Ahlan wasahlan!

smile to the world and the world will smile to you :)

Friday, January 28, 2011

EXCEL

LINEAR REGRESSION

         Linear regression is an approach to modeling the relationship between a scalar variable y and one or more variables denoted X. In linear regression, models of the unknown parameters are estimated from the data using linear functions. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis,it focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis.
Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.

Linear regression has many practical uses. Most applications of linear regression fall into one of the following two broad categories:
  • If the goal is prediction, or forecasting, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y.
  • Given a variable y and a number of variables X1, ..., Xp that may be related to y, then linear regression analysis can be applied to quantify the strength of the relationship between yXj, to assess which Xj may have no relationship with y at all, and to identify which subsets of the Xj contain redundant information about y, thus once one of them is known, the others are no longer informative and the
QUADRATIC REGRESSION         
          Quadratic regression models are often used in economics areas such as utility function , forecasting, cost-befit analysis, etc. This JavaScript provides parabola regression model. This site also presents useful information about the characteristics of the fitted quadratic function.
Prior to using this JavaScript it is necessary to construct the scatter-diagram for your data.
If by visual inspection of the scatter-diagram, you cannot reject a "parabola shape", then you may use this JavaScript. Otherwise, visual inspection of the scatter-diagram enables you to determine what degree of polynomial regression models is the most appropriate for fitting to your data. 

In order to solve problems involving quadratic regression, it is necessary to:
  • know how to enter data into your graphing calculator for completing modeling problems
  • know how to solve quadratic equations
  • know how to calculate a quadratic equation that best fits a set of given data
  • write and solve an equation for the problem  
These are a few examples~

BEER'S LAW AND LINEAR REGRESSION

 TITRATION CURVE


 LINE BEST FIT

 QUADRATIC REGRESSION

LINKS:

Wednesday, January 12, 2011

SMILES

Salam'alaik
today we learn about SMILES ^^)
this is not an ordinary smile but it is the EXTRAORDINARY one,,
if u want to know more about it..
lets check it out~

SMILES
      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 ObeliskWiswesser Line Notation (WLN), ROSDAL and SLN (Tripos Inc). open-source chemistry community. Other 'linear' notations include the
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.

 TERMINALOGY
The term SMILES refers to a line notation for encoding molecular structures and specific instances should strictly be called SMILES strings. However, the term SMILES is also commonly used to refer to both a single SMILES string and a number of SMILES strings; the exact meaning is usually apparent from the context. The terms Canonical and Isomeric can lead to some confusion when applied to SMILES. The terms describe different attributes of SMILES strings and are not mutually exclusive.
Typically, a number of equally valid SMILES can be written for a molecule. For example, CCO, OCC and C(O)C all specify the structure of ethanol. Algorithms have been developed to ensure the same SMILES is generated for a molecule regardless of the order of atoms in the structure. This SMILES is unique for each structure, although dependent on the canonicalisation algorithm used to generate it, and is termed the Canonical SMILES. These algorithms first convert the SMILES to an internal representation of the molecular structure and do not simply manipulate strings as is sometimes thought. Various algorithms for generating Canonical SMILES have been developed, including those by Daylight Chemical Information Systems, OpenEye Scientific Software, MEDIT and Chemical Computing Group. A common application of Canonical SMILES is indexing and ensuring uniqueness of molecules in a database.
SMILES notation allows the specification of configuration at tetrahedral centers, and double bond geometry. These are structural features that cannot be specified by connectivity alone and SMILES which encode this information are termed Isomeric SMILES. A notable feature of these rules is that they allow rigorous partial specification of chirality. The term Isomeric SMILES is also applied to SMILES in which isotopes are specified.


APPLICATION ON SOME MOLECULES


STRUCTURE AND SMILES FORMULA





















LINKS

Tuesday, January 4, 2011

Protein Data Bank (PDB)


Assalamualaikum and hello!
today we learn about Protein Data Bank(PDB)..
Actually,even me myself don't know what it is all about T__T
It is important for Biomedical students like us to know about this thingy  ^^)


First of all,let me introduce and give some information about PDB~

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 biologists and biochemists 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.
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.

HISTORY OF PDB
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.

SOME STRUCRURES OF PDB

Subtilisin
 1) Subtilisin
         Subtilisin (serine endopeptidase) is a non-specific protease (a protein-digesting enzyme) initially obtained from Bacillus subtilis.
Subtilisins belong to subtilases, a group of serine proteases that initiate the nucleophilic attack on the peptide (amide) bond through a serine residue at the active site. They are physically and chemically well-characterized enzymes. Subtilisins typically have molecular weights of about 20,000 to 45,000 dalton. They can be obtained from soil bacteria, for example, Bacillus amyloliquefaciens. Subtilisins are secreted in large amounts from many Bacillus species.
        Subtilisins are widely used in commercial products, for example, in laundry[2] and dishwashing detergents, cosmetics, food processing[3], skin care ointments[4], contact lens cleaners, and for research purposes in synthetic organic chemistry.
The structure of subtilisin has been determined by X-ray crystallography. It is a 275-residue globular protein with several alpha-helices, and a large beta-sheet. It is structurally unrelated to the chymotrypsin-clan of serine proteases, but uses the same type of catalytic triad in the active site. This makes it the classic example of convergent evolution.
        In molecular biology using B. subtilis as a model organism, the gene encoding subtilisin (aprE) is often the second gene of choice after amyE for integrating reporter constructs into, due to its dispensability.

Prolyl aminopeptidase

2) Prolyl aminopeptidase
       The prolyl aminopeptidase complexes of Ala-TBODA [2-alanyl-5-tert-butyl-(1, 3, 4)-oxadiazole] and Sar-TBODA [2-sarcosyl-5-tert-butyl-(1, 3, 4)-oxadiazole] were analyzed by X-ray crystallography at 2.4 angstroms resolution. Frames of alanine and sarcosine residues were well superimposed on each other in the pyrrolidine ring of proline residue, suggesting that Ala and Sar are recognized as parts of this ring of proline residue by the presence of a hydrophobic proline pocket at the active site. Interestingly, there was an unusual extra space at the bottom of the hydrophobic pocket where proline residue is fixed in the prolyl aminopeptidase. 
      Moreover, 4-acetyloxyproline-betaNA (4-acetyloxyproline beta-naphthylamide) was a better substrate than Pro-betaNA. Computer docking simulation well supports the idea that the 4-acetyloxyl group of the substrate fitted into that space. Alanine scanning mutagenesis of Phe139, Tyr149, Tyr150, Phe236, and Cys271, consisting of the hydrophobic pocket, revealed that all of these five residues are involved significantly in the formation of the hydrophobic proline pocket for the substrate. Tyr149 and Cys271 may be important for the extra space and may orient the acetyl derivative of hydroxyproline to a preferable position for hydrolysis. 
     These findings imply that the efficient degradation of collagen fragment may be achieved through an acetylation process by the bacteria.



lexA repressor


3) LexA repressor
      Repressor LexA or LexA is a repressor enzyme (EC 3.4.21.88) that represses SOS response genes coding for DNA polymerases required for repairing DNA damage. LexA is intimately linked to RecA in the biochemical cycle of DNA damage and repair. RecA binds to DNA-bound LexA causing LexA to cleave itself in a process called autoproteolysis.
DNA damage can be inflicted by the action of antibiotics. Bacteria require topoisomerases such as DNA gyrase or topoisomerase IV for DNA replication. Antibiotics such as ciprofloxacin are able to prevent the action of these molecules by attaching themselves to the gyrase - DNA complex. This is counteracted by the polymerase repair molecules from the SOS response. Unfortunately the action is partly counterproductive because ciprofloxacin is also involved in the synthetic pathway to RecA type molecules which means that the bacteria responds to an antibiotic by starting to produce more repair proteins. These repair proteins can lead to eventual benevolent mutations which can render the bacteria resistant to ciprofloxacin.
    Mutations are traditionally thought of as happening as a random process and as a liability to the organism. Many strategies exist in a cell to curb the rate of mutations. Mutations on the other hand can also be part of a survival strategy. For the bacteria under attack from an antibiotic, mutations help to develop the right biochemistry needed for defense. Certain polymerases in the SOS pathway are error-prone in their copying of DNA which leads to mutations. While these mutations are often lethal to the cell, they can also lead to mutations which improve the bacteria's survival. In the specific case of topoisomerases, some bacteria have mutated one of their amino acids so that the ciproflaxin can only create a weak bond to the topoisomerase.     This is one of the methods that bacteria use to become resistant to antibiotics.
    Impaired LexA proteolysis has been shown to interfere with ciprofloxacin resistance.[1] This offers potential for combination therapy that combine quinolones with strategies aimed at interfering with the action of LexA either directly, or via RecA.

LINKS: 
http://en.wikipedia.org/wiki/Subtilisin
http://en.wikipedia.org/wiki/Repressor_lexA
http://www.rcsb.org/pdb/explore/explore.do?structureId=1X2B
http://www.rcsb.org/pdb/results/results.do?outformat=&qrid=335D0850&tabtoshow=Current