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2000
Volume 21, Issue 6
  • ISSN: 1570-1646
  • E-ISSN: 1875-6247

Abstract

Background

The stability of Rho depends on its amino acids, protein structures, oligomerization, strong interactions, salt bridges, and bonding patterns. A single amino acid change can alter the protein's tertiary structure. Rhomboids cut misfolded membranes. They regulate protein synthesis, mitochondrial integrity, parasite invasion, and growth factor secretion. Research into these proteins and their inhibitors can improve therapeutic targeting of the Rho protein, which reduces type II diabetes and Parkinson's disease.

Objectives

The primary aim is to examine Antarctic Rho sequences, amino acid contents, and active domains. Additionally, modeling tools will be used to build three-dimensional Rho structures from extremophiles. Docking simulations determine the predicted proteases' proteolytic and aminopeptidase activity.

Methods

PATRIC discovered Antarctic Rho—MAFFT-aligned structures. Meanwhile, InterProScan and ProtParam determined the amino acid content and active domain. To foretell the 3D structure of proteins, I-TASSER, CB-Dock, and Discovery Studio were used.

Results

Rhomboid gene alignments amongst Antarctica isolates show that protein evolution was limited at cold temperatures. All isolated proteins had similar active domains and amino acid sequences. Rhombic structure and proteolytic activity have not changed appreciably across evolution. A spatial arrangement of Rho reduces resilience. Antarctic Rho contained critical amino acid residues LEU (292, 251, 252, 289) and ALA 304.

Conclusion

Antarctic isolates' Rhomboid alignment demonstrates restricted evolution, possibly bacterial horizontal gene transfer. Despite multiple actions, active domain presence is maintained physically and functionally. Bacterial Rhomboids are therapeutic targets due to their rising role in various illnesses.

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References

  1. TamuraK. NeiM. KumarS. Prospects for inferring very large phylogenies by using the neighbor-joining method.Proc. Natl. Acad. Sci. USA200410130110301103510.1073/pnas.0404206101 15258291
     [Google Scholar]
  2. TamuraK. StecherG. KumarS. MEGA11: Molecular Evolutionary Genetics Analysis Version 11.Mol. Biol. Evol.20213873022302710.1093/molbev/msab120 33892491
     [Google Scholar]
  3. YinW. WangY. LiuL. HeJ. Biofilms: the microbial “protective clothing” in extreme environments.Int. J. Mol. Sci.20192014342310.3390/ijms20143423 31336824
     [Google Scholar]
  4. DeviR. KaurT. KourD. RanaK.L. YadavA. YadavA.N. Beneficial fungal communities from different habitats and their roles in plant growth promotion and soil health.Microbial Biosystems202051214710.21608/mb.2020.32802.1016
     [Google Scholar]
  5. GizawB. Potential microbial ecologyin ethiopia and ex-situ conservation effort. Annal.Microbiol. Infect. Dis.201814839
     [Google Scholar]
  6. VigneswariS. AmeliaT.S.M. HazwanM.H. MouriyaG.K. BhubalanK. AmirulA.A.A. RamakrishnaS. Transformation of biowaste for medical applications: incorporation of biologically derived silver nanoparticles as antimicrobial coating.Antibiotics (Basel)202110322910.3390/antibiotics10030229 33668352
     [Google Scholar]
  7. SalléeJ.B. Southern ocean warming.Oceanography (Wash. D.C.)2018312526210.5670/oceanog.2018.215
     [Google Scholar]
  8. MarínR.S. Spatial modelling of the temperature at the top of permafrost in Cierva Point.(Antarctic Peninsula)2018
     [Google Scholar]
  9. VimercatiR. Microbial adaptation and diversity in the high-elevation extreme cryosphere: Implications for astrobiology.PhD Thesis, University of Colorado at Boulder 2019
     [Google Scholar]
  10. SteffiP. AnburajM.R.R. A Textbook on Marine Microbiology.Ryan Publishers20201314
     [Google Scholar]
  11. StalL.J. A Sea of Microbes: What’s So Special about Marine Microbiology. In: The Marine Microbiome.Springer2022144
     [Google Scholar]
  12. Vásquez-PonceF. Higuera-LlanténS. PavlovM.S. MarshallS.H. Olivares-PachecoJ. Phylogenetic MLSA and phenotypic analysis identification of three probable novel Pseudomonas species isolated on King George Island, South Shetland, Antarctica.Braz. J. Microbiol.201849469570210.1016/j.bjm.2018.02.005 29598976
     [Google Scholar]
  13. SilvaT.R. SilvaL.C.F. de QueirozA.C. Alexandre MoreiraM.S. de Carvalho FragaC.A. de MenezesG.C.A. RosaL.H. BicasJ. de OliveiraV.M. DuarteA.W.F. Pigments from Antarctic bacteria and their biotechnological applications.Crit. Rev. Biotechnol.202141680982610.1080/07388551.2021.1888068 33622142
     [Google Scholar]
  14. ArbigeM.V. ShettyJ.K. ChotaniG.K. Industrial enzymology: the next chapter.Trends Biotechnol.201937121355136610.1016/j.tibtech.2019.09.010 31679826
     [Google Scholar]
  15. PrayogoF.A. BudiharjoA. KusumaningrumH.P. WijanarkaW. SuprihadiA. NurhayatiN. Metagenomic applications in exploration and development of novel enzymes from nature: a review.J. Genet. Eng. Biotechnol.20201813910.1186/s43141‑020‑00043‑9 32749574
     [Google Scholar]
  16. ChongJ. LiuP. ZhouG. XiaJ. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data.Nat. Protoc.202015379982110.1038/s41596‑019‑0264‑1 31942082
     [Google Scholar]
  17. LearG. DickieI. BanksJ. BoyerS. BuckleyH. BuckleyT. CruickshankR. DopheideA. HandleyK. HermansS. KamkeJ. LeeC. MacDiarmidR. MoralesS. OrlovichD. SmissenR. WoodJ. HoldawayR. Methods for the extraction, storage, amplification and sequencing of DNA from environmental samples.N. Z. J. Ecol.20184211050A10.20417/nzjecol.42.9
     [Google Scholar]
  18. Cruz-CasasD.E. AguilarC.N. Ascacio-ValdésJ.A. Rodríguez-HerreraR. Chávez-GonzálezM.L. Flores-GallegosA.C. Enzymatic hydrolysis and microbial fermentation: The most favorable biotechnological methods for the release of bioactive peptides.Food Chem. Molecul. Sci.2021310004710.1016/j.fochms.2021.100047 35415659
     [Google Scholar]
  19. NaveedM. NadeemF. MehmoodT. BilalM. AnwarZ. AmjadF. Protease—a versatile and ecofriendly biocatalyst with multi-industrial applications: an updated review.Catal. Lett.2021151230732310.1007/s10562‑020‑03316‑7
     [Google Scholar]
  20. MatkawalaF. NighojkarS. KumarA. NighojkarA. Microbial alkaline serine proteases: Production, properties and applications.World J. Microbiol. Biotechnol.20213746310.1007/s11274‑021‑03036‑z 33730214
     [Google Scholar]
  21. BeganJ. Rhomboid intramembrane protease YqgP licenses bacterial membrane protein quality control as adaptor of FtsH AAA protease.EMBO J.20203910e102935
     [Google Scholar]
  22. Almeida HernandezY. Integrative analysis of the dynamics of rhomboid protease GlpG from Escherichia coli.Dissertation, Hamburg State and University 2019
     [Google Scholar]
  23. ChenI.M.A. ChuK. PalaniappanK. PillayM. RatnerA. HuangJ. HuntemannM. VargheseN. WhiteJ.R. SeshadriR. SmirnovaT. KirtonE. JungbluthS.P. WoykeT. Eloe-FadroshE.A. IvanovaN.N. KyrpidesN.C. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes.Nucleic Acids Res.201947D1D666D67710.1093/nar/gky901 30289528
     [Google Scholar]
  24. WattamA.R. DavisJ.J. AssafR. BoisvertS. BrettinT. BunC. ConradN. DietrichE.M. DiszT. GabbardJ.L. GerdesS. HenryC.S. KenyonR.W. MachiD. MaoC. NordbergE.K. OlsenG.J. Murphy-OlsonD.E. OlsonR. OverbeekR. ParrelloB. PuschG.D. ShuklaM. VonsteinV. WarrenA. XiaF. YooH. StevensR.L. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center.Nucleic Acids Res.201745D1D535D54210.1093/nar/gkw1017 27899627
     [Google Scholar]
  25. LiW. FuL. NiuB. WuS. WooleyJ. Ultrafast clustering algorithms for metagenomic sequence analysis.Brief. Bioinform.201213665666810.1093/bib/bbs035 22772836
     [Google Scholar]
  26. ChowdhuryB. GaraiG. A review on multiple sequence alignment from the perspective of genetic algorithm.Genomics20171095-641943110.1016/j.ygeno.2017.06.007 28669847
     [Google Scholar]
  27. ChatzouM. MagisC. ChangJ.M. KemenaC. BussottiG. ErbI. NotredameC. Multiple sequence alignment modeling: methods and applications.Brief. Bioinform.20161761009102310.1093/bib/bbv099 26615024
     [Google Scholar]
  28. KatohK. RozewickiJ. YamadaK.D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization.Brief. Bioinform.20192041160116610.1093/bib/bbx108 28968734
     [Google Scholar]
  29. KumarS. StecherG. LiM. KnyazC. TamuraK. Tamura KJMb, evolution. MEGA X: molecular evolutionary genetics analysis across computing platforms.Mol. Biol. Evol.20183561547154910.1093/molbev/msy096 29722887
     [Google Scholar]
  30. GargV.K. AvashthiH. TiwariA. JainP.A. RamketeP.W.R. KayasthaA.M. SinghV.K. MFPPI–multi FASTA ProtParam interface.Bioinformation2016122747710.6026/97320630012074 28104964
     [Google Scholar]
  31. QuevillonE. SilventoinenV. PillaiS. HarteN. MulderN. ApweilerR. LopezR. InterProScan: protein domains identifier.Nucleic Acids Res.200533Web Server issue)(Suppl. 2W1162015980438
     [Google Scholar]
  32. ZhengW. ZhangC. LiY. PearceR. BellE.W. ZhangY. Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.Cell Rep. Methods20211310001410.1016/j.crmeth.2021.100014
     [Google Scholar]
  33. CuenoM.E. UenoM. IguchiR. HaradaT. MikiY. YasumaruK. KisoN. WadaK. BabaK. ImaiK. Insights on the Structural Variations of the Furin-Like Cleavage Site Found Among the December 2019–July 2020 SARS-CoV-2 Spike Glycoprotein: A Computational Study Linking Viral Evolution and Infection.Front. Med. (Lausanne)2021861341210.3389/fmed.2021.613412 33777970
     [Google Scholar]
  34. XuD. ZhangY. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization.Biophys. J.2011101102525253410.1016/j.bpj.2011.10.024 22098752
     [Google Scholar]
  35. GreenerJ.G. KandathilS.M. JonesD.T. Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.Nat. Commun.2019101397710.1038/s41467‑019‑11994‑0 31484923
     [Google Scholar]
  36. RafaeeA. Kashani-AminE. MeybodiA.M. Ebrahim-HabibiA. SabbaghianM. Structural modeling of human AKAP3 protein and in silico analysis of single nucleotide polymorphisms associated with sperm motility.Sci. Rep.2022121365610.1038/s41598‑022‑07513‑9 35256641
     [Google Scholar]
  37. AyoubR. LeeY. RUPEE: A fast and accurate purely geometric protein structure search.PLoS One2019143e021371210.1371/journal.pone.0213712 30875409
     [Google Scholar]
  38. AyoubR. RUPEE: A Big Data Approach to Indexing and Searching Protein Structures.University of Missouri-Kansas City20217476
     [Google Scholar]
  39. VolkamerA. KuhnD. RippmannF. RareyM. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment.Bioinformatics201228152074207510.1093/bioinformatics/bts310 22628523
     [Google Scholar]
  40. WeiningerD. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules.J. Chem. Inf. Comput. Sci.1988281313610.1021/ci00057a005
     [Google Scholar]
  41. KhatikG.L. Identification of Possible Molecular Targets of Potential Anti-Alzheimer Drugs by Predicting their Binding Affinities Using Molecular Docking Technique.International Journal of Green Pharmacy20181202[IJGP
     [Google Scholar]
  42. AvupatiV.R. KurreP.N. BagadiS.R. MuthyalaM.K. YejellaR.P. De novo Based Ligand generation and Docking studies of PPARδ Agonists: Correlations between Predicted Biological activity vs. Biopharmaceutical Descriptors.Chem-Bio Informatics Journal201010748610.1273/cbij.10.74
     [Google Scholar]
  43. RowaiyeA.B. OlubiyiJ. BurD. UzochukwuI.C. AkpaA. EsimoneC.O. In silico screening and molecular dynamic simulation studies of potential small molecule immuno-modulators of the KIR2DS2 receptor.J. Phytomed. Therap.202120154256710.4314/jopat.v20i1.8
     [Google Scholar]
  44. HovhannisyanA. Improvement of the Antibacterial Activity of Benzylpenicillin in combination with Green Silver Nanoparticles against Staphylococcus aureus. Ohanyan, S.; Grabski, H.; Rshtuni, L.; Tiratsuyan, S. Hovhannisyan, A., Eds.International Conference on Nanotechnologies and Biomedical Engineering2019
     [Google Scholar]
  45. WangX. LiuQ. WuS. XuN. LiH. FengA. Identifying the Effect of Celastrol Against Ovarian Cancer With Network Pharmacology and In Vitro Experiments.Front. Pharmacol.20221373947810.3389/fphar.2022.739478 35370699
     [Google Scholar]
  46. LiuY. GrimmM. DaiW. HouM. XiaoZ.X. CaoY. CB-Dock: a web server for cavity detection-guided protein–ligand blind docking.Acta Pharmacol. Sin.202041113814410.1038/s41401‑019‑0228‑6 31263275
     [Google Scholar]
  47. AttanayakeR. Evaluation of Lignin Degrading Ability and In-silico Analysis and Molecular Docking of Laccase of Schizophyllum commune with Lignin. Dharmasiri, B.; Senanayake, G.; Perera, P.; Halmillawewa, A. Attanayake, R., Eds.;Proceedings of International Forestry and Environment Symposium202210.31357/fesympo.v26.5750
     [Google Scholar]
  48. SharmaS. SharmaA. GuptaU. Molecular Docking studies on the Anti-fungal activity of Allium sativum (Garlic) against Mucormycosis (black fungus) by BIOVIA discovery studio visualizer. Annal. Antiviral.Antiretroviral.2021512832
     [Google Scholar]
  49. SinhaR. KundrotasP.J. VakserI.A. Docking by structural similarity at protein‐protein interfaces.Proteins201078153235324110.1002/prot.22812 20715056
     [Google Scholar]
  50. KufarevaI. AbagyanR. Methods of protein structure comparison.Methods Mol. Biol.201185723125710.1007/978‑1‑61779‑588‑6_10 22323224
     [Google Scholar]
  51. LiQ. ZhangN. ZhangL. MaH. Differential evolution of members of the rhomboid gene family with conservative and divergent patterns.New Phytol.2015206136838010.1111/nph.13174 25417867
     [Google Scholar]
  52. KooninE.V. MakarovaK.S. RogozinI.B. DavidovicL. LetellierM.C. PellegriniL. The rhomboids: a nearly ubiquitous family of intramembrane serine proteases that probably evolved by multiple ancient horizontal gene transfers.Genome Biol.200343R1910.1186/gb‑2003‑4‑3‑r19 12620104
     [Google Scholar]
  53. SaitouN. NeiM. The neighbor-joining method: a new method for reconstructing phylogenetic trees.Mol. Biol. Evol.198744406425 3447015
     [Google Scholar]
  54. FelsensteinJ. Confidence limits on phylogenies with a molecular clock.Syst. Zool.198534215216110.2307/2413323
     [Google Scholar]
  55. DuttaS. PregartnerG. RückerF.G. HeitzerE. ZebischA. BullingerL. BergholdA. DöhnerK. SillH. Functional Classification of TP53 Mutations in Acute Myeloid Leukemia.Cancers (Basel)202012363710.3390/cancers12030637 32164171
     [Google Scholar]
  56. KumarS. DangiA.K. ShuklaP. BaishyaD. KhareS.K. Thermozymes: Adaptive strategies and tools for their biotechnological applications.Bioresour. Technol.201927837238210.1016/j.biortech.2019.01.088 30709766
     [Google Scholar]
  57. HaitS. MallikS. BasuS. KunduS. Finding the generalized molecular principles of protein thermal stability.Proteins202088678880810.1002/prot.25866 31872464
     [Google Scholar]
  58. Raymond-BouchardI. GoordialJ. ZolotarovY. RonholmJ. StromvikM. BakermansC. WhyteL.G. Conserved genomic and amino acid traits of cold adaptation in subzero-growing Arctic permafrost bacteria.FEMS Microbiol. Ecol.201894410.1093/femsec/fiy023 29528411
     [Google Scholar]
  59. XieB.B. BianF. ChenX.L. HeH.L. GuoJ. GaoX. ZengY.X. ChenB. ZhouB.C. ZhangY.Z. Cold adaptation of zinc metalloproteases in the thermolysin family from deep sea and arctic sea ice bacteria revealed by catalytic and structural properties and molecular dynamics: new insights into relationship between conformational flexibility and hydrogen bonding.J. Biol. Chem.2009284149257926910.1074/jbc.M808421200 19181663
     [Google Scholar]
  60. BettsM.J. Amino acid properties and consequences of substitutions.Bioinform. geneticist.2003317289
     [Google Scholar]
  61. RobertA. Histidine biosynthesis.Arabidopsis Book20119
     [Google Scholar]
  62. WangY. ZhangY. HaY. Crystal structure of a rhomboid family intramembrane protease.Nature2006444711617918010.1038/nature05255 17051161
     [Google Scholar]
  63. SherrattA.R. BlaisD.R. GhasrianiH. PezackiJ.P. GotoN.K. Activity-based protein profiling of the Escherichia coli GlpG rhomboid protein delineates the catalytic core.Biochemistry201251397794780310.1021/bi301087c 22963263
     [Google Scholar]
  64. Del RioA. DuttaK. ChavezJ. Ubarretxena-BelandiaI. GhoseR. Solution structure and dynamics of the N-terminal cytosolic domain of rhomboid intramembrane protease from Pseudomonas aeruginosa: insights into a functional role in intramembrane proteolysis.J. Mol. Biol.2007365110912210.1016/j.jmb.2006.09.047 17059825
     [Google Scholar]
  65. BrownR.S. Zinc finger proteins: getting a grip on RNA.Curr. Opin. Struct. Biol.2005151949810.1016/j.sbi.2005.01.006 15718139
     [Google Scholar]
  66. ShortK.M. CoxT.C. Subclassification of the RBCC/TRIM superfamily reveals a novel motif necessary for microtubule binding.J. Biol. Chem.2006281138970898010.1074/jbc.M512755200 16434393
     [Google Scholar]
  67. MarxJ-C. CollinsT. D’AmicoS. FellerG. GerdayC. Cold-adapted enzymes from marine Antarctic microorganisms.Mar. Biotechnol. (NY)20079329330410.1007/s10126‑006‑6103‑8 17195087
     [Google Scholar]
  68. JongrujaN. YouD.J. AngkawidjajaC. KanayaE. KogaY. KanayaS. Structure and characterization of RNase H3 from Aquifex aeolicus.FEBS J.2012279152737275310.1111/j.1742‑4658.2012.08657.x 22686566
     [Google Scholar]
  69. SiliakusM.F. van der OostJ. KengenS.W.M. Adaptations of archaeal and bacterial membranes to variations in temperature, pH and pressure.Extremophiles201721465167010.1007/s00792‑017‑0939‑x 28508135
     [Google Scholar]
  70. D’AmicoS. CollinsT. MarxJ.C. FellerG. GerdayC. GerdayC. Psychrophilic microorganisms: challenges for life.EMBO Rep.20067438538910.1038/sj.embor.7400662 16585939
     [Google Scholar]
  71. RatkowskyD.A. OlleyJ. RossT. Unifying temperature effects on the growth rate of bacteria and the stability of globular proteins.J. Theor. Biol.2005233335136210.1016/j.jtbi.2004.10.016 15652145
     [Google Scholar]
  72. TribelliP. LópezN. Reporting key features in cold-adapted bacteria.Life (Basel)201881810.3390/life8010008 29534000
     [Google Scholar]
  73. MoyerC.L. Psychrophiles and psychrotrophs. In: Encyclopedia of life sciences.Wiley2007
     [Google Scholar]
  74. XuD. ZhangJ. RoyA. ZhangY. Automated protein structure modeling in CASP9 by I‐TASSER pipeline combined with QUARK‐based ab initio folding and FG‐MD‐based structure refinement.Proteins201179S10Suppl. 1014716010.1002/prot.23111 22069036
     [Google Scholar]
  75. YangJ. ZhangY. I-TASSER server: new development for protein structure and function predictions.Nucleic Acids Res.201543W1W174W18110.1093/nar/gkv342 25883148
     [Google Scholar]
  76. IqrarU. JavaidH. AshrafN. AhmadA. LatiefN. ShahidA.A. AhmadW. IjazB. Structural and functional analysis of pullulanase type 1 (PulA) from Geobacillus thermopakistaniensis.Mol. Biotechnol.202062837037910.1007/s12033‑020‑00255‑x 32347477
     [Google Scholar]
  77. LiY. ZhangC. ZhengW. ZhouX. BellE.W. YuD.J. ZhangY. Protein inter‐residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.Proteins202189121911192110.1002/prot.26211 34382712
     [Google Scholar]
  78. SharmaP. JoshiT. JoshiT. MathpalS. MaitiP. NandM. In silico screening of natural compounds to inhibit interaction of human ACE2 receptor and spike protein of SARS-CoV-2 for the prevention of COVID-19.J. Biomol. Struct. Dyn.2021113 34854365
     [Google Scholar]
  79. RuddenL.S.P. DegiacomiM.T. Protein docking using a single representation for protein surface, electrostatics, and local dynamics.J. Chem. Theory Comput.20191595135514310.1021/acs.jctc.9b00474 31390206
     [Google Scholar]
  80. AretzJ. WamhoffE.C. HanskeJ. HeymannD. RademacherC. Computational and experimental prediction of human C-type lectin receptor druggability.Front. Immunol.2014532310.3389/fimmu.2014.00323 25071783
     [Google Scholar]
  81. LimaA.N. PhilotE.A. TrossiniG.H.G. ScottL.P.B. MaltarolloV.G. HonorioK.M. Use of machine learning approaches for novel drug discovery.Expert Opin. Drug Discov.201611322523910.1517/17460441.2016.1146250 26814169
     [Google Scholar]
  82. VolkamerA. EidS. TurkS. JaegerS. RippmannF. FulleS. Pocketome of human kinases: prioritizing the ATP binding sites of (yet) untapped protein kinases for drug discovery.J. Chem. Inf. Model.201555353854910.1021/ci500624s 25557645
     [Google Scholar]
  83. CaiW. JägerM. BullerjahnJ.T. HugelT. WolfS. BalzerB.N. Anisotropic Friction in a Ligand-Protein Complex.Nano Lett.202323104111411910.1021/acs.nanolett.2c04632 36948207
     [Google Scholar]
  84. MirzaeiH. ZarbafianS. VillarE. MottarellaS. BeglovD. VajdaS. PaschalidisI.C. VakiliP. KozakovD. Energy Minimization on Manifolds for Docking Flexible Molecules.J. Chem. Theory Comput.20151131063107610.1021/ct500155t 26478722
     [Google Scholar]
  85. CourniaZ. AllenB.K. BeumingT. PearlmanD.A. RadakB.K. ShermanW. Rigorous free energy simulations in virtual screening.J. Chem. Inf. Model.20206094153416910.1021/acs.jcim.0c00116 32539386
     [Google Scholar]
  86. DrexlerK.E. Molecular machinery and manufacturing with applications to computation.PhD thesis, Massachusetts Institute of Technology, Dept. of Architecture 1991
     [Google Scholar]
  87. HarperP.M. GaniR. KolarP. IshikawaT. Computer-aided molecular design with combined molecular modeling and group contribution.Fluid Phase Equilib.1999158-16033734710.1016/S0378‑3812(99)00089‑8
     [Google Scholar]
  88. ChandraA. GhateM.V. AithalK.S. LewisS.A. In silico prediction coupled with in vitro experiments and absorption modeling to study the inclusion complex of telmisartan with modified beta-cyclodextrin.J. Incl. Phenom. Macrocycl. Chem.2018911-2476010.1007/s10847‑018‑0797‑x
     [Google Scholar]
  89. RowaiyeA.B. OniS. UzochukwuI.C. AkpaA. EsimoneC.O. The Structure-Based Virtual Screening for Natural Compounds that Bind with the Activating Receptors of Natural Killer Cells.bioRxiv20202020.06.19.16086110.1101/2020.06.19.160861
     [Google Scholar]
  90. Swathik ClaranciaP. NaveenaraniM. ValarmathiR. SureshaG.S. HemaprabhaG. RamB. AppunuC. Isolation, characterization and expression analysis of novel water deficit stress-responsive DEEPER ROOTING 1 (DRO1) gene from drought-tolerant Erianthus arundinaceus.Journal of Sugarcane Research202010111110.37580/JSR.2020.1.10.1‑11
     [Google Scholar]
  91. TorresP.H.M. SoderoA.C.R. JofilyP. Silva-JrF.P. Key topics in molecular docking for drug design.Int. J. Mol. Sci.20192018457410.3390/ijms20184574 31540192
     [Google Scholar]
  92. SepayN. SahaP.C. ShahzadiZ. ChakrabortyA. HalderU.C. A crystallography-based investigation of weak interactions for drug design against COVID-19.Phys. Chem. Chem. Phys.202123127261727010.1039/D0CP05714B 33876086
     [Google Scholar]
  93. ZangerU.M. SchwabM. Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation.Pharmacol. Ther.2013138110314110.1016/j.pharmthera.2012.12.007 23333322
     [Google Scholar]
  94. YoonI. NamM. KimH.K. MoonH.S. KimS. JangJ. SongJ.A. JeongS.J. KimS.B. ChoS. KimY. LeeJ. YangW.S. YooH.C. KimK. KimM.S. YangA. ChoK. ParkH.S. HwangG.S. HwangK.Y. HanJ.M. KimJ.H. KimS. Glucose-dependent control of leucine metabolism by leucyl-tRNA synthetase 1.Science2020367647420521010.1126/science.aau2753 31780625
     [Google Scholar]
  95. MartínezY. LiX. LiuG. BinP. YanW. MásD. ValdiviéM. HuC.A.A. RenW. YinY. The role of methionine on metabolism, oxidative stress, and diseases.Amino Acids201749122091209810.1007/s00726‑017‑2494‑2 28929442
     [Google Scholar]
  96. KaturiJ.V.P. NagarajanK. Method for the Preparation of N ‐protected β ‐Cyano L‐alanine Tertiary Butyl Esters.ChemistrySelect2021661283128710.1002/slct.202004478
     [Google Scholar]
  97. SinghB. DayC.M. AbdellaS. GargS. Alzheimer’s disease current therapies, novel drug delivery systems and future directions for better disease management.J. Control. Release202436740242410.1016/j.jconrel.2024.01.047 38286338
     [Google Scholar]
  98. ShahB. KhuntD. MisraM. PadhH. Formulation and in-vivo pharmacokinetic consideration of intranasal microemulsion and mucoadhesive microemulsion of rivastigmine for brain targeting.Pharm. Res.2018351810.1007/s11095‑017‑2279‑z 29294189
     [Google Scholar]
  99. LipinskiC.A. Lead- and drug-like compounds: the rule-of-five revolution.Drug Discov. Today. Technol.20041433734110.1016/j.ddtec.2004.11.007 24981612
     [Google Scholar]
  100. LiuY. YangX. GanJ. ChenS. XiaoZ.X. CaoY. CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting.Nucleic Acids Res.202250W1W159W16410.1093/nar/gkac394 35609983
     [Google Scholar]
  101. ShG.B. Online molecular docking and analysis of biological activity of cyanuric acid derivative.Bioorg. Chem.2022695
     [Google Scholar]
  102. DhorajiwalaT. HalderS. SamantL. Comparative in silico molecular docking analysis of l-threonine-3-dehydrogenase, a protein target against African trypanosomiasis using selected phytochemicals.J. Appl. Biotechnol. Rep.20196310110810.29252/JABR.06.03.04
     [Google Scholar]
  103. KandasamyS. DuraisamyS. ChinnappanS. BalakrishnanS. ThangasamyS. MuthusamyG. ArumugamS. PalanisamyS. Molecular modeling and docking of protease from Bacillus sp. for the keratin degradation.Biocatal. Agric. Biotechnol.2018139510410.1016/j.bcab.2017.11.016
     [Google Scholar]
/content/journals/cp/10.2174/0115701646344004241025031355
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In Silico Analysis of Antarctic Rhomboid Proteases (Rho): Unveiling Protein Structure and Evolution
Curr. Proteomics 21, 770 (2024); https://doi.org/10.2174/0115701646344004241025031355
/content/journals/cp/10.2174/0115701646344004241025031355
/content/journals/cp/10.2174/0115701646344004241025031355
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  • Article Type:     Research Article
Keyword(s): antarctica; extremophiles; ligand; molecular docking; psychrophiles; Rhomboid proteases

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