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The dynamic view of proteins
JID:PLREV AID:317 /DIS
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Available online at www.sciencedirect.com
Physics of Life Reviews ••• (••••) •••–••• www.elsevier.com/locate/plrev
Comment
The dynamic view of proteins Comment on “Comparing proteins to their internal dynamics: Exploring structure–function relationships beyond static structural alignments” Modesto Orozco a,b,∗ a Joint IRB-BSC Program on Computational Biology, Institute for Research in Biomedicine Barcelona, Baldiri i Reixac 10,
Barcelona 08028, Spain b Department of Biochemistry, University of Barcelona, Facultat de Biologia, Avgda Diagonal 647, Barcelona 08028, Spain
Communicated by E. Shakhnovich
The seminal works of Watson–Crick, Perutz–Kendrew and others lead more than fifty years ago to the foundational statement of structural biology: “structure is the most powerful tool to understand biology”. A large number of novel awards in chemistry and medicine since then indicates how deep this phrase has permeated into the scientific community. The reason is clear, knowledge of the structure allows chemists to understand the nature of biological macromolecules and is the required step towards the final goal of understanding the intimate nature of biological phenomena by the rules of physics. As X-ray crystallography, electron microscopy and NMR spectroscopy advanced the amount of structural data on macromolecules increased. The latest release of the Protein Data Bank (PDB); [1] contains structural data on 82 512 macromolecules (78 911 proteins, 2432 nucleic acids and 3845 complexes of proteins and nucleic acids), around 22 000 of them corresponding to homo sapiens. Every year around 7500 protein structures are deposited in PDB, but very few new structural classes are detected every year [1; http://www.rcsb.org/pdb/home/home.do], suggesting that we are not far from having a complete structural map of the proteome, at least for those proteins having a well defined three-dimensional structure. Structural biology has taught us many lessons. We have learnt that evolution has conserved better the structure than the sequence, and that proteins with just a moderate sequence identity have typically a very close structural similarity. On the other hand, we have also learnt that the structure of proteins is plastic and that the same protein can display quite different structures depending on external inputs [2,3]. Furthermore, it is now clear that even under buffered conditions structure is a dynamic concept and that proteins exist as a dynamic ensemble of conformations [2,3]. We know now that dynamics plays a key role in many fundamental processes involving proteins, such as ligand recognition, catalysis or energy transformation [2–6]. There are convincing evidence that evolution has created new proteins preserving very well the deformability of parent proteins [7], and that it has in fact used the intrinsic deformability pattern of parent proteins to create new homologs [8–10]. In summary, increasing evidence exists that dynamics is as important as DOI of original article: http://dx.doi.org/10.1016/j.plrev.2012.10.009. * Correspondence to: Joint IRB-BSC Program on Computational Biology, Institute for Research in Biomedicine Barcelona, Baldiri i Reixac 10,
Barcelona 08028, Spain. E-mail address: [email protected]. 1571-0645/$ – see front matter © 2012 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.plrev.2012.10.010
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M. Orozco / Physics of Life Reviews ••• (••••) •••–•••
structure to define protein function, and time has arrived to consider dynamic patterns as one major descriptor of proteins [7,10]. Challenge for computational biologist will be to create complex, flexible, and robust metrics able to compare deformability patterns of proteins [6,7,10–13]. We can predict a future, where metrics of flexibility similarity will be as used by structural biologist with the same frequency than root mean square deviations, and related structural metrics are used today. References [1] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res 2000;28:235–42. [2] Orozco M, Orellana L, Hospital A, Naganathan AN, Emperador A, Carrillo O, et al. Coarse grained representation of protein flexibility. Foundations, successes and shortcomings. In: Christov C, editor. Computational chemistry methods in structural biology. Advances in protein chemistry and structural biology, vol. 85. Burlington: Academic Press; 2011. p. 183–215. [3] Hensen U, Meyer T, Haas J, Rex R, Vriend G, Grubmüller H. Exploring protein dynamics space: the dynasome as the missing link between protein structure and function. PLoS ONE 2012;7(5). [4] Henzler-Wildman KA, Thai V, Lei M, Ott M, Wotf-Watz M, Fenn T, et al. Intrinsic motions along an enzymatic reaction trajectory. Nature 2007;450:838–44. [5] Stein A, Rueda M, Panjkovich A, Orozco M, Aloy P. A systematic study of the energetics involved in structural changes upon association and connectivity in protein-protein interaction networks. Structure 2011;19:881–9. [6] Hinsen K. Analysis of domain motions by approximate normal mode calculations. Proteins 1998;33:417–29. [7] Micheletti C. Comparing proteins by their internal dynamics: exploring structure–function relationships beyond static structural alignments. Phys Life Rev 2012 [in this issue]. [8] Leo-Macias A, Romero P, Lupyan D, Zerbino D, Ortiz AR. An analysis of core deformabilities in protein superfamilies. Biophys J 2005;88:1291–9. [9] Velázquez-Muriel JA, Rueda M, Isabel C, Pascual-Montano A, Orozco M, Carazo JM. Comparison of molecular dynamics and superfamily spaces of protein domain deformation. BMC Struct Biol 2009;9:6–20. [10] Gerstein M, Krebs W. A database of macromolecular motions. Nucleic Acids Res 1998;26:4280–90. [11] Hess B. Convergence of sampling in protein simulations. Phys Rev E 2002;65:031910. [12] Pérez A, Blas JR, Rueda M, López-Bes JM, de la Cruz X, Orozco M. Exploring the essential dynamics of B-DNA. J Chem Theor Comput 2005;1:790–800. [13] Orellana L, Rueda M, Ferrer-Costa C, López-Blanco JR, Chacón P, Orozco M. Approaching elastic network models to atomistic molecular dynamics. J Chem Theor Comput 2010;6:2910–23.
[m3SC+; v 1.153; Prn:9/11/2012; 14:33] P.1 (1-2)
Available online at www.sciencedirect.com
Physics of Life Reviews ••• (••••) •••–••• www.elsevier.com/locate/plrev
Comment
The dynamic view of proteins Comment on “Comparing proteins to their internal dynamics: Exploring structure–function relationships beyond static structural alignments” Modesto Orozco a,b,∗ a Joint IRB-BSC Program on Computational Biology, Institute for Research in Biomedicine Barcelona, Baldiri i Reixac 10,
Barcelona 08028, Spain b Department of Biochemistry, University of Barcelona, Facultat de Biologia, Avgda Diagonal 647, Barcelona 08028, Spain
Communicated by E. Shakhnovich
The seminal works of Watson–Crick, Perutz–Kendrew and others lead more than fifty years ago to the foundational statement of structural biology: “structure is the most powerful tool to understand biology”. A large number of novel awards in chemistry and medicine since then indicates how deep this phrase has permeated into the scientific community. The reason is clear, knowledge of the structure allows chemists to understand the nature of biological macromolecules and is the required step towards the final goal of understanding the intimate nature of biological phenomena by the rules of physics. As X-ray crystallography, electron microscopy and NMR spectroscopy advanced the amount of structural data on macromolecules increased. The latest release of the Protein Data Bank (PDB); [1] contains structural data on 82 512 macromolecules (78 911 proteins, 2432 nucleic acids and 3845 complexes of proteins and nucleic acids), around 22 000 of them corresponding to homo sapiens. Every year around 7500 protein structures are deposited in PDB, but very few new structural classes are detected every year [1; http://www.rcsb.org/pdb/home/home.do], suggesting that we are not far from having a complete structural map of the proteome, at least for those proteins having a well defined three-dimensional structure. Structural biology has taught us many lessons. We have learnt that evolution has conserved better the structure than the sequence, and that proteins with just a moderate sequence identity have typically a very close structural similarity. On the other hand, we have also learnt that the structure of proteins is plastic and that the same protein can display quite different structures depending on external inputs [2,3]. Furthermore, it is now clear that even under buffered conditions structure is a dynamic concept and that proteins exist as a dynamic ensemble of conformations [2,3]. We know now that dynamics plays a key role in many fundamental processes involving proteins, such as ligand recognition, catalysis or energy transformation [2–6]. There are convincing evidence that evolution has created new proteins preserving very well the deformability of parent proteins [7], and that it has in fact used the intrinsic deformability pattern of parent proteins to create new homologs [8–10]. In summary, increasing evidence exists that dynamics is as important as DOI of original article: http://dx.doi.org/10.1016/j.plrev.2012.10.009. * Correspondence to: Joint IRB-BSC Program on Computational Biology, Institute for Research in Biomedicine Barcelona, Baldiri i Reixac 10,
Barcelona 08028, Spain. E-mail address: [email protected]. 1571-0645/$ – see front matter © 2012 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.plrev.2012.10.010
JID:PLREV AID:317 /DIS
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[m3SC+; v 1.153; Prn:9/11/2012; 14:33] P.2 (1-2)
M. Orozco / Physics of Life Reviews ••• (••••) •••–•••
structure to define protein function, and time has arrived to consider dynamic patterns as one major descriptor of proteins [7,10]. Challenge for computational biologist will be to create complex, flexible, and robust metrics able to compare deformability patterns of proteins [6,7,10–13]. We can predict a future, where metrics of flexibility similarity will be as used by structural biologist with the same frequency than root mean square deviations, and related structural metrics are used today. References [1] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The protein data bank. Nucleic Acids Res 2000;28:235–42. [2] Orozco M, Orellana L, Hospital A, Naganathan AN, Emperador A, Carrillo O, et al. Coarse grained representation of protein flexibility. Foundations, successes and shortcomings. In: Christov C, editor. Computational chemistry methods in structural biology. Advances in protein chemistry and structural biology, vol. 85. Burlington: Academic Press; 2011. p. 183–215. [3] Hensen U, Meyer T, Haas J, Rex R, Vriend G, Grubmüller H. Exploring protein dynamics space: the dynasome as the missing link between protein structure and function. PLoS ONE 2012;7(5). [4] Henzler-Wildman KA, Thai V, Lei M, Ott M, Wotf-Watz M, Fenn T, et al. Intrinsic motions along an enzymatic reaction trajectory. Nature 2007;450:838–44. [5] Stein A, Rueda M, Panjkovich A, Orozco M, Aloy P. A systematic study of the energetics involved in structural changes upon association and connectivity in protein-protein interaction networks. Structure 2011;19:881–9. [6] Hinsen K. Analysis of domain motions by approximate normal mode calculations. Proteins 1998;33:417–29. [7] Micheletti C. Comparing proteins by their internal dynamics: exploring structure–function relationships beyond static structural alignments. Phys Life Rev 2012 [in this issue]. [8] Leo-Macias A, Romero P, Lupyan D, Zerbino D, Ortiz AR. An analysis of core deformabilities in protein superfamilies. Biophys J 2005;88:1291–9. [9] Velázquez-Muriel JA, Rueda M, Isabel C, Pascual-Montano A, Orozco M, Carazo JM. Comparison of molecular dynamics and superfamily spaces of protein domain deformation. BMC Struct Biol 2009;9:6–20. [10] Gerstein M, Krebs W. A database of macromolecular motions. Nucleic Acids Res 1998;26:4280–90. [11] Hess B. Convergence of sampling in protein simulations. Phys Rev E 2002;65:031910. [12] Pérez A, Blas JR, Rueda M, López-Bes JM, de la Cruz X, Orozco M. Exploring the essential dynamics of B-DNA. J Chem Theor Comput 2005;1:790–800. [13] Orellana L, Rueda M, Ferrer-Costa C, López-Blanco JR, Chacón P, Orozco M. Approaching elastic network models to atomistic molecular dynamics. J Chem Theor Comput 2010;6:2910–23.