Sequence Versus Composition: What Prescribes IDP Biophysical Properties?
Status PubMed-not-MEDLINE Language English Country Switzerland Media electronic
Document type Journal Article
Grant support
17-10438Y
Grantová Agentura České Republiky
CESNET LM2015042
National Grid
LM2015047
ELIXIR CZ
CZ.02.1.01/0.0/0.0/16_019/0000729
Ministerstvo Školství, Mládeže a Tělovýchovy
PubMed
33267368
PubMed Central
PMC7515148
DOI
10.3390/e21070654
PII: e21070654
Knihovny.cz E-resources
- Keywords
- IDP, IDR, aggregation propensity, secondary structure prediction, sequence randomization,
- Publication type
- Journal Article MeSH
Intrinsically disordered proteins (IDPs) represent a distinct class of proteins and are distinguished from globular proteins by conformational plasticity, high evolvability and a broad functional repertoire. Some of their properties are reminiscent of early proteins, but their abundance in eukaryotes, functional properties and compositional bias suggest that IDPs appeared at later evolutionary stages. The spectrum of IDP properties and their determinants are still not well defined. This study compares rudimentary physicochemical properties of IDPs and globular proteins using bioinformatic analysis on the level of their native sequences and random sequence permutations, addressing the contributions of composition versus sequence as determinants of the properties. IDPs have, on average, lower predicted secondary structure contents and aggregation propensities and biased amino acid compositions. However, our study shows that IDPs exhibit a broad range of these properties. Induced fold IDPs exhibit very similar compositions and secondary structure/aggregation propensities to globular proteins, and can be distinguished from unfoldable IDPs based on analysis of these sequence properties. While amino acid composition seems to be a major determinant of aggregation and secondary structure propensities, sequence randomization does not result in dramatic changes to these properties, but for both IDPs and globular proteins seems to fine-tune the tradeoff between folding and aggregation.
See more in PubMed
Babu M.M. The contribution of intrinsically disordered regions to protein function, cellular complexity, and human disease. Biochem. Soc. Trans. 2016;44:1185–1200. doi: 10.1042/BST20160172. PubMed DOI PMC
Van Der Lee R., Buljan M., Lang B., Weatheritt R.J., Daughdrill G.W., Dunker A.K., Fuxreiter M., Gough J., Gsponer J., Jones D.T., et al. Classification of intrinsically disordered regions and proteins. Chem. Rev. 2014;114:6589–6631. doi: 10.1021/cr400525m. PubMed DOI PMC
Theillet F.X., Kalmar L., Tompa P., Han K.H., Selenko P., Dunker A.K., Daughdrill G.W., Uversky V.N. The alphabet of intrinsic disorder: I. Act like a Pro: On the abundance and roles of proline residues in intrinsically disordered proteins. Intrinsically Disord. Proteins. 2013;1:e24360. doi: 10.4161/idp.24360. PubMed DOI PMC
Romero P., Obradovic Z., Li X., Garner E.C., Brown C.J., Dunker A.K. Sequence complexity of disordered protein. Proteins Struct. Funct. Bioinform. 2001;42:38–48. doi: 10.1002/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3. PubMed DOI
Uversky V.N. Paradoxes and wonders of intrinsic disorder: Complexity of simplicity. Intrinsically Disord. Proteins. 2016;4:e1135015. doi: 10.1080/21690707.2015.1135015. PubMed DOI PMC
Piovesan D., Tabaro F., Mičetić I., Necci M., Quaglia F., Oldfield C.J., Aspromonte M.C., Davey N.E., Davidović R., Dosztányi Z., et al. DisProt 7.0: a major update of the database of disordered proteins. Nucleic Acids Res. 2016;45:D219–D227. doi: 10.1093/nar/gkw1056. PubMed DOI PMC
Heffernan R., Dehzangi A., Lyons J., Paliwal K., Sharma A., Wang J., Sattar A., Zhou Y., Yang Y. Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins. Bioinformatics. 2015;32:843–849. doi: 10.1093/bioinformatics/btv665. PubMed DOI
Jones D.T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 1999;292:195–202. doi: 10.1006/jmbi.1999.3091. PubMed DOI
Frishman D., Argos P. Seventy-five percent accuracy in protein secondary structure prediction. Proteins Struct. Funct. Bioinform. 1997;27:329–335. doi: 10.1002/(SICI)1097-0134(199703)27:3<329::AID-PROT1>3.0.CO;2-8. PubMed DOI
Cuff J.A., Barton G.J. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins Struct. Funct. Bioinform. 1999;34:508–519. doi: 10.1002/(SICI)1097-0134(19990301)34:4<508::AID-PROT10>3.0.CO;2-4. PubMed DOI
Levine J.M., Pascarella S., Argos P., Garnier J. Quantification of secondary structure prediction improvement using multiple alignments. Prot. Eng. 1993;6:849–854. doi: 10.1093/protein/6.8.849. PubMed DOI
Garnier J. GOR secondary structure prediction method version IV. Meth. Enzym. 1998;266:540–553. PubMed
Fang Y., Gao S., Tai D., Middaugh C.R., Fang J. Identification of properties important to protein aggregation using feature selection. Bmc Bioinform. 2013;14:314. doi: 10.1186/1471-2105-14-314. PubMed DOI PMC
Necci M., Piovesan D., Dosztányi Z., Tosatto S.C. MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins. Bioinformatics. 2017;33:1402–1404. doi: 10.1093/bioinformatics/btx015. PubMed DOI
Hunter J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 2007;9:90–95. doi: 10.1109/MCSE.2007.55. DOI
Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 2011;12:2825–2830.
Naranjo Y., Pons M., Konrat R. Meta-structure correlation in protein space unveils different selection rules for folded and intrinsically disordered proteins. Mol. Biosyst. 2012;8:411–416. doi: 10.1039/C1MB05367A. PubMed DOI
Linding R., Schymkowitz J., Rousseau F., Diella F., Serrano L. A comparative study of the relationship between protein structure and β-aggregation in globular and intrinsically disordered proteins. J. Mol. Biol. 2004;342:345–353. doi: 10.1016/j.jmb.2004.06.088. PubMed DOI
Uversky V.N. The alphabet of intrinsic disorder: II. Various roles of glutamic acid in ordered and intrinsically disordered proteins. Intrinsically Disord. Proteins. 2013;1:e24684. doi: 10.4161/idp.24684. PubMed DOI PMC
Vucetic S., Brown C.J., Dunker A.K., Obradovic Z. Flavors of protein disorder. Proteins Struct. Funct. Bioinform. 2003;52:573–584. doi: 10.1002/prot.10437. PubMed DOI
Mao A.H., Lyle N., Pappu R.V. Describing sequence–ensemble relationships for intrinsically disordered proteins. Biochem. J. 2013;449:307–318. doi: 10.1042/BJ20121346. PubMed DOI PMC
Das R.K., Ruff K.M., Pappu R.V. Relating sequence encoded information to form and function of intrinsically disordered proteins. Curr. Opin. Struct. Biol. 2015;32:102–112. doi: 10.1016/j.sbi.2015.03.008. PubMed DOI PMC
Bastolla U., Moya A., Viguera E., van Ham R.C. Genomic determinants of protein folding thermodynamics in prokaryotic organisms. J. Mol. Biol. 2004;343:1451–1466. doi: 10.1016/j.jmb.2004.08.086. PubMed DOI
Monsellier E., Ramazzotti M., De Laureto P.P., Tartaglia G.G., Taddei N., Fontana A., Vendruscolo M., Chiti F. The distribution of residues in a polypeptide sequence is a determinant of aggregation optimized by evolution. Biophys. J. 2007;93:4382–4391. doi: 10.1529/biophysj.107.111336. PubMed DOI PMC
English L.R., Tischer A., Demeler A.K., Demeler B., Whitten S.T. Sequence Reversal Prevents Chain Collapse and Yields Heat-Sensitive Intrinsic Disorder. Biophys. J. 2018;115:328–340. doi: 10.1016/j.bpj.2018.06.006. PubMed DOI PMC
Tretyachenko V., Vymětal J., Bednárová L., Kopecký V., Hofbauerová K., Jindrová H., Hubálek M., Souček R., Konvalinka J., Vondrášek J., et al. Random protein sequences can form defined secondary structures and are well-tolerated in vivo. Sci. Rep. 2017;7:15449. doi: 10.1038/s41598-017-15635-8. PubMed DOI PMC
Ángyán A.F., Perczel A., Gáspári Z. Estimating intrinsic structural preferences of de novo emerging random-sequence proteins: Is aggregation the main bottleneck? Febs Lett. 2012;586:2468–2472. doi: 10.1016/j.febslet.2012.06.007. PubMed DOI
Moesa H.A., Wakabayashi S., Nakai K., Patil A. Chemical composition is maintained in poorly conserved intrinsically disordered regions and suggests a means for their classification. Mol. Biosyst. 2012;8:3262–3273. doi: 10.1039/c2mb25202c. PubMed DOI
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