Abstract
Structure calculation techniques can be very useful to bridge the gap between available sequence information and structural knowledge. In order to understand the molecular mechanisms behind diseases caused by residue exchanges, knowledge about the modified structure is needed. In this chapter, we describe how energy minimizations and molecular dynamics can be useful tools in order to study the structural effects of sequence variation. With these techniques, together with investigation of other properties, it is often possible to obtain a complete picture of the effect and mechanism behind disease-causing mutations. To take this information one step further, we also describe prediction methods that can be used to judge the effects of mutations and how to evaluate these and the interplay between the protein properties.
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Weigelt J. (2010) Structural genomics-impact on biomedicine and drug discovery, Exp Cell Res 316, 1332–1338.
Metzker M L. (2009) Sequencing technologies - the next generation, Nat Rev Genet 11, 31–46.
Durbin R M, Abecasis G R, Altshuler D L, Auton A, Brooks L D, Gibbs R A, Hurles M E, and McVean G A. (2010) A map of human genome variation from population-scale sequencing, Nature 467, 1061–1073.
Benson D A, Karsch-Mizrachi I, Lipman D J, Ostell J, and Wheeler D L. (2005) GenBank, Nucleic Acids Res 33, D34–38.
Boeckmann B, Bairoch A, Apweiler R, Blatter M C, Estreicher A, Gasteiger E, Martin M J, Michoud K, O’Donovan C, Phan I, Pilbout S, and Schneider M. (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003, Nucleic Acids Res 31, 365–370.
Dutta S, Zardecki C, Goodsell D S, and Berman H M. Promoting a structural view of biology for varied audiences: an overview of RCSB PDB resources and experiences, J Appl Crystallogr 43, 1224–1229.
Castrignano T, De Meo P D, Cozzetto D, Talamo I G, and Tramontano A. (2006) The PMDB Protein Model Database, Nucleic Acids Res 34, D306–309.
Arnold K, Bordoli L, Kopp J, and Schwede T. (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling, Bioinformatics 22, 195–201.
Kiefer F, Arnold K, Kunzli M, Bordoli L, and Schwede T. (2009) The SWISS-MODEL Repository and associated resources, Nucleic Acids Res 37, D387–392.
Pieper U, Eswar N, Webb B M, Eramian D, Kelly L, Barkan D T, Carter H, Mankoo P, Karchin R, Marti-Renom M A, Davis F P, and Sali A. (2009) MODBASE, a database of annotated comparative protein structure models and associated resources, Nucleic Acids Res 37, D347–354.
Mackey A J, Haystead T A, and Pearson W R. (2002) Getting more from less: algorithms for rapid protein identification with multiple short peptide sequences, Mol Cell Proteomics 1, 139–147.
Altschul S F, Madden T L, Schaffer A A, Zhang J, Zhang Z, Miller W, and Lipman D J. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucleic Acids Res 25, 3389–3402.
Larkin M A, Blackshields G, Brown N P, Chenna R, McGettigan P A, McWilliam H, Valentin F, Wallace I M, Wilm A, Lopez R, Thompson J D, Gibson T J, and Higgins D G. (2007) Clustal W and Clustal X version 2.0, Bioinformatics 23, 2947–2948.
Edgar R C. (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity, BMC Bioinformatics 5, 113.
Abagyan R, and Totrov M. (1994) Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins, J Mol Biol 235, 983–1002.
Abagyan R, Totrov M, and Kuznetsov D. (1994) ICM - A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation, Journal of Computational Chemistry 15, 488–506.
Pettersen E F, Goddard T D, Huang C C, Couch G S, Greenblatt D M, Meng E C, and Ferrin T E. (2004) UCSF Chimera – a visualization system for exploratory research and analysis, J Comput Chem 25, 1605–1612.
Jorgensen W L, and Tirado-Rives J. (2005) Molecular modeling of organic and biomolecular systems using BOSS and MCPRO, J Comput Chem 26, 1689–1700.
Lindahl E, Hess B, and van der Spoel D. (2001) GROMACS: A package for molecular simulation and trajectory analysis, J Mol Mod 7, 306–317.
Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark A E, and Berendsen H J. (2005) GROMACS: fast, flexible, and free, J Comput Chem 26, 1701–1718.
Gruber C C, and Pleiss J. (2011) Systematic benchmarking of large molecular dynamics simulations employing GROMACS on massive multiprocessing facilities, J Comput Chem 32, 600–606.
Case D A, Cheatham T E, 3rd, Darden T, Gohlke H, Luo R, Merz K M, Jr., Onufriev A, Simmerling C, Wang B, and Woods R J. (2005) The Amber biomolecular simulation programs, J Comput Chem 26, 1668–1688.
Brooks B R, Bruccoleri R E, Olafson B D, States D J, Swaminathan S, and Karplus M. (1982) CHARMM: A program for macromolecular energy, minimization, and dynamics calculations, Journal of Computational Chemistry 4, 187–217.
MacKerell A D, J.; Brooks B, Brooks C L, I., Nilsson L, Roux B, Won Y, and Karplus M. (1998) CHARMM: The Energy Function and Its Parameterization with an Overview of the Program., The Encyclopedia of Computational Chemistry 1, 271–277.
Anfinsen C B, Haber E, Sela M, and White F H. (1961) The kinetics of formation of native ribonuclease during oxidation of the reduced polypeptide chain., Proc Natl Acad Sci USA 47, 1309–1314.
Levinthal C. (1968) Are there pathways for protein folding?, Extrait du Journal de Chimie Physique 65, 44.
Momany F, McGuire R, Burgess A, and Scheraga H. (1975) Energy parameters in polypeptides, VII: Geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids., J. Phys. Chem. 79, 2361–2380.
Schuler L D, Daura X, and van Gunsteren W F. (2001) An improved GROMOS96 force field for aliphatic hydrocarbons in the condensed phase., Journal of Computational Chemistry 11, 1205–1218.
Westermark P. (1972) Quantitative studies on amyloid in the islets of Langerhans, Ups J Med Sci 77, 91–94.
Kruger D F, Martin C L, and Sadler C E. (2006) New insights into glucose regulation, Diabetes Educ 32, 221–228.
Paulsson J F, Andersson A, Westermark P, and Westermark G T. (2006) Intracellular amyloid-like deposits contain unprocessed pro-islet amyloid polypeptide (proIAPP) in beta cells of transgenic mice overexpressing the gene for human IAPP and transplanted human islets, Diabetologia 49, 1237–1246.
Lim D, Poole K, and Strynadka N C. (2002) Crystal structure of the MexR repressor of the mexRAB-oprM multidrug efflux operon of Pseudomonas aeruginosa, J Biol Chem 277, 29253–29259.
Dayhoff M O, Schwartz R, and Orcutt B C. (1978) A model of Evolutionary Change in Proteins, Atlas of protein sequence and structure (volume 5, supplement 3 ed.). Nat. Biomed. Res. Found., 345–358.
Henikoff S, and Henikoff J G. (1992) Amino Acid Substitution Matrices from Protein Blocks, PNAS 89, 10915–10919.
Parthiban V, Gromiha M M, and Schomburg D. (2006) CUPSAT: prediction of protein stability upon point mutations, Nucleic Acids Res 34, W239–242.
Robins T, Carlsson J, Sunnerhagen M, Wedell A, and Persson B. (2006) Molecular model of human CYP21 based on mammalian CYP2C5: structural features correlate with clinical severity of mutations causing congenital adrenal hyperplasia, Mol Endocrinol 20, 2946–2964.
Carlsson J, Soussi T, and Persson B. (2009) Investigation and prediction of the severity of p53 mutants using parameters from structural calculations, FEBS J 276, 4142–4155.
Pearson K. (1901) On Lines and Planes of Closest Fit to Systems of Points in Space, Philosophical Magazine 1901, 13.
Boser B, Guyon I, and Vapnik V. (1992) A training algorithm for optimal margin classifiers., Fifth Annual Workshop on Computational Learning Theory. ACM Press, Pittsburgh.
Kecman V. (2001) Learning and Soft Computing - Support Vector Machines, Neural Networks, Fuzzy Logic Systems, The MIT press.
Joachims T. (1999) Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, MIT Press.
Chang C-C, and Lin C-J. (2001) LIBSVM : a library for support vector machines.
Igel C, Heidrich-Meisner V, and Glasmachers T. (2008) Shark, Journal of Machine Learning Research 9, 993–996.
Breiman L, Friedman J, Olshen R, and Stone C. (1984) Classification and Regression Trees, Wadsworth.
Breiman L. (2001) Random forests, Random forests 45, 28–32.
Yue P, Melamud E, and Moult J. (2006) SNPs3D: candidate gene and SNP selection for association studies, BMC Bioinformatics 7, 166.
Calabrese R, Capriotti E, Fariselli P, Martelli P L, and Casadio R. (2009) Functional annotations improve the predictive score of human disease-related mutations in proteins, Hum Mutat 30, 1237–1244.
Ng P C, and Henikoff S. (2002) Accounting for human polymorphisms predicted to affect protein function, Genome Res 12, 436–446.
Thomas P D, Campbell M J, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, and Narechania A. (2003) PANTHER: a library of protein families and subfamilies indexed by function, Genome Res 13, 2129–2141.
Thomas P D, Kejariwal A, Guo N, Mi H, Campbell M J, Muruganujan A, and Lazareva-Ulitsky B. (2006) Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring tools, Nucleic Acids Res 34, W645–650.
Ramensky V, Bork P, and Sunyaev S. (2002) Human non-synonymous SNPs: server and survey, Nucleic Acids Res 30, 3894–3900.
Sunyaev S, Ramensky V, and Bork P. (2000) Towards a structural basis of human non-synonymous single nucleotide polymorphisms, Trends Genet 16, 198–200.
Sunyaev S, Ramensky V, Koch I, Lathe W, 3rd, Kondrashov A S, and Bork P. (2001) Prediction of deleterious human alleles, Hum Mol Genet 10, 591–597.
Matthews B W. (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme, Biochim Biophys Acta 405, 442–451.
Rodgers J L, and Nicewander W A. (1988) Thirteen ways to look at the correlation coefficient, The American Statistician 42, 59–66.
Tibshirani R. (1996) Regression shrinkage and selection via the lasso, J. Royal. Statist. Soc B. 58, 267–288.
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Carlsson, J., Persson, B. (2011). Investigating Protein Variants Using Structural Calculation Techniques. In: Orry, A., Abagyan, R. (eds) Homology Modeling. Methods in Molecular Biology, vol 857. Humana Press. https://doi.org/10.1007/978-1-61779-588-6_14
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DOI: https://doi.org/10.1007/978-1-61779-588-6_14
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