PENENANG JIWA

Tuesday, January 25, 2011

Excel


SCATTER PLOT IN EXCEL

Objectives

  • Enter and format data in an Excel spreadsheet in a form appropriate for graphing
  • Create a scatter plot from spreadsheet data
  • Insert a linear regression line (trendline) into the scatter plot
  • Use the slope/intercept formula for the regression line to calculate a x value for a known y value
  • Explore curve fitting to scatterplot data
  • Create a connected point (line) graph
  • Place a reference line in a graph

Introduction

Beer's Law states that there is a linear relationship between concentration of a colored compound in solution and the light absorption of the solution. This fact can be used to calculate the concentration of unknown solutions, given their absorption readings. First, a series of solutions of known concentration are tested for their absorption level. Next, a scatter plot is made of this empirical data and a linear regression line is fitted to the data. This regression line can be expressed as a formula and used to calculate the concentration of unknown solutions.
 Part 1 - Beer's Law Scatter Plot and Linear Regression

Entering and Formatting the Data in Excel

Open Excel. On Unity/Eos computers, the program will be located on the Application Launcher. On other computers, it will probably be located under the Start Menu.
Your data will go in the first two columns in the spreadsheet.
  • Title the spreadsheet page in cell A1
  • Label Column A as the concentration of the known solutions in cell A3.
  • Label Column B as the absorption readings for each of the solutions in cell B3.
Begin by formatting the spreadsheet cells so the appropriate number of decimal :
  • Click and drag over the range of cells that will hold the concentration data (A5 through A10 for the sample data)
  • Choose Format > Cells... (this is shorthand for choosing Cells... from the Format menu at the top of the Excel window)
  • Click on the Number tab
  • Under Category choose Number and set Decimal places to 5
  • Click OK
  • Repeat for the absorbance data column (B5 through B10 for the sample data), setting the decimal places to 4
  • Enter your data below the column titles
  • You can also place the absorption readings for the unknown solutions below the other data.

The concentration data is probably better expressed in scientific notation.
  • Highlight the concentration data and choose Format > Cells....
  • Choose the Scientific Category and set the Decimal places to 2.
The last step before creating the graph is to choose the data you want to graph.
  • Highlight the data in both the concentration and absorbance columns (but not the unknown data)

Creating the Initial Scatter Plot

With the data you want graphed, start the chart wizard
  • Choose the Chart Wizard icon from the tool bar. If the Chart Wizard is not visible, you can also choose Insert > Chart...
The first dialogue of the wizard comes up
  • Choose XY (Scatter) and the unconnected points icon for the Chart sub-type
  • Click Next >
The Data Range box should reflect the data you highlighted in the spreadsheet. The Series option should be set to Columns, which is how your data is organized.
  • Click Next >
The next dialogue in the wizard is where you label your chart
  • Enter Beer's Law for the Chart Title
  • Enter Concentration (M) for the Value X Axis
  • Enter Absorbance for the Value Y Axis
  • Click on the Legend tab
  • Click off the Show Legend option
  • Click Next >
Keep the chart as an object in the current sheet. Note: Your current sheet is probably named with the default name of "Sheet 1".
  • Click Finish
The initial scatter plot is now finished and should appear on the same spreadsheet page as your original data. A few items of note:
  • Your data should look as though it falls along a linear path
  • Horizontal reference lines were automatically placed in your chart, along with a gray background
  • Your chart is highlighted with square 'handles' on the corners. When your chart is highlighted, a special Chart floating palette should also appear. Note: If the Chart floating palette does not appear, go to Tools>Customize..., click on the Toolbars tab, and then click on the Chart checkbox.If it still doesn't show up as a floating palette, it may be 'docked' on one of your tool bars at the top of the Excel window.
With your graph highlighted, you can click and drag the chart to a wherever you would like it located on the spreadsheet page. Grabbing one of the four corner handles allows you to resize the graph. Note: the graph will automatically adjust a number of chart properties as you resize the graph, including the font size of the text in the graph. You may need to go back and alter these properties. At the end of the first part of this tutorial, you will learn how to do this.

Creating a Linear Regression Line (Trendline)

When the chart window is highlighted, besides having the chart floating palette appear, a Chart menu also appears. From the Chart menu, you can add a regression line to the chart.
  • Choose Chart > Add trendline...
A dialogue box appears.
  • Select the Linear Trend/Regression type
  • Choose the Options tab and select Display equation on chart
  • Click OK to close the dialogue
The chart now displays the regression line

Using the Regression Equation to Calculate Concentrations

The linear equation shown on the chart represents the relationship between Concentration (x) and Absorbance (y) for the compound in solution. The regression line can be considered an acceptable estimation of the true relationship between concentration and absorbance. We have been given the absorbance readings for two solutions of unknown concentration.
Using the linear equation, a spreadsheet cell can have an equation associated with it to do the calculation for us. We have a value for y (Absorbance) and need to solve for x (Concentration). Below are the algebraic equations working out this calculation:
y = 2071.9x + 0.111
y - 0.0111 = 2071.9x
(y - 0.0111) / 2071.9 = x
Now we have to convert this final equation into an equation in a spreadsheet cell. The 'B12' in the equation represents y (the absorbance of the unknown). The solution for x (Concentration) is then displayed in cell 'C12'.
  • Highlight a spreadsheet cell to hold 'x', the result of the final equation (cell C12, labeled B).
  • Click in the equation area (labeled C)
  • Type an equal sign and then a parentheses
  • Click in the cell representing 'y' in your equation (cell B12) to put this cell label in your equation
  • Finish typing your equation
Note: If your equation differs for the one in this example, use your equation
Duplicate your equation for the other unknown.
  • Highlight the original equation cell (C12) and the cell below it (C13)
  • Choose Edit > Fill > Down
Note that if you highlight your new equation in C13, the reference to cell B12 has also incremented to cell B13.

Adjusting the Chart Display

The readability and display of the scatterplot can be further enhanced by modifying a number of the parameters and options for the chart. Many of these modifications can be accessed through the Chart menu, the Chart floating palette, and by double-clicking the element on the chart itself. Let's start by creating a better contrast between the data points and regression line and the background.
  • Double-click in the gray background area of the chart or by selecting Chart Area in the Chart floating palette and then clicking on the Format icon.
In the Chart Area Format dialogue, set the border and background colors
  • Choose None for a Border
  • Choose the white square from the color palette for an Area color
  • Click OK
Now, delete the horizontal grid lines
  • Click on the horizontal grid lines in the chart and press the Delete key
Now, adjust the color and line weight of the regression line and the color of the data points
  • Double-click on the regression line (or choose Series 1 Trendline 1 from the Chart floating palette and then click the Format icon)
  • Choose a thinner line for the Line Weight
  • Click on the word Automatic next to Line Color and the color palette appears. Choose dark blue from the color palette
  • Click OK
  • Double-click on one of the data points (or choose Series 1 and click the Format icon)
  • Choose dark red from the color palette for the Marker Foreground and Background
  • Click OK
Finally, you can move the regression equation to a more central location on the chart
  • Click and drag the regression equation
If necessary, resize the font size for text elements in the graph.
  • Either double click the text element or choose it from the floating palette
  • Click on the Fonts tab
  • Choose a different font size
This is the end of the first half of the scatter plot tutorial.
 

 Part 2 - Titration Data Plotting

Creating a Scatter Plot of Titration Data

In this next part of the tutorial, we will work with another set of data. In this case, it is of a strong acid-strong base titration. With this titration, a strong base (NaOH) of known concentration is added to a strong acid (also of known concentration, in this case). As the strong base is added to solution, its OH- ions bind with the free H+ions of the acid. An equivalence point is reached when there are no free OH- nor H+ ions in the solution. This equivalence point can be found with a color indicator in the solution or through a pH titration curve. This part of the tutorial will show you how to do the latter.
Note that there should be two columns of data in your spreadsheet:
Column A: mL of 0.1 M NaOH added
Column B: pH of the 0.1 M HCl / 0.1M NaOH mixture
  • Using a new page in the spreadsheet, enter your titration data. If you do not have your own data, use the data shown in Figure 1.
  • Return to the beginning of the tutorial if you need hints on formatting the cells to the proper number of decimal place
Now, create a scatter plot of titration data, just as you did with the Beer's Law plot.
  • Highlight the titration data and the Column headers
  • Click on the Chart wizard icon
  • Choose XY (Scatter) and the scatter Chart sub-type
Continue through steps 2 through 4 of the Chart wizard:
  • The defaults for step 2 should be fine if you properly highlighted the data
  • In step 3 enter the chart title and x and y axis labels and turn off the legend
  • In step 4, leave as an object in the current page

Curve Fitting to Titration Data

The next logical question that you might ask is whether a linear regression line or a curved regression line might help us interpret the titration data. You may remember that our goal with this plot is to calculate the equivalence point, that is, what amount of NaOH is needed to change the pH of the mixture to 7 (neutral)?
Create a linear regression line:
  • Choose Chart > Add Trendline...
  • Pick Linear sub-type
Looking at the data, it is clear that the first 45 ml of NaOH do little to alter the pH of the mixture. Then between 45 ml and 55 ml, there is a sharp rise in pH before leveling off again. The data trend does not seem linear at all and, in fact, a linear regression line does not fit the data well at all.
The next approach might be to choose a different type of trendline:
  • Click on the linear regression line in the plot and press the delete key to delete the line
  • Choose Chart > Add Trendline...
  • Pick Polynomial subtype
  • Set the Order of the curve to 2
You can see that a second order polynomial curve does not capture the steep rise of the data well. A higher order curve might be tried:
  • Double-click on the curved regression line
  • Set the Order of the curve to 3
Still, the third order polynomial does not capture the steep part of the curve where it passes through a pH of 7. Even higher order curves could be created to see if they fit the data better. Instead, a different approach will be taken for this data. Go ahead and delete the regression curve:
  • Click on the curved regression line in the plot and press the delete key

Changing the Scatter Plot to a Line Graph

Instead of adding a curved regression line, all of the points of the titration data are connected with a smooth curve. With this approach, the curve is guaranteed to go through all of the data points. This is both good and bad. This option can be used if you have only one pH reading per amount of NaOH added. If you have multiple pH readings for each amount added on the scatter plot, you will not end up with a smooth curve. To change the scatter plot is a (smoothed) line graph.
  • Choose Chart > Chart Type...
  • Select the Scatter connected by smooth lines Chart subtype
This smooth, connected curve helps locate where the steep part of the curve passes through pH 7.

Adding a Reference Line

The chart can be enhanced by adding a reference line at pH 7. This clearly marks the point where the curve passes through this pH.
  • A set of drawing tools should be visible at the bottom of the window. If not, click on the Draw icon two to the right of the Chart wizard icon.
  • Make sure your chart is highlighted
  • Choose the line tool at the bottom of the window
  • Draw a horizontal line at pH 7 across the width of the chart by clicking and dragging a line across the chart area.
  • With the horizontal line still highlighted, choose a 3/4 pt line thickness and a dashed line type at the bottom of the window
Further refinements in the chart can be made by (as you did with the Beer's law chart):
  • removing the other horizontal grid lines
  • turning off the border
  • changing chart colors
  • Thickening the curve and shrinking the data points emphasizes the fitted curve over the individual data points

Modifying the Chart Axis Scale

The above chart gives a good overview of the entire titration. If you would like to focus exclusively on the steep part of the curve between 45 and 55 ml of added NaOH, a new chart can be created which limits the X Axis range. Start by making a copy of the current chart:
  • Select the current chart by clicking near its border
  • Choose Edit > Copy
  • Click a spreadsheet cell about 10 rows below the current chart
  • Choose Edit > Paste
With the new chart highlighted :
  • Choose Value (X) Axis from the Chart floating palette
  • Click on the Format icon
  • Set the Minimum to 45, Maximum to 55
  • Set the Major unit to 1 and Minor unit to 0.25
  • Click OK
Next, both vertical and horizontal gridlines can be added to more accurately locate the equivalency point:
  • Choose Chart > Chart Options...
  • Click on the Gridlines tab
  • Select X axis Major gridlines and Y axis Major gridlines
  • Click OK
Even with this smooth curve passing through all of the data points, it is still an estimation of what intermediate mL added/pH data points would be. A clear inaccuracy is where the curve moves in a negative X direction between the 50 and 51 mL data points. More data points collected between 49 and 51 mL would both better smooth the curve and give a more accurate estimation of the equivalency point.


Posted by at No comments:

Tuesday, January 11, 2011

SMILE

Simplified molecular Input Line Entry Specification 

 introduction:
The simplified molecular input line entry specification or SMILES is a specification for unambiguously describing the structure of chemical molecules using short ASCII strings. SMILES strings can be imported by most molecule editors for conversion back into two-dimensional drawings or three-dimensional models of the molecules.
The original SMILES specification was developed by Arthur Weininger and David Weininger in the late 1980s. It has since been modified and extended by others, most notably by Daylight Chemical Information Systems Inc. In 2007, an open standard called "OpenSMILES" was developed by the Blue Obelisk open-source chemistry community. Other 'linear' notations include the Wiswesser Line Notation (WLN), ROSDAL and SLN (Tripos Inc).
In July 2006, the IUPAC introduced the InChI as a standard for formula representation. SMILES is generally considered to have the advantage of being slightly more human-readable than InChI; it also has a wide base of software support with extensive theoretical (e.g., graph theory) backing.

 

 

A Simplified Chemical Language

SMILES (Simplified Molecular Input Line Entry System) is a line notation (a typographical method using printable characters) for entering and representing molecules and reactions. Some examples are:



SMILESNameSMILESName
CC ethane [OH3+] hydronium ion
O=C=O carbon dioxide [2H]O[2H] deuterium oxide
C#N hydrogen cyanide [235U] uranium-235
CCN(CC)CC triethylamine F/C=C/F E-difluoroethene
CC(=O)O acetic acid F/C=C\F Z-difluoroethene
C1CCCCC1 cyclohexane N[C@@H](C)C(=O)O L-alanine
c1ccccc1 benzene N[C@H](C)C(=O)O D-alanine

Reaction SMILESName
[I-].[Na+].C=CCBr>>[Na+].[Br-].C=CCI displacement reaction
(C(=O)O).(OCC)>>(C(=O)OCC).(O) intermolecular esterification

SMILES contains the same information as might be found in an extended connection table. The primary reason SMILES is more useful than a connection table is that it is a linguistic construct, rather than a computer data structure. SMILES is a true language, albeit with a simple vocabulary (atom and bond symbols) and only a few grammar rules. SMILES representations of structure can in turn be used as "words" in the vocabulary of other languages designed for storage of chemical information (information about chemicals) and chemical intelligence (information about chemistry).

Part of the power of SMILES is that unique SMILES exist. With standard SMILES, the name of a molecule is synonymous with its structure; with unique SMILES, the name is universal. Anyone in the world who uses unique SMILES to name a molecule will choose the exact same name.
One other important property of SMILES is that it is quite compact compared to most other methods of representing structure. A typical SMILES will take 50% to 70% less space than an equivalent connection table, even binary connection tables. For example, a database of 23,137 structures, with an average of 20 atoms per structure, uses only 1.6 bytes per atom when represented with SMILES. In addition, ordinary compression of SMILES is extremely effective. The same database cited above was reduced to 27% of its original size by Ziv-Lempel compression (i.e. 0.42 bytes per atom).
These properties open many doors to the chemical information programmer. Examples of uses for SMILES are:
  • Keys for database access
  • Mechanism for researchers to exchange chemical information
  • Entry system for chemical data
  • Part of languages for artificial intelligence or expert systems in chemistry
The rest of this chapter is a concise exposition of the SMILES encoding rules. For further information, the reader is referred to "SMILES 1. Introduction and Encoding Rules", Weininger, D., J.Chem. Inf. Comput. Sci. 1988, 28,31.




Posted by at No comments:

Monday, January 3, 2011

Protein Data Bank

  • The Protein Data Bank (PDB) is a repository for the 3-D structural data of large biological molecules, such as proteins and nucleic acids. (See also crystallographic database). The data, typically obtained by X-ray crystallography or NMR spectroscopy and submitted by biologistsbiochemists from around the world, are freely accessible on the Internet via the websites of its member organisations (PDBe, PDBj, and RCSB). The PDB is overseen by an organization called the Worldwide Protein Data Bank, wwPDB.
    and The PDB is a key resource in areas of structural biology, such as structural genomics. Most major scientific journals, and some funding agencies, such as the NIH in the USA, now require scientists to submit their structure data to the PDB. If the contents of the PDB are thought of as primary data, then there are hundreds of derived (i.e., secondary) databases that categorize the data differently. For example, both SCOP and CATH categorize structures according to type of structure and assumed evolutionary relations; GO categorize structures based on genes.[1] 
              History
        The PDB originated as a grassroots effort.[1] In 1971, Walter Hamilton of the Brookhaven National Laboratory agreed to set up the data bank at Brookhaven. Upon Hamilton's death in 1973, Tom Koeztle took over direction of the PDB. In January 1994, Joel Sussman was appointed head of the PDB. In October 1998,[2] the PDB was transferred to the Research Collaboratory for Structural Bioinformatics (RCSB); the transfer was completed in June 1999. The new director was Helen M. Berman of Rutgers University (one of the member institutions of the RCSB).[3] In 2003, with the formation of the wwPDB, the PDB became an international organization. The founding members are PDBe (Europe), RCSB(USA), and PDBj (Japan). The BMRB joined in 2006. Each of the four members of wwPDB can act as deposition, data processing and distribution centers for PDB data. The data processing refers to the fact that wwPDB staff review and annotates each submitted entry. The data are then automatically checked for plausibility. (The source code for this validation software has been made available to the public at no charge.
     Gene Ontology
         The Gene Ontology project provides an ontology of defined terms representing gene product properties. The ontology covers three domains:
         Each GO term within the ontology has a term name, which may be a word or string of words; a unique alphanumeric identifier; a definition with cited sources; and a namespace indicating the domain to which it belongs. Terms may also have synonyms, which are classed as being exactly equivalent to the term name, broader, narrower, or related; references to equivalent concepts in other databases; and comments on term meaning or usage. The GO ontology is structured as a directed acyclic graph, and each term has defined relationships to one or more other terms in the same domain, and sometimes to other domains. The GO vocabulary is designed to be species-neutral, and includes terms applicable to prokaryotes and eukaryotes, single and multicellular organisms.
        The GO ontology is not static, and additions, corrections and alterations are suggested by, and solicited from, members of the research and annotation communities, as well as by those directly involved in the GO project. For example, an annotator may request a specific term to represent a metabolic pathway, or a section of the ontology may be revised with the help of community experts (e.g.[2]). Suggested edits are reviewed by the ontology editors, and implemented where appropriate.
         The GO ontology file is freely available from the GO website in a number of formats, or can be accessed online using the GO browser AmiGO. The Gene Ontology project also provides downloadable mappings of its terms to other classification systems.

    Enzyme  Commission
      The Enzyme Commission number (EC number) is a numerical classification scheme for enzymes, based on the chemical reactions they catalyze.[1] As a system of enzyme nomenclature, every EC number is associated with a recommended name for the respective enzyme.
Strictly speaking, EC numbers do not specify enzymes, but enzyme-catalyzed reactions. If different enzymes (for instance from different organisms) catalyze the same reaction, then they receive the same EC number.[2] By contrast, UniProt identifiers uniquely specify a protein by its amino acid sequence
     KEGG Pathway
KEGG PATHWAY is a collection of manually drawn pathway maps (see new maps, change history, and last updates) representing our knowledge on the molecular interaction and reaction networks for: 0. Global Map
1. Metabolism
    Carbohydrate   Energy   Lipid   Nucleotide   Amino acid   Other amino acid   Glycan
    Cofactor/vitamin   Terpenoid/PK   Other secondary metabolite   Xenobiotics   Overview
2. Genetic Information Processing
3. Environmental Information Processing
4. Cellular Processes
5. Organismal Systems
6. Human Diseases and also on the structure relationships (KEGG drug structure maps) in: 7. Drug Development








     Type of protein  
  •      FtsH peptidase

             Pseudomonas aeruginosa is an opportunistic Gram-negative pathogen that causes nosocomial infections for which there are limited treatment options. Penicillin-binding protein PBP3, a key therapeutic target, is an essential enzyme responsible for the final steps of peptidoglycan synthesis and is covalently inactivated by ?-lactam antibiotics. Here we disclose the first high resolution cocrystal structures of the P. aeruginosa PBP3 with both novel and marketed ?-lactams. These structures reveal a conformational rearrangement of Tyr532 and Phe533 and a ligand-induced conformational change of Tyr409 and Arg489. The well-known affinity of the monobactam aztreonam for P. aeruginosa PBP3 is due to a distinct hydrophobic aromatic wall composed of Tyr503, Tyr532, and Phe533 interacting with the gem-dimethyl group. The structure of MC-1, a new siderophore-conjugated monocarbam complexed with PBP3 provides molecular insights for lead optimization. Importantly, we have identified a novel conformation that is distinct to the high-molecular-weight class B PBP subfamily, which is identifiable by common features such as a hydrophobic aromatic wall formed by Tyr503, Tyr532, and Phe533 and the structural flexibility of Tyr409 flanked by two glycine residues. This is also the first example of a siderophore-conjugated triazolone-linked monocarbam complexed with any PBP. Energetic analysis of tightly and loosely held computed hydration sites indicates protein desolvation effects contribute significantly to PBP3 binding, and analysis of hydration site energies allows rank ordering of the second-order acylation rate constants. Taken together, these structural, biochemical, and computational studies provide a molecular basis for recognition of P. aeruginosa PBP3 and open avenues for future design of inhibitors of this class of PBPs.
      Authors:
      Han, S.
      Release Date: 2010-12-22 Classification: Hydrolase/antibiotic   
      Experiment: X-RAY DIFFRACTION with resolution of 2.00 Å
      Compound: 1 Polymer [ | Display for All Results ]  
      Citation: Structural basis for effectiveness of siderophore-conjugated monocarbams against clinically relevant strains of Pseudomonas aeruginosa.
      (2010) Proc.Natl.Acad.Sci.USA [ | Display for All Results ]  
       
       
        •        Thermolysin

                 Thermolysin EC 3.4.24.27 is a thermostable neutral metalloproteinase enzyme produced by the gram-positive bacteria Bacillus thermoproteolyticus.[2] It requires one zinc ion for enzyme activity and four calcium ions for structural stability.[3] Thermolysin specifically catalyzes the hydrolysis of peptide bonds containing hydrophobic amino acids. However thermolysin is also widely used for peptide bond formation through the reverse reaction of hydrolysis.[4] Thermolysin is the most stable member of a family of metalloproteinases produced by various Bacillus species. These enzymes are also termed 'neutral' proteinases or thermolysin -like proteinases (TLPs).
                 Like all bacterial extracellular proteases thermolysin is first synthesised by the bacterium as a pre-proenzyme.[5] Thermolysin is synthesized as a pre-proenzyme consisting of a signal peptide 28 amino acids long, a pro-peptide 204 amino acids long and the mature enzyme itself 316 amino acids in length. The signal peptide acts as a signal for translocation of pre-prothermolysin to the bacterial cytoplasmic membrane. In the periplasm pre-prothermolysin is then processed into prothermolysin by a signal peptidase. The prosequence then acts as a molecular chaperone and leads to autocleavage of the peptide bond linking pro and mature sequences. The mature protein is then secreted into the extracellular medium.[6]

                 Thermolysin has a molecular weight of 34,600 Da. Its overall structure consists of two roughly spherical domains with a deep cleft running across the middle of the molecule separating the two domains. The secondary structure of each domain is quite different, the N-terminal domain consists of mostly beta pleated sheet, while the C-terminal domain is mostly alpha helical in structure. These two domains are connected by a central alpha helix, spanning amino acids 137-151.[7]
                

                    Structure

                  In contrast to many proteins that undergo conformational changes upon heating and denaturation, thermolysin does not undergo any major conformational changes until at least 70 °C.[8] The thermal stability of members of the TLP family is measured in terms of a T50 temperature. At this temperature incubation for 30 minutes reduces the enzymes activity by half. Thermolysin has a T50 value of 86.9 °C, making it the most thermo stable member of the TLP family.[9] Studies on the contribution of calcium to thermolysin stability have shown that upon thermal inactivation a single calcium ion is released from the molecule.[10] Preventing this calcium from originally binding to the molecule by mutation of its binding site, reduced thermolysin stability by 7 °C. However, while calcium binding makes a significant contribution to stabilising thermolysin, more crucial to stability is a small cluster of N-terminal domain amino acids located at the proteins surface.[9] In particular a phenylalanine (F) at amino acid position 63 and a proline (P) at amino acid position 69 contribute significantly to thermolysin stability. Changing these amino acids to threonine (T) and alanine (A) respectively in a less stable thermolysin-like proteinase produced by Bacillus stearothermophillus (TLP-ste), results in individual reductions in stability of 7 °C (F63→T) and 6.3 °C (P69→A) and when combined a reduction in stability of 12.3 °C.[9]

                     Application

                  In the synthesis of aspartame, less bitter-tasting byproduct is produced when the reaction is catalyzed by thermolysin.[11]

          Authors:
          Juers, D.H.,   Weik, M.
          Release Date: 2010-12-08 Classification: Hydrolase   
          Experiment: X-RAY DIFFRACTION with resolution of 2.20 Å
          Compound: 1 Polymer [ | Display for All Results ]  
          2 Ligands [ | Display for All Results ]  
          Citation: Not Available.        
           
           
          •     Leucyl Aminopeptidase

                 Leucyl aminopeptidases (or leucine aminopeptidases, LAPs) are enzymes that preferentially catalyze the hydrolysis of leucine residues at the N-terminus of peptides and proteins. Other N-terminal residues can also be cleaved, however. LAPs have been found across superkingdoms. Identified LAPs include bovine lens LAP, porcine LAP, Escherichia coli (E. coli) LAP (also known as PepA or XerB), and the solanaceous-specific acidic LAP (LAP-A) in tomato (Solanum lycopersicum).

                                       Enzyme Description, Structure, and Active Site

                 The active sites in PepA and in bovine lens LAP have been found to be similar.[1] Shown in the picture below is the proposed model for the active site of LAP-A in tomato based on the work of Strater et al.[2][3]. It is also known that the

                 Shown are the LAP-A residues in the active site. Two Zn+2 cations are also shown, along with a water and a bicarbonate ion that acts as a general base.
                  biochemistry of the LAPs from these three kingdoms is very similar. PepA, bovine lens LAP, and LAP-A preferentially cleave N-terminal leucine, arginine, and methionine residues. These enzymes are all metallopeptidases requiring divalent metal cations for their enzymatic activity [4] Enzymes are active in the presence of Mn+2, Mg+2 and Zn+2. These enzymes are also known to have high pH (pH 8) and temperature optima. At pH 8, the highest enzymatic activity is seen at 60oC. PepA, bovine lens LAP and LAP-A are also known to form hexamers in vivo. The Gu et al. from 1999 demonstrated that six 55kDA enzymatically inactive LAP-A protomers come together to form the 353kDa bioactive LAP-A hexamer. Structures of the bovine lens LAP protomer and the biologically active hexamer have been constructed [5] can be found through Protein Data Bank (2J9A).
          Authors:
          Natarajan, S.,   Huynh, K.-H.,   Kang, L.W.
          Release Date: 2010-09-08 Classification: Hydrolase   
          Experiment: X-RAY DIFFRACTION with resolution of 2.60 Å
          Compound: 1 Polymer [ | Display for All Results ]  
          3 Ligands [ | Display for All Results ]  
          Citation: Not Available.
          Posted by at No comments:
          Subscribe to: Posts (Atom)