Protein modeling

Emilio Xavier Esposito, Dror Tobi, and Jeffry D. Madura  

Remews in Computational Chemistry, Volume 22 edited by Kenny B. Lipkowitz, Thomas R. Cundari, and Valerie J. Gillet Copyri t 2006 Wiley-VCH, John Wiley 8c Sons, Inc. 

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The first term represents the forces due to the electrostatic field, the second describes forces that occur at the boundary between solute and solvent regime due to the change of dielectric constant, and the third term describes ionic forces due to the tendency of the ions in solution to move into regions of lower dielectric. Applications of the so-called PBSD method on small model systems and for the interaction of a stretch of DNA with a protein model have been discussed recently ([Elcock et al. 1997]). This simulation technique guarantees equilibrated solvent at each state of the simulation and may therefore avoid some of the problems mentioned in the previous section. Due to the smaller number of particles, the method may also speed up simulations potentially. Still, to be able to simulate long time scale protein motion, the method might ideally be combined with non-equilibrium techniques to enforce conformational transitions. 

Abstract. Molecular dynamics (MD) simulations of proteins provide descriptions of atomic motions, which allow to relate observable properties of proteins to microscopic processes. Unfortunately, such MD simulations require an enormous amount of computer time and, therefore, are limited to time scales of nanoseconds. We describe first a fast multiple time step structure adapted multipole method (FA-MUSAMM) to speed up the evaluation of the computationally most demanding Coulomb interactions in solvated protein models, secondly an application of this method aiming at a microscopic understanding of single molecule atomic force microscopy experiments, and, thirdly, a new method to predict slow conformational motions at microsecond time scales. 

H. Grubmueller and P. Tavan. Molecular dynamics of conformational substates for a simplified protein model. J. Chem. Phys. 101 (1994)... 

Brooks, B. R., Janezic, D., Karplus, M. Harmonic Analysis of Large Systems I. Methodology. J. Comput. Chem. 16 (1995) 1522-1542 Janezic, D., Brooks, B. R. Harmonic Analysis of Large Systems II. Comparison of Different Protein Models. J. Comput. Chem. 16 (1995) 1543-1553 Janezic, D., Venable, R. M., Brooks, B. R. Harmonic Analysis of Large Systems. HI. Comparison with Molecular Dynamics. J. Comput. Chem. 16 (1995) 1554-1566... 

Jones T A and S Thirup 1986. Using Known Substructures in Protein Model Building and Crystallography. EMBO journal 5 819-822. 

Mian, K Sjolander and D Haussler 1994. Hidden Markov Models in Computational Biology. Applications to Protein Modelling. Journal of Molecular Biology 235 1501-1531). 

The utility of a protein model depends upon the use to which it is put. In some cases, on< only interested in the general fold that the protein adopts and so a relatively low-resoluti structure is acceptable. For other applications, such as drug design, the model must be me more accurate, including the loops and side chains. In such cases, a poor model may often fa r worse than no model at all, as it can be seriously misleading. 

Liithy R, J U Bowie and D Eisenberg 1992. Assessment of Protein Models with Three-Dimensional Profiles. Nature 356 83-85. 

Sali A and T L Blundell 1993. Comparative Protein Modelling by Satisfaction of Spatial Restraii journal of Molecular Biology 234 779-815. 

MS Johnson, N Srimvasan, R Sowdhamini, TL Blundell. Knowledge-based protein modelling. CRC Crit Rev Biochem Mol Biol 29 1-68, 1994. 

A Sail, TL Blundell. Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234 779-815, 1993. 

A Krogh, M Brown, IS Mian, K Sjolander, D Haussler. Hidden Markov models m computational biology Applications to protein modeling. I Mol Biol 235 1501-1531, 1994. 

TH Jones, S Thinip. Using known substructures in protein model building and crystallography. EMBO J 5 819-822, 1986. 

A Sail, JP Overmgton. Derivation of rules for comparative protein modeling from a database of protein stiaicture alignments. Protein Sci 3 1582-1596, 1994. 

RA Laskowski, MW MacArthur, JM Thornton. Validation of protein models derived from experiment. Curr Opm Struct Biol 5 631-639, 1998. 

J Novotny, R Bruccoleri, M Karplus. An analysis of incorrectly folded protein models Implications for structural predictions. J Mol Biol 177 787-818, 1984. 

L Chiche, LM Gregoret, FE Cohen, PA Kollman. Protein model structure evaluation using the solvation free energy of folding. Proc Natl Acad Sci USA 87 3240-3244, 1990. 

L Holm, C Sander. Evaluation of protein models by atomic solvation preference. J Mol Biol 225 93-105, 1992. 

R Liithy, JU Bowie, D Eisenberg. Assessment of protein models with three-dimensional profiles. Nature 356 83-85, 1992. 

CS Ring, E Sun, JH McKerrow, GK Lee, PI Rosenthal, ID Kuntz, EE Cohen. Structure-based inhibitor design by using protein models for the development of antiparasitic agents. Proc Natl Acad Sci USA 90 3583-3587, 1993. 

MC Peitsch. PROMOD and SWISS-MODEL Internet-based tools for automated comparative protein modeling. Biochem Soc Trans 24 274-279, 1996. 

AR Dinner, M Karplus. A metastable state m folding simulations of a protein model. Nature Struct Biol 5 236-241, 1998. 

KD Ball, RS Beii y, RE Kunz, E-Y Li, A Proykova, DJ Wales. Erom topographies to dynamics of multidimensional potential energy surfaces of atomic clusters. Science 271 963-966, 1996. RS Berry, N Elmaci, JP Rose, B Vekhter. Linking topography of its potential surface with the dynamics of folding of a protein model. Proc Natl Acad Sci USA 94 9520-9524, 1997. Z Guo, D Thii-umalai. J Mol Biol 263 323-343, 1996. 

B Vekhter, RS Berry. Simulation of mutation Influence of a side group on global minimum structure and dynamics of a protein model. J Chem Phys 111 3753-3760, 1999. 

The strongest verification for a 3D-protein model comes from the experimental 3D-structure. This is the objective of the Critical Assessment of Techniques for Protein Structure Prediction, CASP ( http //predic tioncenter.org), where the structural models are made in advance of the experimental structure of a particular protein. 

In molecular pharmacology research an indirect proof of a structural model is possible by functional examinations, e.g., by molecular biological experiments. Well-selected site directed mutagenesis and their functional characterization allows confirmation or rejection of a molecular protein model. The process is organized as an iterative procedure, where the biological answer of suggested mutations is used to refine the model. The iteration continues until the model... 

Blue copper electron transfer proteins, 6,712-717 Blue copper oxidases, 6,699 Blue copper proteins, 2, 557 6, 649 Blue electron transfer proteins, 6,649,652 spectroscopy, 6, 651 Blue oxidases copper, 6,654,655 Blueprint process, 6,124 Blue proteins model studies, 6,653 Boleite... 

Exercise 7.4. (a) Use the parameters of Table 7.3 and the LD model to calculate the activation energy of the 2— 3 step in solution, (b) Repeat the same calculation in a protein model where a positive charge of +0.5 (3 A from the carbonyl carbon) represents the oxyanion holes, while a negative charge of -0.5 near the His+ residue represents the somewhat screened Asp 102. Simulate the rest of the system by the LD model. 

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