Models homology modelling

Figure 15.9. Superimposition of 3D protein structures at CE server. The opening graphic window for the superimposition of the modeled (homology modeling with SPDBV) pigeon lysozyme (USR2 in pink) and hen s egg-white lysozyme, ILYZ.pdb (USR1 in blue). The superimposed structures are displayed with the alignment summary (e.g., sequence identity, rms deviation).

From Sequence to Structure Homology and Ah Initio Structure Models Homology Modeling... 

It is of particular interest to be able to correlate solubility and partitioning with the molecular stmcture of the surfactant and solute. Likes dissolve like is a well-wom plirase that appears applicable, as we see in microemulsion fonnation where reverse micelles solubilize water and nonnal micelles solubilize hydrocarbons. Surfactant interactions, geometrical factors and solute loading produce limitations, however. There appear to be no universal models for solubilization that are readily available and that rest on molecular stmcture. Correlations of homologous solutes in various micellar solutions have been reviewed by Nagarajan [52]. Some examples of solubilization, such as for polycyclic aromatics in dodecyl sulphonate micelles, are driven by hydrophobic... 

Bah A1995. Modelling Mutations and Homologous Proteins. Current Opinion in Biotedinobgy6 437-I5 l... 

Homology modeling is discnssed in recent computational drug design texts and... 

One of the most sensitive tests of the dependence of chemical reactivity on the size of the reacting molecules is the comparison of the rates of reaction for compounds which are members of a homologous series with different chain lengths. Studies by Flory and others on the rates of esterification and saponification of esters were the first investigations conducted to clarify the dependence of reactivity on molecular size. The rate constants for these reactions are observed to converge quite rapidly to a constant value which is independent of molecular size, after an initial dependence on molecular size for small molecules. The effect is reminiscent of the discussion on the uniqueness of end groups in connection with Example 1.1. In the esterification of carboxylic acids, for example, the rate constants are different for acetic, propionic, and butyric acids, but constant for carboxyUc acids with 4-18 carbon atoms. This observation on nonpolymeric compounds has been generalized to apply to polymerization reactions as well. The latter are subject to several complications which are not involved in the study of simple model compounds, but when these complications are properly considered, the independence of reactivity on molecular size has been repeatedly verified. 

D strueture of target-ligand eomplexes modeling by homology... 

There are many simple two-parameter equations for Hquid mixture constituents, including the Wilson (25), Margules (2,3,18), van Laar (3,26), nonrandom two-Hquid (NRTI.v) (27), and universal quasichemical (UNIQUAC) (28) equations. In the case of the NRTL model, one of the three adjustable parameters has been found to be relatively constant within some homologous series, so NRTL is essentially a two-parameter equation. The third parameter is usually treated as a constant which is set according to the type of chemical system (27). A third parameter for Wilson s equation has also been suggested for use with partially miscible systems (29,30,31). These equations all require experimental data to fit the adjustable constants. Simple equations of this type have the additional attraction of being useful for hand calculations. 

Figure 9 Relative accuracy of comparative models. Upper left panel, comparison of homologous structures that share 40% sequence identity. Upper right panel, conformations of ileal lipid-binding protein that satisfy the NMR restraints set equally well. Lower left panel, comparison of two independently determined X-ray structures of interleukin 1(3. Lower right panel, comparison of the X-ray and NMR structures of erabutoxin. The figure was prepared using the program MOLSCRIPT [236].

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MJ Sutcliffe, ERE Hayes, TL Blundell. Knowledge based modeling of homologous proteins. Part II Rules for the conformation of substituted side-chains. Protein Eng 1 385-392, 1987. 

TF Flavel. Predicting the structure of the fiavodoxm from Eschericia coli by homology modeling, distance geometry and molecular dynamics. Mol Simul 10 175-210, 1993. 

MI Sutcliffe, CM Dobson, RE Oswald. Solution structure of neuronal bungarotoxm determined by two-dimensional NMR spectroscopy Calculation of tertiary structure using systematic homologous model building, dynamical simulated annealing, and restrained molecular dynamics. Biochemistry 31 2962-2970, 1992. 

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MJ Bower, FE Cohen, RL Dunbrack Jr. Prediction of protein side-chain rotamers from a backbone-dependent rotamer library A new homology modeling tool. J Mol Biol 267 1268-1282, 1997. 

C Lee. Testing homology modeling on mutant proteins Pi edictmg stiaictural and thermodynamic effects m the Ala98 Val mutants of T4 lysozyme. Folding Des 1 1-12, 1995. 

D Cregut, J-P Liautard, L Chiche. Homology modeling of annexm I Implicit solvation improves side-chain prediction and combination of evaluation criteria allows recognition of different types of conformational eiTor. Protein Eng 7 1333-1344, 1994. 

C Wilson, LM Gregoret, DA Agard. Modeling side-chain conformation for homologous proteins using an energy-based rotamer search. J Mol Biol 229 996-1006, 1993. 

S Modi, MI Paine, MI Sutcliffe, L-Y Lian, WU Pnmi-ose, CR Wolfe, GCK Roberts. A model for human cytochrome P450 2d6 based on homology modeling and NMR studies of substrate binding. Biochemistry 35 4540-4550, 1996. 

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