Abstract
Proteins are located in the twilight zone between chemistry and biology, where a peculiar kind of complexity starts. Proteins are the smallest ‘devices’ showing a sensible adaptation to their environment by the production of appropriate behavior when facing a specific stimulus. This fact qualifies (from the ‘effector’ side) proteins as nanomachines working as catalysts, motors, or switches. However (from the sensor side), the need to single out the ‘specific stimulus’ out of thermal noise qualifies proteins as information processing devices. Allostery corresponds to the modification of the configuration (in a broad sense) of the protein molecule in response to a specific stimulus in a non-strictly local way, thereby connecting the sensor and effector sides of the nanomachine. This is why the ‘disclosing’ of allostery phenomenon is at the very heart of protein function; in this chapter, we will demonstrate how a network-based representation of protein structure in terms of nodes (aminoacid residues) and edges (effective contacts between residues) is the natural language for getting rid of allosteric phenomena and, more in general, of protein structure/function relationships.
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References
Di Paola L, Giuliani A (2015) Protein contact network topology: a natural language for allostery. Curr Opin Struct Biol 31:43–48. https://doi.org/10.1016/j.sbi.2015.03.001
Tsai CJ, del Sol A, Nussinov R (2009) Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms. Mol BioSyst 5:207–216
De Ruvo M, Giuliani A, Paci P et al (2012) Shedding light on protein-ligand binding by graph theory: the topological nature of allostery. Biophys Chem 165–166:21–29. https://doi.org/10.1016/j.bpc.2012.03.001
Giuliani A, Filippi S, Bertolaso M (2014) Why network approach can promote a new way of thinking in biology. Front Genet. https://doi.org/10.3389/fgene.2014.00083
Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440–442
Di Paola L, De Ruvo M, Paci P et al (2013) Protein contact networks: an emerging paradigm in chemistry. Chem Rev 113:1598–1613. https://doi.org/10.1021/cr3002356
Berman H, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28:235–242
Liu T, Lin Y, Wen X et al (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35:198–201
Wang R, Fang X, Lu Y, Wang S (2004) The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem 47:2977–2980
Yang J, Roy A, Zhang Y (2013) BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions. Nucleic Acids Res 41:1096–1103
De Ruvo M, Di Paola L, Giuliani A et al (2012) Shedding light on protein–ligand binding by graph theory: the topological nature of allostery. Biophys Chem 165–166:21–29. https://doi.org/10.1016/j.bpc.2012.03.001
Deo N, Pang C (1984) Shortest-path algorithms: taxonomy and annotation. Networks 14:275–323. https://doi.org/10.1002/net.3230140208
Johnson DB (1977) Efficient algorithms for shortest paths in sparse networks. J ACM 24:1–13. https://doi.org/10.1145/321992.321993
Hu G, Di Paola L, Liang Z, Giuliani A (2017) Comparative study of elastic network model and protein contact network for protein complexes: the hemoglobin case. Biomed Res Int 2017:2483264
Santoni D, Paci P, Paola LD, Giuliani A (2016) Are proteins just coiled cords? Local and global analysis of contact maps reveals the backbone-dependent nature of proteins. Curr Protein Pept Sci 17:26–29
Borgatti SP, Everett MG (2006) A graph-theoretic perspective on centrality. Soc Networks 28:466–484. https://doi.org/10.1016/j.socnet.2005.11.005
Sabidussi G (1966) The centrality index of a graph. Psychometrika 31:581–603. https://doi.org/10.1007/BF02289527
Amitai G, Shemesh A, Sitbon E et al (2004) Network analysis of protein structures identifies functional residues. J Mol Biol 344:1135–1146. https://doi.org/10.1016/j.jmb.2004.10.055
Bonchev DD, Rouvray DH (1990) Chemical graph theory: introduction and fundamentals. Gordon & Breach Science Publishers, London
Cimini S, Di Paola L, Giuliani A et al (2016) GH32 family activity: a topological approach through protein contact networks. Plant Mol Biol:1–10
Di Paola L, Mei G, Di Venere A, Giuliani A (2016) Exploring the stability of dimers through protein structure topology. Curr Protein Pept Sci 17:30–36. https://doi.org/10.2174/1389203716666150923104054
del Sol A, Araúzo-Bravo MJ, Amoros D, Nussinov R (2007) Modular architecture of protein structures and allosteric communications: potential implications for signaling proteins and regulatory linkages. Genome Biol 8:R92
Tasdighian S, Di Paola L, De Ruvo M et al (2013) Modules identification in protein structures: the topological and geometrical solutions. J Chem Inf Model 54:159–168
Cumbo F, Paci P, Santoni D et al (2014) GIANT: a cytoscape plugin for modular networks. PLoS One 9:e105001. https://doi.org/10.1371/journal.pone.0105001
Tasdighian S, Di Paola L, De Ruvo M et al (2014) Modules identification in protein structures: the topological and geometrical solutions. J Chem Inf Model 54:159–168. https://doi.org/10.1021/ci400218v
Tsai CJ, del Sol A, Nussinov R (2008) Allostery: absence of a change in shape does not imply that allostery is not at play. J Mol Biol 378:1–11
Nussinov R, Tsai C-J (2012) The different ways through which specificity works in orthosteric and allosteric drugs. Curr Pharm Des 18:1311–1316. https://doi.org/10.2174/138920012799362855
Csermely P, Nussinov R, Szilágyi A (2013) From allosteric drugs to allo-network drugs: state of the art and trends of design, synthesis and computational methods. Curr Top Med Chem 13:2–4. https://doi.org/10.2174/1568026611313010002
Di Paola L, Platania CBM, Oliva G et al (2015) Characterization of protein–protein interfaces through a protein contact network approach. Front Bioeng Biotechnol 3:170. https://doi.org/10.3389/fbioe.2015.00170
Di Paola L, De Ruvo M, Paci P et al (2012) Protein contact networks: an emerging paradigm in chemistry. Chem Rev 113:1598–1613
Viloria JS, Allega MF, Lambrughi M, Papaleo E (2017) An optimal distance cutoff for contact-based Protein Structure Networks using side chain center of masses. Sci Rep 7:2838. https://doi.org/10.1038/s41598-017-01498-6
Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25:163–177. https://doi.org/10.1080/0022250X.2001.9990249
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Di Paola, L., Mei, G., Di Venere, A., Giuliani, A. (2021). Disclosing Allostery Through Protein Contact Networks. In: Di Paola, L., Giuliani, A. (eds) Allostery. Methods in Molecular Biology, vol 2253. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1154-8_2
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DOI: https://doi.org/10.1007/978-1-0716-1154-8_2
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