In silico study to predict potential precursors of human dipeptidyl peptidase-IV inhibitors from hazelnut

References

• Adamo, M., Labate, A. M., Ferrulli, A., Macrã, C., Terruzzi, I., & Luzi, L. (2017). Effects of hazelnuts and cocoa on vascular reactivity in healthy subjects: A randomised study. International Journal of Food Sciences & Nutrition, 69(2), 1–12.
   PubMed Web of Science ®Google Scholar
• Bleakley, S., Hayes, M., Shea, N. O., Gallagher, E., & Lafarga, T. (2017). Predicted release and analysis of novel ACE-I, renin, and DPP-IV inhibitory peptides from common oat (Avena sativa) protein hydrolysates using in silico analysis. Foods, 6(12), 108. https://doi.org/10.3390/foods6120108
   PubMed Web of Science ®Google Scholar
• Chovancova, E., Pavelka, A., Benes, P., Strnad, O., Brezovsky, J., Kozlikova, B., Gora, A., Sustr, V., Klvana, M., Medek, P., Biedermannova, L., Sochor, J., & Damborsky, J. (2012). CAVER 3.0: A tool for the analysis of transport pathways in dynamic protein structures. PLoS Computational Biology, 8(10), e1002708. https://doi.org/10.1371/journal.pcbi.1002708
   PubMed Web of Science ®Google Scholar
• Durinx, C., Lambeir, A. M., Bosmans, E., Falmagne, J. B., Berghmans, R., Haemers, A., Scharp, S., & Meester, I. D. (2000). Molecular characterization of dipeptidyl peptidase activity in serum: Soluble CD26/dipeptidyl peptidase IV is responsible for the release of X-Pro dipeptides. European Journal of Biochemistry, 267(17), 5608–5613. https://doi.org/10.1046/j.1432-1327.2000.01634.x
   PubMedGoogle Scholar
• Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., & Raghava, G. (2015). Peptide toxicity prediction (pp. 143–157). Springer.
   Google Scholar
• Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., Raghava, G. P. S., & Discovery, O, S. D (2013). In silico approach for predicting toxicity of peptides and proteins. PLoS One, 8(9), e73957, https://doi.org/10.1371/journal.pone.0073957
   PubMed Web of Science ®Google Scholar
• Izrailev, S., Stepaniants, S., Isralewitz, B., Kosztin, D., & Schulten, K. (1999). Steered molecular dynamics. Springer.
   Google Scholar
• Ji, W., Zhang, C., & Ji, H. (2017). Purification, identification and molecular mechanism of two dipeptidyl peptidase IV (DPP-IV) inhibitory peptides from Antarctic krill (Euphausia superba) protein hydrolysate. Journal of Chromatography B, Analytical Technologies in the Biomedical and Life Sciences, 1064, 56–61. https://doi.org/10.1016/j.jchromb.2017.09.001
   PubMed Web of Science ®Google Scholar
• Kalé, L., Bhatele, A., Bohm, E. J., Phillips, J. C., & Dam, H. V. J. J. (2011). NAnoscale molecular dynamics (NAMD). Encyclopedia of Parallel Computing.
   Google Scholar
• Klauda, J. B., Venable, R. M., Freites, J. A., O'Connor, J. W., Tobias, D. J., Mondragon-Ramirez, C., Vorobyov, I., MacKerell, A. D., & Pastor, R. W. (2010). Update of the CHARMM all-atom additive force field for lipids: Validation on six lipid types. The Journal of Physical Chemistry. B, 114(23), 7830–7843. https://doi.org/10.1021/jp101759q
   PubMed Web of Science ®Google Scholar
• Li-Chan, E. C. Y., Shih-Li, H., Chia-Ling, J., Kit-Pan, H., & Kuo-Chiang, H. (2012). Peptides derived from Atlantic salmon skin gelatin as dipeptidyl-peptidase IV inhibitors. Journal of Agricultural and Food Chemistry, 60(4), 973–978. https://doi.org/10.1021/jf204720q
   PubMed Web of Science ®Google Scholar
• Liu, C., Fang, L., Min, W., Liu, J., & Li, H. (2018). Exploration of the molecular interactions between angiotensin-I-converting enzyme (ACE) and the inhibitory peptides derived from hazelnut (Corylus heterophylla Fisch.). Food Chemistry, 245, 471–480. https://doi.org/10.1016/j.foodchem.2017.10.095
   PubMed Web of Science ®Google Scholar
• Minkiewicz, P., Dziuba, J., & Michalska, J. (2011). Bovine meat proteins as potential precursors of biologically active peptides-a computational study based on the BIOPEP database. Food Science and Technology International = Ciencia y Tecnologia de Los Alimentos International, 17(1), 39–45. https://doi.org/10.1177/1082013210368461
   PubMed Web of Science ®Google Scholar
• Minkiewicz, P., Dziuba, J., Iwaniak, A., Dziuba, M., & Darewicz, M. (2008). BIOPEP database and other programs for processing bioactive peptide sequences. Journal of AOAC International, 91(4), 965–980. https://doi.org/10.1093/jaoac/91.4.965
   PubMed Web of Science ®Google Scholar
• Minoukadeh, K., Chipot, C., & LelieVre, T. (2010). Potential of mean force calculations: A multiple-walker adaptive biasing force approach. Journal of Chemical Theory and Computation, 6(4), 1008–1017. https://doi.org/10.1021/ct900524t
   Web of Science ®Google Scholar
• Nongonierma, A. B., Dellafiora, L., Paolella, S., Galaverna, G., Cozzini, P., & Fitzgerald, R. J. (2018). In silico approaches applied to the study of peptide analogs of Ile-Pro-Ile in relation to their dipeptidyl peptidase IV inhibitory properties. Frontiers in Endocrinology, 9, 329. https://doi.org/10.3389/fendo.2018.00329
   PubMed Web of Science ®Google Scholar
• Nongonierma, A. B., Paolella, S., Mudgil, P., Maqsood, S., & Fitzgerald, R. J. (2018). Identification of novel dipeptidyl peptidase IV (DPP-IV) inhibitory peptides in camel milk protein hydrolysates. Food Chemistry, 244, 340–348. https://doi.org/10.1016/j.foodchem.2017.10.033
   PubMed Web of Science ®Google Scholar
• Panu, A., Manohar, J., Konstantin, A., Delphine, B., Gabor, C., Edouard, D. C., Séverine, D., Volker, F., Arnaud, F., & Elisabeth, G. (2012). ExPASy: SIB bioinformatics resource portal. Nucleic Acids Research, (W1), W597.
   PubMed Web of Science ®Google Scholar
• Piotr, M., Jerzy, D., Anna, I., Marta, D., & Magorzata, D. (2008). BIOPEP database and other programs for processing bioactive peptide sequences. Journal of AOAC International, 91(4), 960–980.
   Web of Science ®Google Scholar
• Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
   PubMed Web of Science ®Google Scholar
• Udenigwe, C. C. (2016). Towards rice bran protein utilization: In silico insight on the role of oryzacystatins in biologically-active peptide production. Food Chemistry, 191, 135–138. https://doi.org/10.1016/j.foodchem.2015.01.043
   PubMed Web of Science ®Google Scholar
• Velarde-Salcedo, A. J., Barrera-Pacheco, A., Lara-González, S., Montero-Morán, G. M., Díaz-Gois, A., González de Mejia, E., & Barba de la Rosa, A. P. (2013). In vitro inhibition of dipeptidyl peptidase IV by peptides derived from the hydrolysis of amaranth (Amaranthus hypochondriacus L.) proteins . Food Chemistry, 136(2), 758–764. https://doi.org/10.1016/j.foodchem.2012.08.032
   PubMed Web of Science ®Google Scholar
• Wu, G. S., Robertson, D. H., Brooks, C. L., & Vieth, M. (2003). Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm . Journal of Computational Chemistry, 24(13), 1549–1562. https://doi.org/10.1002/jcc.10306
   PubMed Web of Science ®Google Scholar
• Wu, G., Robertson, D. H., Brooks, C. L., & Vieth, M. (2003). Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm. Journal of Computational Chemistry, 24(13), 1549–1562. https://doi.org/10.1002/jcc.10306
   PubMed Web of Science ®Google Scholar
• Xu, C., Gu, F. L., & Zhu, C. Y. (2018). Excited-state Wolff rearrangement reaction of 5-diazo Meldrum’s acid: An ab initio on-the-fly nonadiabatic dynamic simulation. Physical Chemistry Chemical Physics, 20(35), 22681–22688. https://doi.org/10.1039/C8CP04164D
   PubMed Web of Science ®Google Scholar
• Yaron, A., & Naider, F. (1993). Proline-dependent structural and biological properties of peptides and proteins. Critical Reviews in Biochemistry and Molecular Biology, 28(1), 31–81. https://doi.org/10.3109/10409239309082572
   PubMed Web of Science ®Google Scholar
• Zhang, N., & Zhao, H. (2016). Enriching screening libraries with bioactive fragment space. Bioorganic & Medicinal Chemistry Letters, 26(15), 3594–3597. https://doi.org/10.1016/j.bmcl.2016.06.013
   PubMed Web of Science ®Google Scholar
• Zhu, J., Lv, Y., Han, X., Xu, D., & Han, W. (2017). Understanding the differences of the ligand binding/unbinding pathways between phosphorylated and non-phosphorylated ARH1 using molecular dynamics simulations. Scientific Reports, 7(1), 12439. https://doi.org/10.1038/s41598-017-12031-0
   PubMedGoogle Scholar