Structural and functional insights from molecular modeling, docking, and MD simulations of bacterial xylanase: implications for biofuel efficiency

Arpita Sarangi , Sandesh Behera , Manish Paul , Sumanta Kumar Sahu , Rakesh kumar , Hrudayanath Thatoi

Systems Microbiology and Biomanufacturing ›› 2025, Vol. 5 ›› Issue (3) : 1191 -1210.

PDF
Systems Microbiology and Biomanufacturing ›› 2025, Vol. 5 ›› Issue (3) : 1191 -1210. DOI: 10.1007/s43393-025-00329-4
Original Article
research-article

Structural and functional insights from molecular modeling, docking, and MD simulations of bacterial xylanase: implications for biofuel efficiency

Author information +
History +
PDF

Abstract

Xylan is the major hemicellulose component of the plant cell wall (second most naturally abundant carbohydrate) and is a linear polymer of β-d-xylopyranosyl units linked by β-1-4 glycosidic bonds. Microbial xylanase is an efficient xylan degrading enzyme that catalyzes the hydrolysis of internal β-1-4 glycosidic bonds and is reported to be involved in bioethanol production from lignocellulosic biomass. Due to its wide range of applications at the industrial level, it is important to understand the structural and functional aspects of xylanase. Therefore, in the present study, an in silico investigation was carried out through the docking of bacterial xylanases with multiple substrates xylobiose, xylotriose, xylotetraose, and xylopentaose to determine the molecular interaction and substrate specificity during enzymatic catalysis. The amino acid sequences of four xylanolytic bacterial species, viz., Actinosynnema pretiosum, Streptomyces sp., Spirosoma sordidisoli and Streptomyces bingchenggensis were retrieved from UniProtKB and their homologous structures were predicted using the SWISS-PROT model webserver to undertake docking studies using the xylanase enzyme of the above bacterial species and xylan as a substrate. Results of the docking studies showed that the xylanase of all the bacterial species exhibited the highest interaction with xylopentaose. Binding energy was determined using the DINC server. Further multiple sequence alignment (MEGA X), phylogenetic analysis (MEGA X), and molecular dynamics simulation (GROMACS) studies were performed. Overall, the present in silico study will reveal the importance of understanding the catalytic mechanism of substrate xylan with different bacterial xylanases, which could be helpful for the development of engineered xylanase towards the efficient production of bioethanol from lignocellulosic biomass.

Keywords

Bacterial xylanase / Lignocellulosic biomass / Molecular docking / Molecular dynamic simulation / Biofuel production

Cite this article

Download citation ▾
Arpita Sarangi, Sandesh Behera, Manish Paul, Sumanta Kumar Sahu, Rakesh kumar, Hrudayanath Thatoi. Structural and functional insights from molecular modeling, docking, and MD simulations of bacterial xylanase: implications for biofuel efficiency. Systems Microbiology and Biomanufacturing, 2025, 5(3): 1191-1210 DOI:10.1007/s43393-025-00329-4

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

CoughlanMP, HazlewoodGP. Beta‐1,4‐D‐xylan degrading enzyme systems: biochemistry, molecular biology and applications. Biotechnol Appl Biochem, 1993, 17(3): 259-289

[2]

BegQ, KapoorM, MahajanL, HoondalGS. Microbial xylanases and their industrial applications: a review. Appl Microbiol Biotechnol, 2001, 56: 326-338

[3]

JuturuV, WuJC. Microbial xylanases: engineering, production and industrial applications. Biotechnol Adv, 2012, 30(6): 1219-1227

[4]

PaësG, BerrinJG, BeaugrandJ. GH11 xylanases: structure/function/properties relationships and applications. Biotechnol Adv, 2012, 30(3): 564-592

[5]

LafondM, GuaisO, MaestracciM, BonninE, GiardinaT. Four GH11 xylanases from the xylanolytic fungus Talaromycesversatilis act differently on (arabino) xylans. Appl Microbiol Biotechnol, 2014, 98: 6339-6352

[6]

ChakdarH, KumarM, PandiyanK, SinghA, NanjappanK, KashyapPL, SrivastavaAK. Bacterial xylanases: biology to biotechnology. 3 Biotech, 2016, 6: 1-15

[7]

JuturuV, WuJC. Microbial exo-xylanases: a mini review. Appl Biochem Biotechnol, 2014, 174: 81-92

[8]

WaliaA, GuleriaS, MehtaP, ChauhanA, ParkashJ. Microbial xylanases and their industrial application in pulp and paper biobleaching: a review. 3 Biotech, 2017, 7: 1-12

[9]

GallardoO, Fernández-FernándezM, VallsC, ValenzuelaSV, RonceroMB, VidalT, DíazP, PastorFJ. Characterization of a family GH5 xylanase with activity on neutral oligosaccharides and evaluation as a pulp bleaching aid. Appl Environ Microbiol, 2010, 76(18): 6290-6294

[10]

WaliaA, MehtaP, GuleriaS, ShirkotCK. Modification in the properties of paper by using cellulase-free xylanase produced from alkalophilic Cellulosimicrobiumcellulans CKMX1 in biobleaching of wheat straw pulp. Can J Microbiol, 2015, 61(9): 671-681

[11]

Chakdar H, Kumar M, Pandiyan K, Singh A, Nanjappan K, Kashyap PL, Srivastava AK. Bacterial xylanases: biology to biotechnology. 3 Biotech. 2016;6:1-15.

[12]

SidhuG, WithersSG, NguyenNT, McIntoshLP, ZiserL, BrayerGD. Sugar ring distortion in the glycosyl-enzyme intermediate of a family G/11 xylanase. Biochemistry, 1999, 38(17): 5346-5354

[13]

VyasVK, UkawalaRD, GhateM, ChinthaC. Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci, 2012, 7411

[14]

UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res, 2019, 47(D1): D506-D515

[15]

MadeiraF, ParkYM, LeeJ, BusoN, GurT, MadhusoodananN, BasutkarP, TiveyAR, PotterSC, FinnRD, LopezR. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res, 2019, 47(W1): W636-W641

[16]

Al AzadS, FarjanaM, MazumderB, Abdullah-Al-MamunM, HaqueAI. Molecular identification of a Bacillus cereus strain from Murrah buffalo milk showed in vitro bioremediation properties on selective heavy metals. J Adv Vet Anim Res, 2020, 7162

[17]

HolmesS. Bootstrapping phylogenetic trees: theory and methods. Stat Sci, 2003, 18(2): 241-255

[18]

HigginsD. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucl Acids Res, 1994, 22: 4673-4680

[19]

BermanHM, WestbrookJ, FengZ, GillilandG, BhatTN, WeissigH, ShindyalovIN, BournePE. The protein data bank. Nucleic Acids Res, 2000, 28(1): 235-242

[20]

FerdausiN, IslamS, RimtiFH, QuayumST, ArshadEM, IbnatA, IslamT, ArefinA, EmaTI, BiswasP, DeyD. Point-specific interactions of isovitexin with the neighboring amino acid residues of the hACE2 receptor as a targeted therapeutic agent in suppressing the SARS-CoV-2 influx mechanism. J Adv Vet Anim Res, 2022, 92230

[21]

DeyD, PaulPK, Al AzadS, Al MazidMF, KhanAM, SharifMA, RahmanMH. Molecular optimization, docking, and dynamic simulation profiling of selective aromatic phytochemical ligands in blocking the SARS-CoV-2 S protein attachment to ACE2 receptor: an in silico approach of targeted drug designing. J Adv Veterinary Anim Res, 2021, 8124

[22]

JaghooriMM, BleijlevensB, OlabarriagaSD. 1001 Ways to run AutoDock Vina for virtual screening. J Comput Aided Mol Des, 2016, 30: 237-249

[23]

RehmanHM, ShoaibW, KhanMN, YousafN, BashirF, BashirH, AliQ, HanS. A comprehensive in silico study of the NDB-IL-24 fusion protein for tumor targeting: a promising anti-cancer therapeutic candidate. J Biol Regul Homeost Agents, 2024, 38(4): 3449-3461

[24]

ArefinA, EmaTI, IslamT, HossenMS, IslamT, Al AzadS, BadalMNU, IslamMA, BiswasP, AlamNU, IslamE. Target specificity of selective bioactive compounds in blocking α-dystroglycan receptor to suppress Lassa virus infection: an in silico approach. J Biomed Res, 2021, 356459

[25]

Jin-XiaLIN, ZhangLY, ZhangGY, Bai-ShanFANG. Molecular docking of Bacillus pumilus xylanase and xylan substrate using computer modeling. Chin J Biotechnol, 2007, 23(4): 715-718

[26]

Conejo-SaucedoU, Cano-CamachoH, López-RomeroE, RiveraMGV, AliciaL-M, MaríaGZ-P. Cloning and characterization of an endo–1, 4-xylanase gene from Colletotrichum lindemuthianum and phylogenetic analysis of similar genes from phytopathogenic fungus. Afr J Microbiol Res, 2016, 10(32): 1292-1305

[27]

AltschulSF, GishW, MillerW, MyersEW, LipmanDJ. Basic local alignment search tool. J Mol Biol, 1990, 215(3): 403-410

[28]

TörrönenA, HarkkiA, RouvinenJ. Three-dimensional structure of endo-1, 4-beta-xylanase II from Trichoderma reesei: two conformational states in the active site. EMBO J, 1994, 13(11): 2493-2501

[29]

WaterhouseA, BertoniM, BienertS, StuderG, TaurielloG, GumiennyR, HeerFT, de BeerTAP, RempferC, BordoliL, LeporeR. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res, 2018, 46(W1): W296-W303

[30]

PaulM, PandaG, MohapatraPKD, ThatoiH. Study of structural and molecular interaction for the catalytic activity of cellulases: an insight in cellulose hydrolysis for higher bioethanol yield. J Mol Struct, 2020, 1204127547

[31]

RahmanMO, AhmedSS. Anti-angiogenic potential of bioactive phytochemicals from Helicteresisora targeting VEGFR-2 to fight cancer through molecular docking and molecular dynamics simulation. J Biomol Struct Dyn, 2023, 41(15): 7447-7462

[32]

MorshedAH, Al AzadS, MiaMAR, UddinMF, EmaTI, YeasinRB, SrishtiSA, SarkerP, AurthiRY, JamilF, SamiaNSN. Oncoinformatic screening of the gene clusters involved in the HER2-positive breast cancer formation along with the in silico pharmacodynamic profiling of selective long-chain omega-3 fatty acids as the metastatic antagonists. Mol Diversity, 2023, 27(6): 2651-2672

Funding

Department of Science and Technology, Government of Odisha(ST-BT-MISC-0010-2022-2373)

RIGHTS & PERMISSIONS

Jiangnan University

AI Summary AI Mindmap
PDF

380

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/