Tools for Characterizing Proteins: Circular Variance, Mutual Proximity, Chameleon Sequences, and Subsequence Propensities.
Amino acid propensity
Chameleon sequence
Circular variance
Foldability score
Mutual proximity
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
17
3
2022
pubmed:
18
3
2022
medline:
22
3
2022
Statut:
ppublish
Résumé
For the characterization of various aspects of protein structures, four useful concepts are discussed: chameleon sequences, circular variance, mutual proximity, and a subsequence-based foldability score. These concepts were used in estimating foldability of globular, intrinsically disordered and fold-switching proteins, properties of protein-protein interfaces, quantifying sphericity, helping to improve protein-protein docking scores, and estimating the effect of mutations on stability. A conjecture about the Achilles' heel of proteins is presented as well.
Identifiants
pubmed: 35298807
doi: 10.1007/978-1-0716-1855-4_2
doi:
Substances chimiques
Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
39-61Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
Références
Dokholyan N (2009) Protein designability and engineering. In: Structural bioinformatics, 2nd edn. Wiley-Blackwell, Hoboken, NJ
Mezei M (2018) Revisiting chameleon sequences in the protein data Bank. Algorithms 11:114. https://doi.org/10.3390/a11080114
doi: 10.3390/a11080114
Porter LL, Looger LL (2018) Extant fold-switching proteins are widespread. Proc Natl Acad Sci U S A 115:5968–5973. https://doi.org/10.1073/pnas.1800168115
doi: 10.1073/pnas.1800168115
pubmed: 29784778
pmcid: 6003340
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data Bank. Nucleic Acids Res 28:235–242. https://doi.org/10.1093/nar/28.1.235
doi: 10.1093/nar/28.1.235
pubmed: 10592235
pmcid: 102472
Mezei M (2015) Statistical properties of protein-protein interfaces. Algorithms 8:92–99. https://doi.org/10.3390/a8020092
doi: 10.3390/a8020092
Piovesan D, Tabaro F, Marco IM, Quaglia NF, Oldfield CJ, Aspromonte MC, Davey NE, Davidović R, Dosztányi Z, Elofsson A, Gasparini A, Hatos A, Kajava AV, Kalmar L, Leonardi E, Lazar T, Macedo-Ribeiro S, Macossay-Castillo M, Meszaros A, Minervini G, Murvai N, Pujols J, Roche DB, Salladini E, Schad E, Schramm A, Szabo B, Tantos A, Tonello F, Tsirigos KD, Veljković N, Ventura S, Vranken W, Warholm P, Uversky VN, Dunker AK, Longhi S, Silvio P, Tosatto CE (2016) DisProt 7.0: a major update of the database of disordered proteins. Nucleic Acids Res 45:D219–D227. https://doi.org/10.1093/nar/gkw1279
doi: 10.1093/nar/gkw1279
pubmed: 27899601
pmcid: 5210544
Pucci F, Bourgeas R, Rooman M (2016) High-quality thermodynamic data on the stability changes of proteins upon single-site mutations. J Phys Chem Ref Data 45. https://doi.org/10.1063/1.4947493
Kirys T, Ruvinsky AM, Singla D, Tuzikov AV, Kundrotas PJ, Vakser IA (2015) Simulated unbound structures for benchmarking of protein docking in the DOCKGROUND resource. BMC Bioinformatics 16:243
doi: 10.1186/s12859-015-0672-3
Vreven T, Moal I, Vangone A, Pierce B, Kastritis P, Torchala M, Chaleil R, Jimenez-Garcia B, Bates P, Fernandez-Recio J, Bonvin A, Weng Z (2015) Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. J Mol Biol 427:3031–3041
doi: 10.1016/j.jmb.2015.07.016
Comeau SR, Gatchell DW, Vajda S, Camacho CJ (2004) ClusPro: a fully automated algorithm for protein-protein docking. Nucleic Acids Res 32:W96–W99. https://doi.org/10.1093/nar/gkh354
doi: 10.1093/nar/gkh354
pubmed: 15215358
pmcid: 441492
Schneidman-Duhovny D, Inbar Y, Nussinov R, Wolfson HJ (2005) PatchDock and SymmDock: servers for rigid and symmetric docking. Nucl Acids Res 33:W363–W367. https://doi.org/10.1093/nar/gki481
doi: 10.1093/nar/gki481
pubmed: 15980490
pmcid: 1160241
Mardia KV, Jupp PE (2000) Directional statistics. John Wiley & Sons, Ltd, Chichester
Mezei M (2003) A new method for mapping macromolecular topography. J Mol Graph Model 21(5):463–472
doi: 10.1016/S1093-3263(02)00203-6
Mezei M (2010) Simulaid: a simulation facilitator and analysis program. J Comput Chem 31(14):2658–2668. https://doi.org/10.1002/jcc.21551
doi: 10.1002/jcc.21551
pubmed: 20740566
Mezei M, Zhou M-M (2007) Pspace: a program to plan the covering of a protein space. Source Code Biol Med 2:6. https://doi.org/10.1186/1751-0473-2-6
doi: 10.1186/1751-0473-2-6
pubmed: 17956630
pmcid: 2231351
Mezei M (2003) Efficient Monte Carlo sampling for long molecular chains using local moves, tested on a solvated lipid bilayer. J Chem Phys 118:3874–3880. https://doi.org/10.1063/1.1539839
doi: 10.1063/1.1539839
Mezei M (1998) Chameleon sequences in the PDB. Prot Engng 11:411–414. https://doi.org/10.1093/protein/11.6.411
doi: 10.1093/protein/11.6.411
Mezei M (2020) Foldability and chameleon propensity of fold-switching protein sequences. Proteins 89:3–5. https://doi.org/10.1002/prot.25989
Göbel U, Sander C, Schneider R, Valencia A (1994) Correlated mutations and residue contacts in proteins. Proteins 18:309–317. https://doi.org/10.1002/prot.340180402
doi: 10.1002/prot.340180402
pubmed: 8208723
Mezei M (2015) Use of circular variance to quantify the deviation of a macromolecule from the spherical shape. J Math Chem 53:2184–2189. https://doi.org/10.1007/s10910-015-0540-4
doi: 10.1007/s10910-015-0540-4
pubmed: 26702193
pmcid: 4684833
Hass J, Koeh P (2014) How round is a protein? Exploring protein structured for globularity using conformal mapping. Front Mol Biosci 1:1–15. https://doi.org/10.3389/fmolb.2014.00026
doi: 10.3389/fmolb.2014.00026
Rose GD, Geselowitz AR, Lesser GJ, Lee RH, Zehfus MH (1985) Hydrophobicity of amino acid residues in globular proteins. Science 229:834–838. https://doi.org/10.1126/science.4023714
doi: 10.1126/science.4023714
pubmed: 4023714
Creighton NJDaTE (1993) Protein structure. In: Focus. IRL Press, Oxford University Press, Oxford. https://doi.org/10.1016/0307-4412(95)90200-7
doi: 10.1016/0307-4412(95)90200-7
Gaur RK (2014) Amino acid frequency distribution among eukaryotic proteins. IIOAB J 5:6–11
Mittal A, Jayaram B, Shenoy S, Bawa TS (2010) A stoichiometry driven universal spatial Organization of Backbones of folded proteins: are there Chargaff’s rules for protein folding? J Biomol Struct Dyn 28:133–142. https://doi.org/10.1080/07391102.2010.10507349
doi: 10.1080/07391102.2010.10507349
pubmed: 20645648
Mezei M (2020) On predicting foldability of a protein from its sequence. Proteins 88:355–356. https://doi.org/10.1002/prot.25811
doi: 10.1002/prot.25811
pubmed: 31479556
Mezei M (2019) Exploiting sparse statistics for a sequence-based prediction of the effect of mutations. Algorithms 12:214. https://doi.org/10.3390/a12100214
doi: 10.3390/a12100214
Kaushik R, Zhang KYJ (2020) A protein sequence fitness function for identifying natural and nonnatural proteins. Proteins 88(10):1271–1284. https://doi.org/10.1002/prot.25900
doi: 10.1002/prot.25900
pubmed: 32415863
Mittal A, Changani AM, Taparia S (2021) Unique and exclusive peptide signatures directly identify intrinsically disordered proteins from sequences without structural information. J Biomol Struct Dyn 39(8):2885–2893. https://doi.org/10.1080/07391102.2020.1756410