Friday, January 28, 2011

ExCEL!!!


  LINEAR REGRESSION



Introduction
Linear regression is a method of organizing data.  Sometimes it is appropriate to show data as points on a graph, then try to draw a straight line through the data.  Linear regression is an algorithm for drawing such a line.  Linear regression typically uses the least squares method to determine which line best fits the data.  R-Squared is a measure of how well the data points match the resulting line.
Many trading strategies assume that the way a stock moves during a specific time of day can be used to predict the way a stock will move later in the day.  How would you verify or automate such a strategy?  Start by recording historical values.  Each day, record the size and direction of the change in the first period, and the direction and size of the second change, later in the day.  One point on a graph will represent each day's data.  If the original idea was correct, these points should look like a line.  If this is the case, a trader can look at the size of a move in the morning, and guess what the second move that day will look like.
Linear regression provides a deterministic way to this.  First, linear regression will provide an R-Squared value for the historical data.  If this value is too small, the data is not linear, so the original assumptions must change.  If R-Squared is large enough, then the linear regression will provide the best prediction of the second move each day based on the first move.
Imagine that the six points on the graph below represent the historical data that we collected above.  A common way to look at this is to say that the trend is obvious from five of the points, and the sixth point must be a mistake.  This type of reasoning leads to traders who are very successful right up until the day they loose it all.  The least squares method provides a more appropriate way to view the data, because it incorporates all of the points.  In this case it is clear that the strategy is risky, and requires more work.




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.


Below are some example of these regressions:-






that is all about the regression that i known this far..as now the mid break is begin..let's all enjoy our holiday!!!
Hepi hOLIDAy!!!!!!  xD

Tuesday, January 11, 2011

SmiILe$

Hye there,here we are again..Ermm,today i'm going to tell uall about a new things that i had learned just now..It is about the program called smiles..I doubted that all of u had guess the meaning of that right..Well, let me brief u a little about this software.This document presents the foundations of the Daylight Chemical Information System, including our motivations and goals, theoretical discussions on chemical information processing and chemical database design, and insight into the inner workings of some of our algorithms. 

At Daylight, our goal is to provide the best known computer algorithms for chemical information processing to those who need them. We provide these algorithms both as a Daylight Toolkit programmer's library and as a set of ready-to-use programs for the non-programmer. This document is meant to serve both groups as well as users of other products that incorporate Daylight's technology.


Input SMILESDiagram  SMILES
C=CC\C=C\O

CCN(CC)CC

CC(C)C(=O)O

CC(C)C(CCC)C(CCC)C=C

C1CCCCC1

CC1=CC(Br)CCC1


C1CN(CCC1)C2CCCCO2



Input SMILESDiagram SMILES
c1ccco1

Oc1ccncn1

c1ccccn1

ON1CCCCC1

O[n+]1ccccc1

Oc1ccccn1

Cn1cccc1
   
Input SMILESDiagram SMILES
c1cccn1

Oc1ccccn1

C[C@H]=C\C=C\F

C\C=C\C=C\F




So together with this,i attached some piece of my work..I hope u all will enjoy it..


SMILESNameSMILESName
CCethane[OH3+]hydronium ion
O=C=Ocarbon dioxide[2H]O[2H]deuterium oxide
C#Nhydrogen cyanide[235U]uranium-235
CCN(CC)CCtriethylamineF/C=C/FE-difluoroethene
CC(=O)Oacetic acidF/C=C\FZ-difluoroethene
C1CCCCC1cyclohexaneN[C@@H](C)C(=O)OL-alanine
c1ccccc1benzeneN[C@H](C)C(=O)OD-alanine



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


Hermm,,so,how was it? nice right..i think u all should try it..It is really fun though..Besides,it also can teaches us more about chemistry on long carbon chain and others..
I think that is all what i want to write  for this topic..

Wassalam!!!! xD

Tuesday, January 4, 2011

PrOTEin DATA Bank!!



What is PDB?? It is the Protein Data Bank (PDB) that 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.

SUBTISILIN


 Experiment methods:


Many bacterial pathogens produce extracellular proteases that degrade the extracellular matrix of the host and therefore are involved in disease pathogenesis. Dichelobacter nodosus is the causative agent of ovine footrot, a highly contagious disease that is characterized by the separation of the hoof from the underlying tissue. D. nodosus secretes three subtilisin-like proteases whose analysis forms the basis of diagnostic tests that differentiate between virulent and benign strains and have been postulated to play a role in virulence. We have constructed protease mutants of D. nodosus; their analysis in a sheep virulence model revealed that one of these enzymes, AprV2, was required for virulence. These studies challenge the previous hypothesis that the elastase activity of AprV2 is important for disease progression, since aprV2 mutants were virulent when complemented with aprB2, which encodes a variant that has impaired elastase activity. We have determined the crystal structures of both AprV2 and AprB2 and characterized the biological activity of these enzymes. These data reveal that an unusual extended disulphide-tethered loop functions as an exosite, mediating effective enzyme-substrate interactions. The disulphide bond and Tyr92, which was located at the exposed end of the loop, were functionally important. Bioinformatic analyses suggested that other pathogenic bacteria may have proteases that utilize a similar mechanism. In conclusion, we have used an integrated multidisciplinary combination of bacterial genetics, whole animal virulence trials in the original host, biochemical studies, and comprehensive aof crystal structures to provide the first definitive evidence that the extracellular secreted proteases produced by D. nodosus are required for virulence and to elucidate the molecular mechanism by which these proteases bind to their natural substrates. We postulate that this exosite mechanism may be used by proteases produced by other bacterial pathogens of both humans and animals.


Classification : Hydrolase


Author
Kennan, R.M.,   Wong, W.,   Dhungyel, O.P.,   Han, X.,   Wong, D.,   Parker, D.,   Rosado, C.J.,   Law, R.H.P.,   McGowan, S.,   Reeve, S.B.,   Levina, V.,   Powers, G.A.,


Prolyl Aminopeptidase
 Experiment methods:

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.


Classification : Hydrolase


Author  Nakajima, Y.,   Ito, K.,   Sakata, M.,   Xu, Y.,   Matsubara, F.,   Hatakeyama, S.,  Yoshimoto
 Lex A Repressor

 Experiment methods :


Escherichia coli shows a pleiotropic response (the SOS response) to treatments that damage DNA or inhibit DNA replication. Previous evidence has suggested that the product of the lexA gene is involved in regulating the SOS response, perhaps as a repressor, and that it is sensitive to the recA protease. We show here that lexA protein is a repressor of at least two genes, recA and lexA. Purified protein bound specifically to the regulatory regions of the two genes, as judged by DNase I protection experiments, and it specifically inhibited in vitro transcription of both genes. The binding sites in recA and lexA were found to be about 20 base pairs (bp) and 40 bp long, respectively. The 40-bp sequence in lexA was composed of two adjacent 20-bp sequences, which had considerable homology to one another and to the corresponding recA sequence. These 20-bp sequences, which we term "SOS boxes," show considerable inverted repeat structure as well. These features suggest that each box represents a single repressor binding site. Finally, we found that purified lexA protein was a substrate for the recA protease in a reaction requiring ATP or an analogue, adenosine 5'-[gamma-thio]triphosphate, and denatured DNA. 

Classification : Lipid transport

Author : J W Little,D W Mount, and C R Yanisch-Perron

Those are some of my works about Protein Data Bank.Here are some of the websites that you all can refer to  during doing this work.
1)www.pnas.org
2)www.rcsb.org

I hope all of u will enjoy this programme as much as i did..Thank you for spending ur time here...till next time..Wassalamualaikum!!