192-1200.
[12]
Fan, Y.N.; Xiao, X.; Min, J.L.; Chou, K.C. iNR-Drug: Predicting the interaction of drugs with nuclear receptors in cellular networking. Int. J. Mol. Sci., 2014, 15(3), 4915-4937.
[13]
Xiao, X.; Min, J.L.; Wang, P.; Chou, K.C. iGPCR-Drug: A web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One, 2013, 8(8)e72234
[14]
Jia, J.H.; Liu, Z.; Xiao, X.; Liu, B.X.; Chou, K.C. iCar-PseCp: Identify carbonylation sites in proteins by Monto Carlo sampling and incorporating sequence coupled effects into general PseAAC. Oncotarget, 2016, 7(23), 34558-34570.
[15]
Janecek, S.; Svensson, B.; MacGregor, E.A. Alpha-Amylase: An enzyme specificity found in various families of glycoside hydrolases. Cell. Mol. Life Sci., 2014, 71(7), 1149-1170.
[16]
Nielsen, J.E.; Borchert, T.V. Protein engineering of bacterial alpha-amylases. BBA. Protein Struct. M., 2000, 1543(2), 253-274.
[17]
Janecek, S. Alpha-amylase family: Molecular biology and evolution. Prog. Biophys. Mol. Biol., 1997, 67(1), 67-97.
[18]
Miura, T.; Suzuki, K.; Kohata, N.; Takeuchi, H. Metal binding modes of Alzheimer’s amyloid beta-peptide in insoluble aggregates and soluble complexes. Biochemistry, 2000, 39(23), 7024-7031.
[19]
Navarra, G.; Tinti, A.; Di Foggia, M.; Leone, M.; Militello, V.; Torreggiani, A. Metal ions modulate thermal aggregation of beta-lactoglobulin: A joint chemical and physical characterization. J. Inorg. Biochem., 2014, 137, 64-73.
[20]
Boopathi, S.; Kolandaivel, P. Fe2+ binding on amyloid -peptide promotes aggregation. Proteins, 2016, 84(9), 1257-1274.
[21]
Gerber, H.; Wu, F.; Dimitrov, M.; Osuna, G.M.G.; Fraering, P.C. Zinc and copper differentially modulate Amyloid precursor protein processing by -Secretase and Amyloid- peptide production. J. Biol. Chem., 2017, 292(9), 3751-3767.
[22]
Dey, G.; Palit, S.; Banerjee, R.; Maiti, B.R. Purification and characterization of maltooligosaccharide-forming amylase from Bacillus circulans GRS 313. J. Ind. Microbiol. Biotechnol., 2002, 28(4), 193-200.
[23]
Zohra, R.R.; Ul-Qader, S.A.; Pervez, S.; Aman, A. Influence of different metals on the activation and inhibition of alpha-amylase from thermophilic Bacillus firmus KIBGE-IB28. Pak. J. Pharm. Sci., 2016, 29(4), 1275-1278.
[24]
Donadio, G.; Di Martino, R.; Oliva, R.; Petraccone, L.; Del Vecchio, P.; Di Luccia, B.; Ricca, E.; Isticato, R.; Di Donato, A.; Notomista, E. A new peptide-based fluorescent probe selective for zinc(II) and copper(II). J. Mater. Chem. B, 2016, 4(43), 6979-6988.
[25]
Shah, R.; Chou, T.F.; Maize, K.M.; Strom, A.; Finzel, B.C.; Wagner, C.R. Inhibition by divalent metal ions of human histidine triad nucleotide binding proteinl (hHint1), a regulator of opioid analgesia and neuropathic pain. Biochem. Biophys. Res. Commun., 2017, 491(3), 760-766.
[26]
Dudev, T.; Lin, Y.L.; Dudev, M.; Lim, C. First-second shell interactions in metal binding sites in proteins: A PDB survey and DFT/CDM calculations. J. Am. Chem. Soc., 2003, 125(10), 3168-3180.
[27]
Laitaoja, M.; Valjakka, J.; Janis, J. Zinc coordination spheres in protein structures. Inorg. Chem., 2013, 52(19), 10983-10991.
[28]
De La Rosa, V.; Bennett, A.L.; Ramsey, I.S. Coupling between an electrostatic network and the Zn2+ binding site modulates Hv1 activation. J. Gen. Physiol., 2018, 150(6), 863-881.
[29]
Hecel, A.; Watly, J.; Rowinska-Zyrek, M.; Swiatek-Kozlowska, J.; Kozlowski, H. Histidine tracts in human transcription factors: Insight into metal ion coordination ability. J. Biol. Inorg. Chem., 2018, 23(1), 81-90.
[30]
Liao, S.M.; Sun, L.; Wang, Q.Y.; Shen, N.K.; Zhu, J.; Huang, G.Y.; Huang, J.M.; Chen, D.; Huang, R.B. Screening of thermostable α-amylase producing strain and cloning, expression and characterization of the gene AmyGX. Guangxi. Sci., 2017, 1, 92-99.
[31]
Greenfield, N.J. Using circular dichroism collected as a function of temperature to determine the thermodynamics of protein unfolding and binding interactions. Nat. Protoc., 2006, 1(6), 2527-2535.
[32]
Ahmed, A.; Villinger, S.; Gohlke, H. Large-scale comparison of protein essential dynamics from molecular dynamics simulations and coarse-grained normal mode analyses. Proteins, 2010, 78(16), 3341-3352.
[33]
Adcock, S.A.; McCammon, J.A. Molecular dynamics: Survey of methods for simulating the activity of proteins. Chem. Rev., 2006, 106(5), 1589-1615.
[34]
Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem., 1976, 72(1-2), 248-254.
[35]
Nazmi, A.R.; Remisch, T.; Hinz, H-J. Ca-binding to Bacillus licheniformis alpha-amylase (BLA). Arch. Biochem. Biophys., 2006, 453(1), 18-25.
[36]
Whitmore, L.; Wallace, B.A. DICHROWEB, an online server for protein secondary structure analyses from circular dichroism spectroscopic data. Nucleic Acids Res., 2004, 32, 668-673.
[37]
Altschul, S.F.; Madden, T.L.; Schaffer, A.A.; Zhang, J.H.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res., 1997, 25(17), 3389-3402.
[38]
Chai, K.P.; Othman, N.F.B.; Teh, A-H.; Ho, K.L.; Chan, K-G.; Shamsir, M.S.; Goh, K.M.; Ng, C.L. Crystal structure of Anoxybacillus alpha-amylase provides insights into maltose binding of a new glycosyl hydrolase subclass. Sci. Rep-Uk., 2016, 6, 23126.
[39]
Sali, A.; Potterton, L.; Yuan, F.; van Vlijmen, H.; Karplus, M. Evaluation of comparative protein modeling by MODELLER. Proteins, 1995, 23(3), 318-326.
[40]
Shen, M-Y.; Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci., 2006, 15(11), 2507-2524.
[41]
Lovell, S.C.; Davis, I.W.; Adrendall, W.B.; de Bakker, P.I.W.; Word, J.M.; Prisant, M.G.; Richardson, J.S.; Richardson, D.C. Structure validation by C alpha geometry: Phi, psi and C beta deviation. Proteins, 2003, 50(3), 437-450.
[42]
Eisenberg, D.; Luthy, R.; Bowie, J.U. VERIFY3D: Assessment of protein models with three-dimensional profiles. In Macromolecular Crystallography, , Pt B, Carter, C.W.; Sweet, R.M., Eds.,. 1997, 277, 396-404.
[43]
Molecular Operating Environment (MOE); 2013. 08; Chemical Computing Group: Montreal, QC, Canada, . , 2013.
[44]
Gordon, J.C.; Myers, J.B.; Folta, T.; Shoja, V.; Heath, L.S.; Onufriev, A.H. ++: A server for estimating pK(a)s and adding missing hydrogens to macromolecules. Nucleic Acids Res., 2005, 33, 368-371.
[45]
AMBER, 14; University of California: San Francisco, USA, 2014.
[46]
Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys., 1983, 79(2), 926-935.
[47]
Cornell, W.D.; Cieplak, P.; Bayly, C.I.; Gould, I.R.; Merz, K.M.; Ferguson, D.M.; Spellmeyer, D.C.; Fox, T.; Caldwell, J.W.; Kollman, P.A. A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc., 1996, 118(9), 2309-2309.
[48]
Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C. Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins, 2006, 65(3), 712-725.
[49]
Svozil, D.; Sponer, J.E.; Marchan, I.; Perez, A.; Cheatham, T.E.; Forti, F.; Luque, F.J.; Orozco, M.; Sponer, J. Geometrical and electronic structure variability of the sugar-phosphate backbone in nucleic acids. J. Phys. Chem. B, 2008, 112(27), 8188-8197.
[50]
Ryckaert, J-P.; Ciccotti, G.; Berendsen, H.J. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys., 1977, 23(3), 327-341.
[51]
Pastor, R.W.; Brooks, B.R.; Szabo, A. An analysis of the accuracy of Langevin and molecular dynamics algorithms. Mol. Phys., 1988, 65(6), 1409-1419.
[52]
Durrant, J.D.; de Oliveira, C.A.F.; McCammon, J.A. POVME: An algorithm for measuring binding-pocket volumes. J. Mol. Graph. Model., 2011, 29(5), 773-776.
[53]
Wagner, J.R.; Sorensen, J.; Hensley, N.; Wong, C.; Zhu, C.; Perison, T.; Amaro, R.E. POVME 3.0: Software for mapping binding pocket flexibility. J. Chem. Theory Comput., 2017, 13(9), 4584-4592.
[54]
Beychok, S. Circular dichroism of biological macromolecules. Science, 1966, 154(3754), 1288-1299.
[55]
Kelly, S.M.; Jess, T.J.; Price, N.C. How to study proteins by circular dichroism. BBA. Proteins Proteom., 2005, 1751(2), 119-139.
[56]
Zhou, R.; Liu, H.; Hou, G.; Ju, L.; Liu, C. Multi-spectral and thermodynamic analysis of the interaction mechanism between Cu2+ and alpha-amylase and impact on sludge hydrolysis. Environ. Sci. Pollut. R., 2017, 24(10), 9428-9436.
[57]
Ponnusamy, S.; Haldar, S.; Mulani, F.; Zinjarde, S.; Thulasiram, H. RaviKumar, A. Gedunin and Azadiradione: Human pancreatic alpha-amylase inhibiting limonoids from Neem (Azadirachta indica) as anti-diabetic agents. PLoS One, 2015, 10(10)e0140113
[58]
Chou, K.C.; Jones, D.; Heinrikson, R.L. Prediction of the tertiary structure and substrate binding site of caspase-8. FEBS Lett., 1997, 419(1), 49-54.
[59]
Chou, K.C.; Tomasselli, A.G.; Heinrikson, R.L. Prediction of the Tertiary Structure of a Caspase-9/Inhibitor Complex. FEBS Lett., 2000, 470, 249-256.
[60]
Chou, K.C.; Howe, W.J. Prediction of the tertiary structure of the beta-secretase zymogen. Biochem. Biophys. Res. Commun., 2002, 292(3), 702-708.
[61]
Chou, K.C.; Howe, W.J. Prediction of the tertiary structure of the beta-secretase zymogen. Biochem. Biophys. Res. Commun., 2002, 292, 702-708.
[62]
Chou, K.C. Modelling extracellular domains of GABA-A receptors: subtypes 1, 2, 3, and 5. Biochem. Biophys. Res. Commun., 2004, 316, 636-642.
[63]
Chou, K.C. Insights from modelling three-dimensional structures of the human potassium and sodium channels. J. Proteome Res., 2004, 3, 856-861.
[64]
Chou, K.C. Insights from modelling the tertiary structure of BACE2. J. Proteome Res., 2004, 3, 1069-1072.
[65]
Chou, K.C. Insights from modelling the 3D structure of the extracellular domain of alpha7 nicotinic acetylcholine receptor. Biochem. Biophys. Res. Commun., 2004, 319, 433-438.
[66]
Chou, K.C. Modeling the tertiary structure of human cathepsin-E. Biochem. Biophys. Res. Commun., 2005, 331, 56-60.
[67]
Chou, K.C. Insights from modeling the 3D structure of DNA-CBF3b complex. J. Proteome Res., 2005, 4, 1657-1660.
[68]
Chou, K.C. Coupling interaction between thromboxane A2 receptor and alpha-13 subunit of guanine nucleotide-binding protein. J. Proteome Res., 2005, 4, 1681-1686.
[69]
Huang, R.B.; Cheng, D.; Lu, B.; Liao, S.M.; Troy, F.A.; Zhou, G.P. The intrinsic relationship between structure and function of the sialyltransferase ST8Sia family members. Curr. Top. Med. Chem., 2017, 17, 2359-2369.
[70]
Zhou, G.P. Impacts of biological science to medicinal chemistry. Curr. Top. Med. Chem., 2017, 17, 2335-2336.
[71]
Zhou, G.P.; Zhong, W.Z. Perspectives in the medicinal chemistry. Curr. Top. Med. Chem., 2016, 16, 381-382.
[72]
Zhou, G.P. Editorial, special issue: Modulations and their biological functions of protein-biomolecule interactions. Curr. Top. Med. Chem., 2016, 16, 579-580.
[73]
Zhou, G.P.; Chen, D.; Liao, S.M.; Huang, R.B. Recent progresses in studying helix-helix interactions in proteins by incorporating the Wenxiang diagram into the NMR spectroscopy. Curr. Top. Med. Chem., 2016, 16, 581-590.
[74]
Zhou, G.P. Editorial, special issue: Current progress in structural bioinformatics of protein-biomolecule interactions. Med. Chem., 2015, 11(3), 216-217.
[75]
Zhou, G.P.; Huang, R.B.; Troy, F.A. 3D Structural conformation and functional domains of polysialyltransferase ST8Sia IV required for polysialylation of neural cell adhesion molecules. Protein Pept. Lett., 2015, 22, 137-148.
[76]
Chou, K.C.; Chou, N.Y. The biological functions of low-frequency phonons. Sci. Sin., 1977, 20(4), 447-457.
[77]
Chou, K.; Chen, N.; Forsen, S. The biological functions of low-frequency phonons. 2. Cooperative effects. Chem. Scr., 1981, 18(3), 126-132.
[78]
Chou, K.C. Low-frequency vibrations of helical structures in protein molecules. Biochem. J., 1983, 209(3), 573-580.
[79]
Chou, K.C. The biological functions of low-frequency vibrations (phonons): 4. Resonance effects and allosteric transition. Biophys. Chem., 1984, 20(1-2), 61-71.
[80]
Chou, K.C. Low-frequency motions in protein molecules. Beta-sheet and beta-barrel. Biophys. J., 1985, 48(2), 289-297.
[81]
Zhou, G.P. Biological functions of soliton and extra electron motion in DNA structure. Phys. Scr., 1989, 40(5), 698.
[82]
Chou, K.C. Low-frequency collective motion in biomacromolecules and its biological functions. Biophys. Chem., 1988, 30(1), 3-48.
[83]
Martel, P. Biophysical aspects of neutron scattering from vibrational modes of proteins. Prog. Biophys. Mol. Biol., 1992, 57(3), 129-179.
[84]
Wang, J.F.; Gong, K.; Wei, D.Q.; Li, Y.X.; Chou, K.C. Molecular dynamics studies on the interactions of PTP1B with inhibitors: From the first phosphate-binding site to the second one. Protein Eng. Des. Sel., 2009, 22(6), 349-355.
[85]
Chou, K.C. Low-frequency resonance and cooperativity of hemoglobin. Trends Biochem. Sci., 1989, 14(6), 212.
[86]
Chou, K.C.; Mao, B. Collective motion in DNA and its role in drug intercalation. Biopolymers, 1988, 27(11), 1795-1815.
[87]
Chou, K.C.; Zhang, C.T.; Maggiora, G.M. Solitary wave dynamics as a mechanism for explaining for explaining the internal motion during microtubule growth. Biopolymers, 1994, 34(1), 143-153.
[88]
Gordon, G.A. Designed electromagnetic pulsed therapy: Clinical applications. J. Cell. Physiol., 2007, 212(3), 579-582.
[89]
Abhilash, J.; Haridas, M. Metal ion coordination essential for specific molecular interactions of Butea monosperma Lectin: ITC and MD simulation studies. Appl. Biochem. Biotechnol., 2015, 176(1), 277-286.
[90]
Ishikawa, K.; Matsui, I.; Honda, K.; Nakatani, H. Multifunctional of a histidine residue in human pancreatic alpha-amylase. Biochem. Biophys. Res. Commun., 1992, 183(1), 286-291.
[91]
Ishikawa, K.; Matsui, I.; Kobayashi, S.; Nakatani, H.; Honda, K. Substrate recognition at the binding -site in mammalian pancreatic alpha-amylases. Biochemistry-Us., 1993, 32(24), 6259-6265.
[92]
Sogaard, M.; Abe, J.; Martineauclaire, M.F.; Svensson, B. Alpha-amylases-structure and function. Carbohydr. Polym., 1993, 21(2-3), 137-146.
[93]
Vihinen, M.; Ollikka, P.; Niskanen, J.; Meyer, P.; Suominen, I.; Karp, M.; Holm, L.; Knowles, J.; Mantsala, P. Site-directed mutagenesis of a thermostable alpha-amylase from bacillus-stearothermophilus-putative role of 3 conserved residues. J. Biochem., 1990, 107(2), 267-272.
[94]
Uchida, K. Histidine and lysine as targets of oxidative modification. Amino Acids, 2003, 25(3-4), 249-257.
[95]
Li, F.; Fitz, D.; Fraser, D.G.; Rode, B.M. Catalytic effects of histidine enantiomers and glycine on the formation of dileucine and dimethionine in the salt-induced peptide formation reaction. Amino Acids, 2010, 38(1), 287-294.
[96]
Liao, S.M.; Du, Q.S.; Meng, J.Z.; Pang, Z.W.; Huang, R.B. The multiple roles of histidine in protein interactions. Chem. Cent. J., 2013, 7(1), 44.
[97]
Manas, N.H.A.; Abu Bakar, F.D.; Illias, R.M. Computational docking, molecular dynamics simulation and subsite structure analysis of a maltogenic amylase from Bacillus lehensis G1 provide insights into substrate and product specificity. J. Mol. Graph. Model., 2016, 67, 1-13.
[98]
Matsui, I.; Yoneda, S.; Ishikawa, K.; Miyairi, S.; Fukui, S.; Umeyama, H.; Honda, K. Roles of the aromatic residues conderved in the active-center of saccharomycopsis alpha-amylase for transglycosylation and hydrolysis activity. Biochemistry-Us., 1994, 33(2), 451-458.
[99]
Arodola, O.A.; Soliman, M.E.S. Molecular dynamics simulations of ligand-induced flap conformational changes in Cathepsin-DA comparative study. J. Cell. Biochem., 2016, 117(11), 2643-2657.
[100]
Kobayashi, M.; Saburi, W.; Nakatsuka, D.; Hondoh, H.; Kato, K.; Okuyama, M.; Mori, H.; Kimura, A.; Yao, M. Structural insights into the catalytic reaction that is involved in the reorientation of Trp238 at the substrate-binding site in GH13 dextran glucosidase. FEBS Lett., 2015, 589(4), 484-489.
[101]
Chou, K.C.; Shen, H.B. Recent advances in developing web-servers for predicting protein attributes. Nat. Sci., 2009, 1, 63-92.
[102]
Chen, W.; Feng, P.M.; Lin, H. iSS-PseDNC: Identifying splicing sites using pseudo dinucleotide composition. BioMed Res. Int., 2014, 2014623149
[103]
Feng, P.M.; Chen, W.; Lin, H. iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition. Anal. Biochem., 2013, 442, 118-125.
[104]
Chen, W.; Ding, H.; Feng, P.; Lin, H. iACP: A sequence-based tool for identifying anticancer peptides. Oncotarget, 2016, 7, 16895-16909.
[105]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mPlant: Predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Mol. Biosyst., 2017, 13, 1722-1727.
[106]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene, 2017, 628, 315-321.
[107]
Cheng, X.; Zhao, S.G.; Lin, W.Z.; Xiao, X.; Chou, K.C. pLoc-mAnimal: Predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics, 2017, 33, 3524-3531.
[108]
Xiao, X.; Cheng, X.; Su, S.; Nao, Q.; Chou, K.C. pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Nat. Sci., 2017, 9, 331-349.
[109]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics, 2018, 110, 50-58.
[110]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics, 2018, 110, 231-239.
[111]
Cheng, X.; Xiao, X.; Chou, K.C. pLoc-mHum: Predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics, 2018, 34, 1448-1456.
[112]
Cheng, X.; Lin, W.Z.; Xiao, X.; Chou, K.C. pLoc_bal-mAnimal: Predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics, 2018, 458, 92-102.
[113]
Chou, K.C. Impacts of bioinformatics to medicinal chemistry. Med. Chem., 2015, 11, 218-234.
[114]
Chou, K.C. An unprecedented revolution in medicinal chemistry driven by the progress of biological science. Curr. Top. Med. Chem., 2017, 17, 2337-2358.

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