IN-SILICO ALANINE SCANNING ANALYSIS ON THE CATALYTIC RESIDUES OF A NOVEL Β-GLUCOSIDASE FROM TRICHODERMA ASPERELLUM UC1
DOI:
https://doi.org/10.11113/jurnalteknologi.v83.15098Keywords:
β-glucosidase from UC1, alanine scanning, catalytic triad, β-glucosidase, molecular docking, molecular dynamics.Abstract
Currently, the catalytic residue of the highly prolific fungal β-glucosidase (BGL) of Trichoderma asperellum UC1 remains unvalidated. The study used the alanine scanning method to confirm the catalytic residues of the BGL as Glu165, Asp226, and Glu423. This method cancels out all intermolecular hydrogen bonds with substrates, lignin, hemicellulose, and cellulose. Results revealed an overall decline in the stability of the energy-minimized mutant enzymes' compared to the wild-type BGL. The mutant enzyme registered lower PROCHECK (91.0%), ERRAT (93.09%), and Verify-3D (98.92%) values, in comparison to 90.2%, 92.09%, 98.06%, in the wild-type BGL, respectively. The mutant BGL UC1-substrate complexes were less stable than the wild-type enzyme, in which the mutant exhibited higher binding energies for docked lignin (−7.4% kcal mol-1), cellulose (−7.2 kcal mol-1), and hemicellulose (−7.2 kcal mol-1). Binding energies of the wild-type BGL with the corresponding substrates were lower at −7.9 kcal mol-1, −8.1 kcal mol-1, and −7.8 kcal mol-1. An interesting observation was that the alanine scanning changed the substrate preference order based on the calculated binding energies. The mutant BGL bound preferentially to lignin>cellulose=hemicellulose, while the wild-type BGL was selective to cellulose>lignin>hemicellulose. Hence, the findings convey the high likelihood of Glu165, Asp 256, and Glu423 are the catalytic residues of the BGL of T. asperellum UC1.
References
Ezeilo, U.R., Lee, C.T., Huyop, F., Zakaria, I.I. and Wahab, R.A. 2019. Raw oil palm frond leaves as cost-effective substrate for cellulase and xylanase productions by Trichoderma asperellum UC1 under solid-state fermentation. Journal of environmental management. 243206–217. DOI:https://doi.org/10.1016/j.jenvman.2019.04.113.
Ezeilo, U.R., Wahab, R.A. and Mahat, N.A. 2020. Optimization studies on cellulase and xylanase production by Rhizopus oryzae UC2 using raw oil palm frond leaves as substrate under solid state fermentation. Renewable Energy. 1561301–1312. DOI:https://doi.org/10.1016/j.renene.2019.11.149.
Elias, N., Chandren, S., Razak, F.I.A., Jamalis, J., Widodo, N. and Wahab, R.A. 2018. Characterization, optimization and stability studies on Candida rugosa lipase supported on nanocellulose reinforced chitosan prepared from oil palm biomass. International journal of biological macromolecules. 114306–316. DOI:https://doi.org/10.1016/j.ijbiomac.2018.03.095.
Elias, N., Wahab, R.A., Chandren, S., Razak, F.I.A. and Jamalis, J. 2019. Effect of operative variables and kinetic study of butyl butyrate synthesis by Candida rugosa lipase activated by chitosan-reinforced nanocellulose derived from raw oil palm leaves. Enzyme and microbial technology. 130109367. DOI:https://doi.org/10.1016/j.enzmictec.2019.109367.
Tiwari, P., Misra, B.N. and Sangwan, N.S. 2013. β-Glucosidases from the Fungus Trichoderma: An Efficient Cellulase Machinery in Biotechnological Applications. BioMed Research International. 2013203735. DOI:https://doi.org/10.1155/2013/203735.
Asad, S.A., Tabassum, A., Hameed, A., Afzal, A., Khan, S.A., Ahmed, R. and Shahzad, M. 2015. Determination of lytic enzyme activities of indigenous Trichoderma isolates from Pakistan. Brazilian Journal of Microbiology. 46(4): 1053–1064. DOI:https://doi.org/10.1590/S1517-838246420140787.
Bech, L., Busk, P.K. and Lange, L. 2015. Cell wall degrading enzymes in Trichoderma asperellum grown on wheat bran. Fungal Genom Biol. 4116. DOI:https://doi.org/10.4172/2165-8056.1000116.
Santos, C.A., Zanphorlin, L.M., Crucello, A., Tonoli, C.C.C., Ruller, R., Horta, M.A.C., Murakami, M.T. and de Souza, A.P. 2016. Crystal structure and biochemical characterization of the recombinant ThBgl, a GH1 β-glucosidase overexpressed in Trichoderma harzianum under biomass degradation conditions. Biotechnology for Biofuels. 9(1): 71. DOI:https://doi.org/10.1186/s13068-016-0487-0.
McIntosh, Lawrence P., Greg Hand, Philip E. Johnson, Manish D. Joshi, Michael Körner, Leigh A. Plesniak, Lothar Ziser, and Warren W. Wakarchuk. "i Withers, S.G. 1996. The pKa of the general acid/base carboxyl group of a glycosidase cycles during catalysis: A13c-NMR study of Bacillus circulans xylanase. Biochemistry. 35(31): 9958–9966. DOI:https://doi.org/10.1021/bi9613234.
Florindo, R.N., Souza, V.P., Mutti, H.S., Camilo, C., Manzine, L.R., Marana, S.R., Polikarpov, I. and Nascimento, A.S. 2018. Structural insights into β-glucosidase transglycosylation based on biochemical, structural and computational analysis of two GH1 enzymes from Trichoderma harzianum. New biotechnology. 40218–227. DOI:https://doi.org/10.1016/j.nbt.2017.08.012.
Bahaman, A.H., Wahab, R.A., Abdul Hamid, A.A., Abd Halim, K.B. and Kaya, Y. 2020. Molecular docking and molecular dynamics simulations studies on β-glucosidase and xylanase Trichoderma asperellum to predict degradation order of cellulosic components in oil palm leaves for nanocellulose preparation. Journal of Biomolecular Structure and Dynamics. 1–14. DOI:https://doi.org/10.1080/07391102.2020.1751713.
Fellinger, K., Leonhardt, H. and Spada, F. 2008. A mutagenesis strategy combining systematic alanine scanning with larger mutations to study protein interactions. Analytical biochemistry. 373(1): 176–178. DOI:https://doi.org/10.1016/j.ab.2007.10.016.
Morrison, K.L. and Weiss, G.A. 2001. Combinatorial alanine-scanning. Current opinion in chemical biology. 5(3): 302–307. DOI:https://doi.org/10.1016/S1367-5931(00)00206-4.
Biswas, A., Shukla, A., Vijayan, R.S.K., Jeyakanthan, J. and Sekar, K. 2017. Crystal structures of an archaeal thymidylate kinase from Sulfolobus tokodaii provide insights into the role of a conserved active site Arginine residue. Journal of structural biology. 197(3): 236–249. DOI:https://doi.org/10.1016/j.jsb.2016.12.001.
Cunningham, B.C. and Wells, J.A. 1989. High-resolution epitope mapping of hGH-receptor interactions by alanine-scanning mutagenesis. Science. 244(4908): 1081–1085. DOI:https://doi.org/10.1126/science.2471267.
Yamamoto, K., Choi, M., Abe, D., Shimizu, M. and Yamada, S. 2007. Alanine scanning mutational analysis of the ligand binding pocket of the human Vitamin D receptor. The Journal of Steroid Biochemistry and Molecular Biology. 103(3): 282–285. DOI:https://doi.org/10.1016/j.jsbmb.2006.12.018.
SaÃz-Urra, L., Cabrera, M.A. and Froeyen, M. 2011. Exploring the conformational changes of the ATP binding site of gyrase B from Escherichia coli complexed with different established inhibitors by using molecular dynamics simulation: Protein–ligand interactions in the light of the alanine scanning and fre. Journal of Molecular Graphics and Modelling. 29(5): 726–739. DOI:https://doi.org/10.1016/j.jmgm.2010.12.005.
Seenivasagan, R., Kasimani, R., Rajakumar, S., Kalidoss, R. and Ayyasamy, P.M. 2016. Comparative modelling and molecular docking of nitrate reductase from Bacillus weihenstephanensis (DS45). Journal of Taibah University for Science. 10(4): 621–630. DOI:https://doi.org/10.1016/j.jtusci.2016.02.006.
Kumarasinghe, I.R. and Woster, P.M. 2018. Cyclic peptide inhibitors of lysine-specific demethylase 1 with improved potency identified by alanine scanning mutagenesis. European journal of medicinal chemistry. 148210–220. DOI:https://doi.org/10.1002/prot.23165.
Hu, Y., Hua, Q., Sun, G., Shi, K., Zhang, H., Zhao, K., Jia, S., Dai, Y. and Wu, Q. 2018. The catalytic activity for ginkgolic acid biodegradation, homology modeling and molecular dynamic simulation of salicylic acid decarboxylase. Computational biology and chemistry. 7582–90. DOI:https://doi.org/10.1016/j.compbiolchem.2018.05.003.
Pandurangan, A.P., Ochoa-Montaño, B., Ascher, D.B. and Blundell, T.L. 2017. SDM: a server for predicting effects of mutations on protein stability. Nucleic acids research. 45(W1): W229–W235. DOI:https://doi.org/10.1093/nar/gkx439.
Park, H., Ovchinnikov, S., Kim, D.E., DiMaio, F. and Baker, D. 2018. Protein homology model refinement by large-scale energy optimization. Proceedings of the National Academy of Sciences. 115(12): 3054–3059. DOI:https://doi.org/10.1093/nar/gkx439.
Makarewicz, T. and KazÌmierkiewicz, R. 2013. Molecular dynamics simulation by GROMACS using GUI plugin for PyMOL. J. Chem. Inf. Model. 1229–1234. DOI:https://doi.org/10.1021/ci400071x.
Feig, M. 2017. Computational protein structure refinement: Almost there, yet still so far to go. Wiley Interdisciplinary Reviews: Computational Molecular Science. 7(3): e1307. DOI:https://doi.org/10.1002/wcms.1307.
Hess, B., Kutzner, C., van der Spoel, D. and Lindahl, E. 2008. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. Journal of Chemical Theory and Computation. 4(3): 435–447. DOI:https://doi.org/10.1021/ct700301q.
Colovos, C. and Yeates, T.O. 1993. Verification of protein structures: patterns of nonbonded atomic interactions. Protein science. 2(9): 1511–1519. DOI:https://doi.org/10.1002/pro.5560020916.
Anuar, N.F.S.K., Wahab, R.A., Huyop, F., Halim, K.B.A. and Hamid, A.A.A. 2019. In silico mutation on a mutant lipase from Acinetobacter haemolyticus towards enhancing alkaline stability. Journal of Biomolecular Structure and Dynamics. 1–15. DOI:https://doi.org/10.1080/07391102.2019.1683074.
Vedamurthy, G. V, Ahmad, H., Onteru, S.K. and Saxena, V.K. 2019. In silico homology modelling and prediction of novel epitopic peptides from P24 protein of Haemonchus contortus. Gene. 703102–111. DOI:https://doi.org/10.1016/j.gene.2019.03.056.
Bahaman, A.H., Abdul Wahab, R., Hamid, A.A.A., Halim, K.B.A., Kaya, Y. and Edbeib, M.F. 2019. Substrate docking and molecular dynamic simulation for prediction of fungal enzymes from Trichoderma species-assisted extraction of nanocellulose from oil palm leaves. Journal of Biomolecular Structure and Dynamics. 1–13. DOI:https://doi.org/10.1080/07391102.2019.1679667.
Lemkul, J. 2018. From proteins to perturbed Hamiltonians: A suite of tutorials for the GROMACS-2018 molecular simulation package [article v1. 0]. Living Journal of Computational Molecular Science. 1(1): 5068. DOI:https://doi.org/10.33011/livecoms.1.1.5068.
Xiang, Z. 2006. Advances in homology protein structure modeling. Current Protein and Peptide Science. 7(3): 217–227. DOI:https://doi.org/10.2174/138920306777452312.
Edbeib, M.F., Wahab, R.A., Kaya, Y. and Huyop, F. 2017. In silico characterization of a novel dehalogenase (DehHX) from the halophile Pseudomonas halophila HX isolated from Tuz Gölü Lake, Turkey: insights into a hypersaline-adapted dehalogenase. Annals of Microbiology. 67(5): 371–382. DOI:https://doi.org/10.1007/s13213-017-1266-2.
Kuriata, A., Gierut, A.M., Oleniecki, T., Ciemny, M.P., Kolinski, A., Kurcinski, M. and Kmiecik, S. 2018. CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic Acids Research. 46(W1): W338–W343. DOI:https://doi.org/10.1093/nar/gky356.
Yang, Y. 2016. Chapter 8 - Side Reactions on Hydroxyl and Carboxyl Groups in Peptide Synthesis. Y.B.T.-S.R. in P.S. Yang, ed. Academic Press. 203–216.
Doering, J.A., Lee, S., Kristiansen, K., Evenseth, L., Barron, M.G., Sylte, I. and LaLone, C.A. 2018. In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool. Toxicological Sciences. 166(1): 131–145. DOI:https://doi.org/10.1093/toxsci/kfy186.
Worth, C.L., Preissner, R. and Blundell, T.L. 2011. SDM—a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Research. 39(suppl_2): W215–W222. DOI:https://doi.org/10.1093/nar/gkr363.
Jeffrey, G.A. and Jeffrey, G.A. 1997. An introduction to hydrogen bonding. Oxford university press New York.
Lee, H.S. and Zhang, Y. 2012. BSPâ€SLIM: A blind lowâ€resolution ligandâ€protein docking approach using predicted protein structures. Proteins: Structure, Function, and Bioinformatics. 80(1): 93–110. DOI:https://doi.org/10.1002/prot.23165.
Neitzel, J.J. 2010. Enzyme catalysis: the serine proteases. Nature Education. 3(9): 21.
Kovacic, F., Mandrysch, A., Poojari, C., Strodel, B. and Jaeger, K.-E. 2016. Structural features determining thermal adaptation of esterases. Protein Engineering, Design and Selection. 29(2): 65–76. DOI:https://doi.org/10.1093/protein/gzv061.
Versavel, J. 1999. Road Safety Through Video Detection. Intelligent Transportation System, 1999, Proceedings 1999 IEEE/IEEJ/JSAI International Conference. 753-757.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Ozkurt, C., and Camci, F. 2009. Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance System Using Neural Networks. Mathematical and Computer Applications. 14(3): 187-196.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Koutsia, A., Semertzidis, T., Dimitropoulos, K., Grammalidis, N., & Georgouleas, K. 2008, June. Intelligent Traffic Monitoring and Surveillance with Multiple Cameras. In Content-Based Multimedia Indexing, 2008. CBMI 2008. International Workshop on IEEE. 125-132.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Yoneyama, A., Yeh, C. H. and JayKuo, C. C. 2005: Robust Vehicle and Traffic Information Extraction for Highway Surveillance. Eurasip Journal on Applied Signal Processing 2005. 2305-21.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Parameswaran, V., Burlina, P. and Chellappa, R. 1997. Performance Analysis and Learning Approaches for Vehicle Detection and Counting in Aerial Images. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 4: 2753-2756.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Redding, N. J., Booth, D. M. and Jones, R. 2005. Urban video Surveillance from Airborne and Ground-based Platforms. Proceedings of the IEEE International Symposium on Imaging for Crime Detection and Prevention. 79-84.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Coifman, B., McCord, M., Mishalani, R. G., Iswalt, M., & Ji, Y. 2006, March. Roadway Traffic Monitoring from an Unmanned Aerial Vehicle. In IEE Proceedings-Intelligent Transport Systems. 153(1): 11-20.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Angel, A., Hickman, M., Mirchandani, P. and Chandnani, D. 2002. Application of Aerial Video for Traffic Flow Monitoring and Management. Proceedings of the 7th International Conference on Applications of Advanced Technology in Transportation. 346-53.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Medioni, G., Cohen, I., BreAˆ mond, F., Hongeng, S. and Nevatia, R. 2001. Event Detection and Analysis from Video Streams. IEEE Transactions on Pattern Analysis and Machine Intelligence. 23: 873-89.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Srinivasan, S., Latchman, H., Shea, J., Wong, T., & McNair, J. 2004, October. Airborne Traffic Surveillance Systems: Video Surveillance of Highway Traffic. In Proceedings of the ACM 2nd International Workshop on Video Surveillance & Sensor Networks. 131-135.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Roldán, J. J., Joossen, G., Sanz, D., del Cerro, J., and Barrientos, A. 2015. Mini-UAV Based Sensory System for Measuring Environmental Variables in Greenhouses. Sensors. 15(2): 3334-3350.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Mohamed, N., Al-Jaroodi, J., Jawhar, I., & Lazarova-Molnar, S. 2013, May. Middleware Requirements for Collaborative Unmanned Aerial Vehicles. In Unmanned Aircraft Systems (ICUAS), 2013 International Conference on IEEE. 1051-1060.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Bedford, M. A. 2015. Unmanned Aircraft System (UAS) Service Demand 2015-2035.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Versavel, J. 1999. Road Safety Through Video Detection. Intelligent Transportation System, 1999, Proceedings 1999 IEEE/IEEJ/JSAI International Conference. 753-757.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Ozkurt, C., and Camci, F. 2009. Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance System Using Neural Networks. Mathematical and Computer Applications. 14(3): 187-196.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Koutsia, A., Semertzidis, T., Dimitropoulos, K., Grammalidis, N., & Georgouleas, K. 2008, June. Intelligent Traffic Monitoring and Surveillance with Multiple Cameras. In Content-Based Multimedia Indexing, 2008. CBMI 2008. International Workshop on IEEE. 125-132.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Yoneyama, A., Yeh, C. H. and JayKuo, C. C. 2005: Robust Vehicle and Traffic Information Extraction for Highway Surveillance. Eurasip Journal on Applied Signal Processing 2005. 2305-21.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Parameswaran, V., Burlina, P. and Chellappa, R. 1997. Performance Analysis and Learning Approaches for Vehicle Detection and Counting in Aerial Images. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. 4: 2753-2756.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Redding, N. J., Booth, D. M. and Jones, R. 2005. Urban video Surveillance from Airborne and Ground-based Platforms. Proceedings of the IEEE International Symposium on Imaging for Crime Detection and Prevention. 79-84.
DOI : http://dx.doi.org/10.11113/jt.v79.9987
Coifman, B., McCord, M., Mishalani, R. G., Iswalt, M., & Ji, Y. 2006, March. Roadway Traffic Monitoring from an Unmanned Aerial Vehicle. In IEE Proceedings-Intelligent Transport Systems. 153(1): 11-20.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.