Structural, functional, phylogenetic, and molecular dynamic simulation study of PEST-containing nuclear protein: An e-science view
PEST-containing nuclear protein (PCNP) is a short-lived novel nuclear protein. It has been well evaluated that PCNP mediates the progression of several cancers, but the exact mechanisms are still under investigation. In this study, we provided an e-science view of PCNP protein from the aspects of protein structure, interactions, and bioinformatics-based analysis related to evolutionary features as well as proteomic profile. The phylogenetic relationship results reveal that PCNP is closely related to Pan troglodytes and the Bovidae family, while being distantly related to the Muridae family. The analysis of the physicochemical properties of PCNP demonstrated that it is a thermolabile protein which is slightly acidic and hydrophilic in nature. Further, coexpression and protein-protein interaction analyses were carried out, which demonstrated that the PCNP gene was remarkably expressed with MORF4LI and RSL24D1 genes and has close interactions with TRAM1, PSMC6, SRP9, PRKRIR, UHRF2, and BMI1 proteins. Gene ontology and pathway enrichment analyses showed that PCNP has a high tendency to work in cell cycle regulation. Moreover, among the four 3D structure generating tools, I-TASSER-generated structure had the highest quality factor score. The validation analysis revealed that the I-TASSER-generated structure exhibited the best quality factor score with maximum amino acids in the favored region. In addition, molecular dynamic simulation analysis approved the stable structure of the PCNP. This is the first study that highlights the usefulness of the understanding of the structural and functional analysis of the PCNP, which lays the groundwork for further experimental studies to validate the outcome.
Sigal A, Milo R, Cohen A, et al., 2006, Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. Nat Methods, 3(7): 525–531. https://doi.org/10.1038/nmeth892
Afzal A, Sarfraz M, Li GL, et al., 2019, Taking a holistic view of PEST-containing nuclear protein (PCNP) in cancer biology. Cancer Med, 8(14): 6335–6343. https://doi.org/10.1002/cam4.2465
Chevaillier P, 1993, Pest sequences in nuclear proteins. Int J Biochem, 25(4): 479–482.
Sarfraz M, Afzal A, Khattak S, et al., 2021, Multifaceted behavior of PEST sequence enriched nuclear proteins in cancer biology and role in gene therapy. J Cell Physiol, 236(3): 1658–1676. https://doi.org/10.1002/jcp.30011
Yan J, Wang J, Zhang H, 2002, An ankyrin repeat-containing protein plays a role in both disease resistance and antioxidation metabolism. Plant J, 29(2): 193–202. https://doi.org/10.1046/j.0960-7412.2001.01205.x
Sekhar KR, Freeman M, 1998, PEST sequences in proteins involved in cyclic nucleotide signalling pathways. J Recept Signal Transduct Res, 18(2-3): 113–132. https://doi.org/10.3109/10799899809047740
Rechsteiner M, Rogers S, 1996, PEST sequences and regulation by proteolysis. Trends Biochem Sci, 21(7): 267–271. https://doi.org/10.1016/s0968-0004(96)10031-1
Salazar-Retana AL, Maruri-López I, Hernández-Sánchez IE, et al., 2019, PEST sequences from a cactus dehydrin regulate its proteolytic degradation. PeerJ, 7: e6810. https://doi.org/10.7717/peerj.6810
Mori T, Li Y, Hata H, et al., 2002, NIRF, a novel RING finger protein, is involved in cell-cycle regulation. Biochem Biophys Res Commun, 296(3): 530–536. https://doi.org/10.1016/s0006-291x(02)00890-2
Wang DY, Hong Y, Chen YG, et al., 2019, PEST-containing nuclear protein regulates cell proliferation, migration, and invasion in lung adenocarcinoma. Oncogenesis, 8(3): 1–14.
Wu DD, Gao YR, Li T, et al., 2018, PEST-containing nuclear protein mediates the proliferation, migration, and invasion of human neuroblastoma cells through MAPK and PI3K/AKT/mTOR signaling pathways. BMC Cancer, 18(1): 1–15. https://doi.org/10.1186/s12885-018-4391-9
Dong P, Fu H, Chen L, et al., 2020, PCNP promotes ovarian cancer progression by accelerating β-catenin nuclear accumulation and triggering EMT transition. J Cell Mol Med, 24(14): 8221–8235. https://doi.org/10.1111/jcmm.15491
PDB Consortium, 2019, Protein data bank: The single global archive for 3D macromolecular structure data. J Nucleic Acids Res, 47(D1): D520–D528.
Wang J, Lisanza S, Juergens D, et al., 2021, Deep learning methods for designing proteins scaffolding functional sites. bioRxiv, 2021: 468128. https://doi.org/10.1101/2021.11.10.468128
Jumper J, Evans R, Pritzel A, et al., 2021, Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873): 583–589.
Roy A, Kucukural A, Zhang Y, 2010, I-TASSER: A unified platform for automated protein structure and function prediction. Nat Protoc, 5(4): 725–738. https://doi.org/10.1038/nprot.2010.5
Kim DE, Chivian D, Baker D, 2004, Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res, 32(Suppl 2): W526–W531. https://doi.org/10.1093/nar/gkh468
Clementi C, 2021, Fast track to structural biology. Nat Chem, 13(11): 1032–1034. https://doi.org/10.1038/s41557-021-00814-y
Pennisi E, 2021, Protein structure prediction now easier, faster. Science, 373(6552): 262–263. https://doi.org/10.1126/science.373.6552.262
Yang Z, Lasker K, Schneidman-Duhovny D, et al., 2012, UCSF chimera, MODELLER, and IMP: An integrated modeling system. J Struct Biol, 179(3): 269–278. https://doi.org/10.1016/j.jsb.2011.09.006
Lengths M, Angles M, 2018, Limitations of structure evaluation tools errat. Quick Guidel Comput Drug Des, 16: 75.
Laskowski R, MacArthur M, Thornton J, 2006, PROCHECK: Validation of Protein-Structure Coordinates. Hoboken, New Jersey: Wiley.
Benkert P, Künzli M, Schwede T, 2009, QMEAN server for protein model quality estimation. Nucleic Acids Res, 37(Suppl 2): W510–W514. https://doi.org/10.1093/nar/gkp322
Kutzner C, Páll S, Fechner M, et al., 2019, More bang for your buck: Improved use of GPU nodes for GROMACS 2018. J Comput Chem, 40(27): 2418–2431. https://doi.org/10.1002/jcc.26011
Hollingsworth SA, Dror R, 2018, Molecular dynamics simulation for all. Neuron, 99(6): 1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011
ProtParam E, 2017, ExPASy-ProtParam Tool. ProtParam E. https://doi.org/10.7717/peerj.10143/table-2
Gasteiger E, Hoogland C, Gattiker A, et al., 2005, Protein Identification and Analysis Tools on the ExPASy Server. Berlin: Springer. p571-607. https://doi.org/10.1385/1-59259-890-0:571
Suhaibun, S.R., Elengoe A, Poddar R, 2020, Technology advance in drug design using computational biology tool. Malays J Med Health Sci, 16(110): 2636–9346.
Geourjon C, Deleage G, 1995, SOPMA: Significant improvement in protein secondary structure prediction by c prediction from alignments and joint prediction. Comput Appl Biosci, 11(6): 681–684. https://doi.org/10.1093/bioinformatics/11.6.681
Singh N, Upadhyay S, Jaiswar A, et al., 2016, In silico analysis of protein. JSM Bioinform Genom Proteom, 1(2): 1007.
Warde-Farley D, Donaldson SL, Comes O, et al., 2010, The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res, 38(Suppl 2): W214–W220. https://doi.org/10.1093/nar/gkq537
Szklarczyk D, Gable AL, Nastou KC, et al., 2021, The STRING database in 2021: customizable protein protein networks, and functional characterization of user-uploaded gene/ measurement sets. Nucleic Acids Res, 49(D1): D605–D612. https://doi.org/10.1093/nar/gkaa1074
Shannon P, Markiel A, Ozier O, et al., 2003, Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res, 13(11): 2498–2504. https://doi.org/10.1101/gr.1239303
Zhou G, Soufan O, Ewald J, et al., 2019, NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res, 47(W1): W234–W241. https://doi.org/10.1093/nar/gkz240
Kuleshov MV, Jones MR, Rouillard AD, et al., 2016, Enrichr: A comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res, 44(W1): W90–W97. https://doi.org/10.1093/nar/gkw377
Chan JN, Nislow C, Emili A, 2010, Recent advances and method development for drug target identification. Trends Pharmacol Sci, 31(2): 82–88. https://doi.org/10.1016/j.tips.2009.11.002
Huang DW, Sherman BT, Lempicki R, 2009, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4(1): 44–57. https://doi.org/10.1038/nprot.2008.211
Kumar S, Stecher G, Li M, et al., 2018, MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol, 35(6): 1547–1549. https://doi.org/10.1093/molbev/msy096
Muhire BM, Varsani A, Martin D, 2014, SDT: A virus classification tool based on pairwise sequence alignment and identity calculation. PLoS One, 9(9): e108277. https://doi.org/10.1371/journal.pone.0108277
Gronau I, Moran S, 2007, Optimal implementations of UPGMA and other common clustering algorithms. Inform Process Lett, 104(6): 205–210. https://doi.org/10.1016/j.ipl.2007.07.002
Hua GJ, Hung CL, Lin CY, et al., 2017, MGUPGMA: A fast UPGMA algorithm with multiple graphics processing units using NCCL. Evol Bioinform Online, 13: 1176934317734220. https://doi.org/10.1177/1176934317734220
Ugwu SO, Apte S, 2004, The effect of buffers on protein conformational stability. Pharm Technol, 28(3): 86–109.
Ikai A, 1980, Thermostability and aliphatic index of globular proteins. J Biochem, 88(6): 1895–1898.
Yochum GS, Ayer DE, 2002, Role for the mortality factors MORF4, MRGX, and MRG15 in transcriptional repression via associations with Pf1, mSin3A, and transducin-like enhancer of split. Mol Cell Biol, 22(22): 7868–7876.https://doi.org/10.1128/mcb.22.22.7868-7876.2002
Durand S, Bruelle M, Bourdelais F, et al., 2021, RSL24D1 sustains steady-state ribosome biogenesis and pluripotency translational programs in embryonic stem cells. bioRxiv, 2021: 443845. https://doi.org/10.1101/2021.05.27.443845
Doublié S, Kapp U, Aberg A, et al., 1996, Crystallization and preliminary X-ray analysis of the 9 kDa protein of the mouse signal recognition particle and the selenomethionyl-SRP9. FEBS Lett, 384(3): 219–221. https://doi.org/10.1016/0014-5793(96)00316-x
Zhang Y, Cao X, Li P, et al., 2020, PSMC6 promotes osteoblast apoptosis through inhibiting PI3K/AKT signaling pathway activation in ovariectomy induced osteoporosis mouse model. J Cell Physiol, 235(7-8): 5511–5524. https://doi.org/10.1002/jcp.29261
Xu CR, Lee S, Ho C, et al., 2009, Bmi1 functions as an oncogene independent of Ink4A/Arf repression in hepatic carcinogenesis. Mol Cancer Res, 7(12): 1937–1945. https://doi.org/10.1158/1541-7786.mcr-09-0333
Khan NH, Chen HJ, Fan Y, et al., 2022, Biology of PEST-containing nuclear protein: A potential molecular target for cancer research. Front Oncol, 12: 784597.
Makarava N, Chang JC, Molesworth K, et al., 2020, Posttranslational modifications define course of prion strain adaptation and disease phenotype. J Clin Investig, 130: 4382–4395.