Abstract
Cold shock proteins (CSPs) are small, cytoplasmic, ubiquitous and acidic proteins. They have a single nucleic acid-binding domain and pose as “RNA chaperones” by binding to ssRNA in a low sequence specificity and cooperative manner. They are found in a family of nine homologous CSPs in E. coli. CspA, CspB, CspG and CspI are immensely cold inducible, CspE and CspC are consistently released at usual physiological temperatures and CspD is also induced under nutrient stress. The paralogous protein pairs CSPA/CSPB, CSPC/CSPE, CSPG/CSPI and CSPF/CSPH were first identified. The eight proteins were subjected to molecular modelling and simulation to obtain the most stable conformation in correspondence to their equilibrated RMSD and RMSF graph. The results were compared and it was observed that CSPB, CSPE, CSPF and CSPI were more stable than their paralogous partner conforming to their near equilibrated RMSD curve and low fluctuating RMSF graph. The paralogous proteins were docked with ssRNA and simultaneously binding affinity, interaction types, electrostatic surface potential, hydrophobicity, conformational analysis and SASA were calculated to minutely study and understand the molecular mechanism initiated by these proteins. It was found that CSPB, CSPC, CSPH and CSPI displayed higher affinity towards ssRNA than their paralogous partner. The results further corroborated with ΔGmmgbsa and ΔGfold energy. Between the paralogous pairs CSPC, CSPH and CSPI exhibited higher binding free energy than their partner. Further, CSPB, CSPC and CSPI exhibited higher folding free energy than their paralogous pair. CSPH exhibited highest ΔGmmgbsa of − 522.2 kcal/mol and lowest was displayed by CSPG of around − 309.3 kcal/mol. Highest number of mutations were recognised in CSPF/CSPH and CSPG/CSPI pair. Difference in interaction pattern was maximum in CSPF/CSPH owing to their high number of non-synonymous substitutions. Maximum difference in surface electrostatic potential was observed in case of CSPA, CSPG and CSPF. This research work emphasizes on discerning the molecular mechanism initiated by these proteins with a structural, mutational and functional approach.
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Abbreviations
- CSP:
-
Cold shock proteins
- ssRNA:
-
Single stranded RNA
- RBM:
-
RNA binding motif
- RMSF:
-
Root mean square fluctuation
- RMSD:
-
Root mean square deviation
- SASA:
-
Solvent-accessible surface area
References
Ahmed A et al (2011) A normal mode-based geometric simulation approach for exploring biologically relevant conformational transitions in proteins. J Chem Inform Model 51:1604–1622. https://doi.org/10.1021/ci100461k
Aier I, Varadwaj PK, Raj U (2016) Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep 6:34984. https://doi.org/10.1038/srep34984
Bhattacharya S, Dhar S, Banerjee A, Ray S (2019) Structural, functional, and evolutionary analysis of late embryogenesis abundant proteins (LEA) in Triticum aestivum: a detailed molecular level biochemistry using in silico approach. Comput Biol Chem 82:9–24. https://doi.org/10.1016/j.compbiolchem.2019.06.005
Biesiada M, Purzycka KJ, Szachniuk M, Blazewicz J, Adamiak RW (2016) Automated RNA 3D structure prediction with RNAComposer. Methods Mol Biol 1490:199–215. https://doi.org/10.1007/978-1-4939-6433-8_13
Blum M et al (2021a) The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 49:D344–D354. https://doi.org/10.1093/nar/gkaa977
Blum M, Chang HY, Chuguransky S et al (2021b) The InterPro protein families and domains database: 20 years on. Nucleic Acids Res 49:D344–D354. https://doi.org/10.1093/nar/gkaa977
Bornot A, Etchebest C, de Brevern AG (2011) Predicting protein flexibility through the prediction of local structures. Proteins Struct Funct Bioinform 79:839–852. https://doi.org/10.1002/prot.22922
Bosshard HR, Marti DN, Jelesarov I (2004) Protein stabilization by salt bridges: concepts, experimental approaches and clarification of some misunderstandings. J Mol Recognit 17:1–16. https://doi.org/10.1002/jmr.657
Bowie JU, Ltcy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253:164–170. https://doi.org/10.1126/science.1853201
Choudhuri S (2014) Fundamentals of Molecular Evolution. In: Bioinformatics for beginners. Elsevier, pp 27–53. https://doi.org/10.1016/B978-0-12-410471-6.00002-5
Chris C, Todd OY (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2:1511–1519. https://doi.org/10.1002/pro.5560020916
Dagan T, Talmor Y, Graur D (2002) Ratios of radical to conservative amino acid replacement are affected by mutational and compositional factors and may not be indicative of positive Darwinian selection. Mol Biol Evol 19:1022–1025. https://doi.org/10.1093/oxfordjournals.molbev.a004161
Dhiman A, Purohit R (2022) Identification of potential mutational hotspots in serratiopeptidase to address its poor pH tolerance issue. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2022.2137699
Dhiman A, Purohit R (2023) Profiling the disintegration of BRPs released by massive wasp stings using serratiopeptidase: an in-silico insight. Comput Biol Med 159:106951. https://doi.org/10.1016/j.compbiomed.2023.106951
Dominguez C, Boelens R, Bonvin AM (2003) HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J Am Chem Soc 125:1731–1737. https://doi.org/10.1021/ja026939x
Du X, Li Y, Xia YL et al (2016) Insights into protein–ligand interactions: mechanisms, models, and methods. Int J Mol Sci 17:144. https://doi.org/10.3390/ijms17020144
Durham E, Woetzel DBN, Staritzbichler R, Meiler J (2009) Solvent accessible surface area approximations for rapid and accurate protein structure prediction. J Mol Model 15:1093–1108. https://doi.org/10.1007/s00894-009-0454-9
Esque J, Léonard S, de Brevern AG, Oguey C (2013) VLDP web server: a powerful geometric tool for analysing protein structures in their environment. Nucleic Acids Res 41:W373–W378. https://doi.org/10.1093/nar/gkt509
Fiser A, Kinh R, Do G, Ali AS (2000) Modeling of loops in protein structures. Protein Sci 9:1753–1773. https://doi.org/10.1110/ps.9.9.1753
Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL (2008) The Vienna RNA websuite. Nucleic Acids Res 36:W70–W74. https://doi.org/10.1093/nar/gkn188
Hagemans D, van Belzen IA, Morán Luengo T, Rüdiger SG (2015) A script to highlight hydrophobicity and charge on protein surfaces. Front Mol Biosci 2:56. https://doi.org/10.3389/fmolb.2015.00056
Heinig M, Frishman D (2004) STRIDE: A web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32:W500–W502. https://doi.org/10.1093/2Fnar/2Fgkh429
Hellmuth M, Wieseke N, Lechner M, Lenhof HP, Middendorf M, Stadler PF (2015) Phylogenomics with paralogs. Proc Natl Acad Sci U S A 112:2058–2063. https://doi.org/10.1073/pnas.1412770112
Horn G, Hofweber R, Kremer W, Kalbitzer HR (2007) Structure and function of bacterial cold shock proteins. Cell Mol Life Sci 64:1457–1470. https://doi.org/10.1007/s00018-007-6388-4
Jacobson DR, Saleh OA (2017) Counting the ions surrounding nucleic acids. Nucleic Acids Res 45:1596–1605. https://doi.org/10.1093/nar/gkw1305
Javadpour MM, Eilers M, Groesbeek M, Smith SO (1999) Helix packing in polytopic membrane proteins: role of glycine in transmembrane helix association. Biophys J 77:1609–1618. https://doi.org/10.1016/s0006-3495(99)77009-8
Jiang W, Hou Y, Inouye M (1997) CspA, the major cold-shock protein of Escherichia coli, is an RNA chaperone. J Biol Chem 272:196–202. https://doi.org/10.1074/jbc.272.1.196
Jiang Y, Liu HF, Liu R (2021) Systematic comparison and prediction of the effects of missense mutations on protein-DNA and protein-RNA interactions. PLoS Comput Biol 17:e1008951. https://doi.org/10.1371/journal.pcbi.1008951
Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10:845–858. https://doi.org/10.1038/nprot.2015.053
Kim H, Jeong E, Lee SW, Han K (2003) Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns. FEBS Lett 552:231–239. https://doi.org/10.1016/s0014-5793(03)00930-x
Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32:W526–W531. https://doi.org/10.1093/nar/gkh468
Klose DP, Wallace BA, Janes RW (2010) 2Struc: The secondary structure server. Bioinformatics 26:2624–2625. https://doi.org/10.1093/bioinformatics/btq480
Krüger DM, Ahmed A, Gohlke H (2012) NMSim web server: Integrated approach for normal mode-based geometric simulations of biologically relevant conformational transitions in proteins. Nucleic Acids Res 40:W310–W316. https://doi.org/10.1093/nar/gks478
Kumar S, Bhardwaj V, Singh R, Purohit R (2023) Structure restoration and aggregate inhibition of V30M mutant transthyretin protein by potential quinoline molecules. Int J Biol Macromol 231:123318. https://doi.org/10.1080/07391102.2022.2137699
Lakshminarayanan S, Jeyasingh V, Murugesan K, Selvapalam N, Dass G (2012) Molecular electrostatic potential (MEP) surface analysis of chemo sensors: an extra supporting hand for strength, selectivity & non-traditional interactions. J Photochem Photobiol 6:100022. https://doi.org/10.1016/j.jpap.2021.100022
Li SC, Goto NK, Williams KA, Deber CM, Fasman GD (1996) Alpha-helical, but not beta-sheet, propensity of proline is determined by peptide environment. Proc Natl Acad Sci USA 93:6676–6681. https://doi.org/10.1073/pnas.93.13.6676
Li Z, Guo Q, Zhang J et al (2021) The RNA-binding motif protein family in cancer: friend or foe? Front Oncol 11:757135. https://doi.org/10.3389/fonc.2021.757135
Lijnzaad P, Berendsen HJ, Argos P (1996) Hydrophobic patches on the surfaces of protein structures. Proteins 25(3):389–397. https://doi.org/10.1002/(SICI)1097-0134(199607)25:3%3c389::AID-PROT10%3e3.0.CO;2-E
Martínez L (2015) Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis. PLoS ONE 10:e0119264. https://doi.org/10.1371/journal.pone.0119264
Mukherjee S, Bahadur RP (2018) An account of solvent accessibility in protein-RNA recognition. Sci Rep 8:10546. https://doi.org/10.1038/s41598-018-28373-2
Nakaminami K, Karlson DT, Imai R (2006) Functional conservation of cold shock domains in bacteria and higher plants. PNAS 103:10122–10127. https://doi.org/10.1073/pnas.0603168103
Pantsar T, Poso A (2018) Binding affinity via docking: fact and fiction. Molecules 23:1899. https://doi.org/10.3390/molecules23081899
Phadtare S (2004) Recent developments in bacterial cold-shock response. Curr Issues Mol Biol 6:125–136. https://doi.org/10.21775/cimb.006.125
Plaxco KW, Simons KT, Baker D (1998) Contact order, transition state placement and the refolding rates of single domain proteins. J Mol Biol 277:985–994. https://doi.org/10.1006/jmbi.1998.1645
Receveur-Bréchot V, Durand D (2012) How random are intrinsically disordered proteins? A small angle scattering perspective. Curr Protein Pept Sci 13:55–75. https://doi.org/10.2174/138920312799277901
Rennella E, Sára T, Juen M et al (2017) RNA binding and chaperone activity of the E. coli cold-shock protein CspA. Nucleic Acids Res 45:4255–4268. https://doi.org/10.1093/nar/gkx044
Roman AL, Antoon J, Rullmann C, Malcolm WM, Robert K, Janet MT (1996) AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR*. J Biomol NMR 8:477–486. https://doi.org/10.1007/bf00228148
Sachs R, Max KEA, Heinemann U, Balbach J (2012) RNA single strands bind to a conserved surface of the major cold shock protein in crystals and solution. RNA 18:65–76. https://doi.org/10.1261/rna.02809212
Schindler T, Graumann PL, Perl D, Ma S, Schmid FX, Marahiel MA (1999) The family of cold shock proteins of Bacillus subtilis: stability and dynamics in vitro and in vivo. J Biol Chem 274:3407–3413. https://doi.org/10.1074/jbc.274.6.3407
Singh R, Purohit R (2023) Computational analysis of protein-ligand interaction by targeting a cell cycle restrainer. Comput Methods Progr Biomed 231:107367. https://doi.org/10.1016/j.cmpb.2023.107367
Voet D, Voet JG, Pratt CW (2016) Principles of biochemistry, 5th edn. Wiley (ISBN 978-1-118-91840-1)
Wang N, Yamanaka K (1999) CspI, the ninth member of the CspA family of Escherichia coli is induced upon cold shock. J Bacteriol 181:1603–1609. https://doi.org/10.1128/jb.181.5.1603-1609.1999
Waterhouse A, Bertoni M, Bienert S et al (2018) SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res 46:W296–W303. https://doi.org/10.1093/nar/gky427
Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410. https://doi.org/10.1093/nar/gkm290
Willard L, Ranjan A, Zhang H, Monzavi H, Boyko RF, Sykes BD, Wishart DS (2003) VADAR: a web server for quantitative evaluation of protein structure quality. Nucleic Acids Res 31:3316–3319. https://doi.org/10.1093/nar/gkg565
Xu D, Zhang Y (2011) Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J 101:2525–2534. https://doi.org/10.1016/j.bpj.2011.10.024
Yamanaka K (1999) Cold shock response in Escherichia coli. JMMB Symposium 1:193–202
Zhang J (2000) Rates of conservative and radical nonsynonymous nucleotide substitutions in mammalian nuclear genes. J Mol Evol 50:56–68. https://doi.org/10.1007/s002399910007
Zhang Y, Burkhardt DH, Rouskin S, Li GW, Weissman JS, Gross CA (2018) A stress response that monitors and regulates mRNA structure is central to cold shock adaptation. Mol Cell 70:274–286. https://doi.org/10.1016/j.molcel.2018.02.035
Zhao H, Huang D (2011) Hydrogen bonding penalty upon ligand binding. PLoS ONE 6:e19923. https://doi.org/10.1371/journal.pone.0019923
Zheng W, Zhang C, Li Y, Pearce R, Bell EW, Zhang Y (2021) Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations. Cell Rep Methods 1:100014. https://doi.org/10.1016/j.crmeth.2021.100014
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The authors would like to thank, Amity Institute of Biotechnology, Amity University, Kolkata, India, for their cooperation.
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Alankar Roy (AR) and Sujay Ray (SR) conceived of the presented idea. AR, did the calculation part. SR helped to select databases, software and web servers required for this study. Main backbone manuscript was written by AR. Tables, Figures were constructed by AR with the help of SR. Some portion of the result and discussion portion was specifically oriented by AR and SR. Overall Guidance and design were given by SR.
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Roy, A., Ray, S. An in-silico study to understand the effect of lineage diversity on cold shock response: unveiling protein-RNA interactions among paralogous CSPs of E. coli. 3 Biotech 13, 236 (2023). https://doi.org/10.1007/s13205-023-03656-2
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DOI: https://doi.org/10.1007/s13205-023-03656-2