Mol.modelling of BCL-2 Final

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1. INTRODUCTION

BIOINFORMATICS

Bioinformatics is the combination of biology and information technology. The

discipline encompasses any computational tools and methods used to manage, analyze and

manipulate large sets of biological data. Essentially, bioinformatics has three components:

Fig.1. Applications of Bioinformatics

The creation of databases allowing the storage and management of large

biological data sets.

HOMOLOGY MODELLING OF APOPTOSIS REGULATOR BCL-2 BETA TRANSFORM

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The development of algorithms and statistics to determine relationships among

members of large data sets.

The use of these tools for the analysis and interpretation of various types of biological

data, including DNA, RNA and protein sequences, protein structures, gene expressionProfiles, and biochemical pathways.

The term bioinformatics first came into use in the 1990s and was originally

synonymous with the management and analysis of DNA, RNA and protein sequence data.

Computational tools for sequence analysis had been available since the 1960s, but this was a

minority interest until advances in sequencing technology led to a rapid expansion in the

number of stored sequences in databases such as GenBank. Now, the term has expanded to

incorporate many other types of biological data, for example protein structures, gene

expression profiles and protein interactions. Each of these areas requires its own set of

databases, algorithms and statistical methods.

Second, computers are required for their problem-solving power. Typical problems that

might be addressed using bioinformatics could include solving the folding pathways of protein

given its amino acid sequence, or deducing a biochemical pathway given a collection of RNA

expression profiles. Computers can help with such problems, but it is important to note that

expert input and robust original data are also required.


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The future of bioinformatics is integration. For example, integration of a wide variety

of data sources such as clinical and genomic data will allow us to use disease symptoms to

predict genetic mutations and vice versa. The integration of GIS data, such as maps, weather

systems, with crop health and genotype data, will allow us to predict successful outcomes of

agriculture experiments. Another future area of research in bioinformatics is large-scale

comparative genomics. For example, the development of tools that can do 10-way comparisons

of genomes will push forward the discovery rate in this field of bioinformatics. Along these

lines, the modeling and visualization of full networks of complex systems could be used in the

future to predict how the system (or cell) reacts to a drug for example.

A technical set of challenges faces bioinformatics and is being addressed by faster

computers, technological advances in disk storage space, and increased bandwidth. Finally, a

key research question for the future of bioinformatics will be how to computationally compare

complex biological observations, such as gene expression patterns and protein networks.

Bioinformatics is about converting biological observations to a model that a computer will

understand. This is a very challenging task since biology can be very complex. This problem of

how to digitize phenotypic data such as behavior, electrocardiograms, and crop health into a

computer readable form offers exciting challenges for future bioinformaticians.

HOMOLOGY MODELING

Homology modeling, also known as comparative modeling of protein refers to

constructing an atomic-resolution model of the "target" protein from its amino acid

sequence and an experimental three-dimensional structure of a related homologous protein

(the "template"). Homology modeling relies on the identification of one or more known

protein structures likely to resemble the structure of the query sequence, and on the

production of an alignment that maps residues in the query sequence to residues in the

template sequence. The sequence alignment and template structure are then used to

produce a structural model of the target. Because protein structures are more conserved

than DNA sequences, detectable levels of sequence similarity usually imply significant

structural similarity


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The quality of the homology model is dependent on the quality of the sequence

alignment and template structure. The approach can be complicated by the presence of

alignment gaps (commonly called indels) that indicate a structural region present in the

target but not in the template, and by structure gaps in the template that arise from poor

resolution in the experimental procedure (usually X-ray crystallography) used to solve the

structure. Model quality declines with decreasing sequence identity; a typical model has

~1-2 root mean square deviation between the matched C atoms at 70% sequence

identity but only 2-4 agreement at 25% sequence identity. However, the errors are

significantly higher in the loop regions, where the amino acid sequences of the target and

template proteins may be completely different.

Regions of the model that were constructed without a template, usually by loop

modeling, are generally much less accurate than the rest of the model. Errors in side chain

packing and position also increase with decreasing identity, and variations in these packing

configurations have been suggested as a major reason for poor model quality at low

identity. Taken together, these various atomic-position errors are significant and impede

the use of homology models for purposes that require atomic-resolution data, such as drug

design and protein-protein interaction predictions; even the quaternary structure of a

protein may be difficult to predict from homology models of its subunit(s). Nevertheless,

homology models can be useful in reaching qualitative conclusions about the biochemistry

of the query sequence, especially in formulating hypotheses about why certain residues are

conserved, which may in turn lead to experiments to test those hypotheses. For example,

the spatial arrangement of conserved residues may suggest whether a particular residue is

conserved to stabilize the folding, to participate in binding some small molecule, or to

foster association with another protein or nucleic acid.

Homology modeling can produce high-quality structural models when the target

and template are closely related, which has inspired the formation of a structural genomics

consortium dedicated to the production of representative experimental structures for all

classes of protein folds. The chief inaccuracies in homology modeling, which worsen with

lower sequence identity, derive from errors in the initial sequence alignment and from

improper template selection. Like other methods of structure prediction, current practice in


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homology modeling is assessed in a biannual large-scale experiment known as the Critical

Assessment of Techniques for Protein Structure Prediction, or CASP.

MODELLER

MODELLER is a computer program used in producing homology models of

protein tertiary structures as well as quaternary structures (rarer). It implements a technique

inspired by nuclear magnetic resonance known as satisfaction of spatial restraints, by

which a set of geometrical criteria are used to create a probability density function for the

location of each atom in the protein. The method relies on an input sequence alignment

between the target amino acid sequence to be modeled and a template protein whose

structure has been solved.

MODELLER was originally written and is currently maintained by Andrej Sali

at the University of California, San Francisco. Although it is freely available for academic

use, graphical user interfaces and commercial versions are distributed by Accelrys.

MODELLER is most frequently used for homology or comparative protein structure

modeling: The user provides an alignment of a sequence to be modeled with known related

structures and MODELLER will automatically calculate a model with all non-hydrogen

atoms. MODELLER can also perform multiple comparisons of protein sequences and/or

structures, clustering of proteins, and searching of sequence databases. The program is

used with a scripting language and does not include any graphics. MODELLER implements

an automated approach to comparative protein structure modeling by satisfaction of spatial

restraints.

Briefly, the core modeling procedure begins with an alignment of the

sequence to be modeled (target) with related known 3D structures (templates). This

alignment is usually the input to the program. The output is a 3D model for the targetsequence containing all main chain and side chain non-hydrogen atoms. Given an

alignment, the model is obtained without any user intervention.


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Method for comparative protein structure modeling by

Modeller

Modeller implements an automated approach to comparative protein structure

modeling by satisfaction of spatial Briefly, the core modeling procedure begins with an

alignment of the sequence to be modeled (target) with related known 3D structures

(templates). This alignment is usually the input to the program. The output is a 3D model

for the target sequence containing all main chain and side chain non hydrogen atoms.

Given an alignment, the model is obtained without any user intervention. First, many

distance and dihedral angle restraints on the target sequence are calculated from its

alignment with template 3D structures. The form of these restraints was obtained from a

statistical analysis of the relationships between many pairs of homologous structures. Thisanalysis relied on a database of 105 family alignments that included 416 proteins with

known three dimensional structure. By scanning the database, tables quantifying various

correlations were obtained, such as the correlations between two equivalents C_ C_

distances, or between equivalent main chain dihedral angles from two related proteins.

These relationships were expressed as conditional probability density functions (pdf) and

can be used directly as spatial restraints. For example, probabilities for different values of

the main chain dihedral angles are calculated from the type of a residue considered, from

main chain conformation of an equivalent residue, and from sequence similarity between

the two proteins. Another example is the pdf for a certain C_C_ distance given equivalent

distances in two related protein structures.

Using Modeller for comparative modeling

Simple demonstrations of Modeller in all steps of comparative protein structure

modeling, including fold assignment, sequence-structure alignment, model building, and

model assessment, can be found in references listed http://salilab.org /modeler

/documentation.html. A number of additional tools useful in comparative modeling are

listed at http://salilab.org/bioinformatics resources.shtml.


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The rest of this section is a hands on description of the most basic use of Modeller

in comparative modeling, in which the input are Protein Data Bank (PDB) atom files of

known protein structures, and their alignment with the target sequence to be modeled, and

the output is a model for the target that includes all non-hydrogen atoms. Although

Modeller can find template structures as well as calculate sequence and structure

alignments, it is better in the difficult cases to identify the templates and prepare the

alignment carefully by other means.

The sample input files in this tutorial can be found in the examples/auto model

directory of the Modeller distribution. There are three kinds of input files: Protein Data

Bank atom files with coordinates for the template structures, the alignment file with the

alignment of the template structures with the target sequence, and Modeller commands in

script files that instruct Modeller what to do.

Each atom file is named code.atm where code is a short protein code, preferably the

PDB code; for example,Peptococcus aerogenes ferredoxin would be in a file 1fdx.atm. If

you wish, you can also use file extensions .pdb and .ent instead of .atm. The code must be

used as that proteins identifier throughout the modeling.

Influence of the alignment on the quality of the model cannot be overemphasized.

To obtain the best possible model, it is important to understand how the alignment is used

by Modeller [Sali & Blundell, 1993]. In outline, for the aligned regions, Modeller tries to

derive a 3D model for the target sequence that is as close to one or the other of the

template structures as possible while also satisfying stereo chemical restraints ( e.g., bond

lengths, angles, non-bonded atom contacts, the inserted regions, which do not have any

equivalent segments in any of the templates, are modeled in the context of the whole

molecule, but using their sequence alone. This way of deriving a model means that

whenever a user aligns a target residue with a template residue, he tells Modeller to treatthe aligned residues as structurally equivalent. Command alignment. Check () can be used

to find some trivial alignment mistakes.


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Modeller is a command-line only tool, and has no graphical user interface; instead,

you must provide it with a script file containing Modeller commands. This is an ordinary

Python script.

Modeller is a command-line only tool, and has no graphical user interface; instead,you must provide it with a script file containing Modeller commands. This is an ordinary

Python script. If you are not familiar with Python, you can simply adapt one of the many

examples in the examples directory, or look at the code for the classes used by Modeller

itself, in the modlib/modeller directory. Finally, there are many resources for learning

Python itself, such as a comprehensive tutorial at http://www.python.org/doc/2.3.5/tut/

To run Modeller with the script file model-default.py above, do the following:

1. On Windows: Click on the Modeller link on your Start Menu. This will give

you a Windows Command Prompt, set up for you to run Modeller.

2. Change to the directory containing the script and alignment files you created

earlier, using the cd command.

3. Run Modeller itself by typing the following at the command prompt:

4. Mod9v7 model-default.py

A number of intermediary files are created as the program proceeds. After about 10

seconds on a modern PC, the final 1fdx model is written to file 1fdx.B99990001.pdb.

Examine the model-default.log file for information about the run. In particular, one should

always check the output of the alignment. Check () command, which you can find by

searching for check a. Also,check for warning and error messages by searching for W>

and E>, respectively. There should be no error messages; most often, there are some

warning messages that can usually be ignored.


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2. REVIEW OF LITERATURE

The majority of non-Hodgkin's B-cell lymphomas contain a translocation that

places the bc12 gene into juxtaposition with the transcriptically active Ig heavy-chain

locus, thus deregulating the expression of this proto-oncogene. The bc12 gene product is amembrane-associated mitochondrial protein that regulates cell survival through unknown

mechanisms. Although overproduction of the normal protein appears sufficient for

conferring a selective growth or survival advantage to B cells, point mutations that alter the

coding region of translocated bc12 genes have been described previously by others in a

lymphoma cell line. However, it is not known whether somatic mutations that alter BCL2

proteins occur in vivo or whether they result from chemotherapy or arise through other

mechanisms. For these reasons, we obtained DNA from the t(14;18)-containing tumors of

five patients who had not undergone treatment for their disease, and used a polymerase

chain reaction (PCR)-mismatch technique for rapid identification of point mutations in a

portion of the bc12 open reading frame (ORF) corresponding to the first 131 aminoacids

(aa) of the 239 aa p26 BCL2 protein. DNAs from two t(14;18)-containing cell lines were

also analyzed. Point mutations in this region of the bc12 gene ORF were detected in three

of five patients' tumors and in both cell lines. PCR-mismatch analysis of bc12 in cell lines

and non-Hodgkin's lymphoma cases that lacked the t(14;18) translocation was negative,

thus establishing the specificity of these results. DNA sequencing determined that these

mutations are predicted to produce aa substitutions in the BCL2 proteins of two of the

primary tumors and one of the cell lines. Interestingly, two of the patients contained an

identical C----T transition that resulted in a nonconservative aa substitution (proline----

serine) at position 59 of the BCL2 protein. Further analysis excluded the possibility that

these mutations represented hereditary polymorphisms or PCR artifacts. A cluster of four

point mutations within the translocation + bc12 allele of one patient had hallmarks of the

somatic hypermutation mechanism that is associated with Ig genes and that contributes toantibody diversity. Because of the region of the bcl2 gene analyzed in these t(14;18)

translocations is located nearly 300 kbp from the Ig heavy-chain locus, our data suggest

that the Ig gene somatic hypermutation mechanism can act over extreme distances of


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DNA. It remains to be established whether these somatic mutations that alter BCL2

proteins influence the pathobiology of nonHodgkin's lymphomas.(Tanaka S et al.,1992)

Early pre-B cells derived from mouse lymphoid bone marrow cultures were

expanded on a surrogate stromal cell line composed of NIH3T3 fibroblasts engineered to

secrete interleukin 7 (IL-7). Three immortal, IL-7-dependent cell lines were generated and

infected with recombinant retroviruses to determine the effects of the human follicular B-

cell lymphoma gene, bcl-2, on immature stages of B-cell development. Cells expressing

bcl-2 grew at rates similar to those of control (vector only) cells when plated on bone

marrow stromal lines, but exhibited a c. two-fold net proliferative advantage when grown

in liquid medium supplemented with IL-7 alone. Bcl-2 prevented apoptosis when the

infected early pre-B-cell lines were deprived of IL-7 and other growth factors provided by

stromal cells. Following factor deprivation, a subset of cells expressing bcl-2 survived

indefinitely. Two such cultures spontaneously gave rise to factor-independent variants

which grew slowly in unsupplemented liquid culture and formed agar colonies, yet still

responded positively to IL-7 and kit ligand, and negatively to gamma-interferon. Bcl-2

thus provides a survival capacity and modest growth advantage to early pre-B cells, which

may recapitulate its effects in human B cells bearing t(14;18) translocations and ultimately

contribute to transformation.(Borzillo GV et al., 1992)

Previous cell subfractionation studies have indicated that bcl-2 is an inner

mitochondrial membrane protein. We have sought to determine the ultrastructural

localization of bcl-2 protein in lymphoma and breast carcinoma cell lines and biopsy

material known to overexpress bcl-2 using immunoelectron microscopy. To avoid the

possibility of processing artifacts, samples were prepared by three different methods:

progressive lowering of temperature, cryosectioning, and freeze-substitution. In all

instances the labeling of bcl-2 protein was relatively weak but the distribution the same. In

both lymphoma and breast carcinoma tissues, bcl-2 protein was detected on the periphery

of mitochondria: little labeling of either the mitochondrial matrix or cristae could be

detected. Labeling was also detected on the perinuclear membrane and throughout the

cytoplasm, as also indicated by confocal microscopy. These data therefore indicate that


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bcl-2 protein can be detected at several intracellular sites and that at the likely functional

destination, the mitochondria, there appears to be, contrary to expectations, a preferential

association with the outer membrane.(Mohaghan et al.,1992)

Caspases are a family of cysteine proteases implicated in the biochemical and

morphological changes that occur during apoptosis (programmed cell death). The loop

domain of Bcl-2 is cleaved at Asp34 by caspase-3 (CPP32) in vitro, in cells overexpressing

caspase-3, and after induction of apoptosis by Fas ligation and interleukin-3 withdrawal.

The carboxyl-terminal Bcl-2 cleavage product triggered cell death and accelerated Sindbis

virus-induced apoptosis, which was dependent on the BH3 homology and transmembrane

domains of Bcl-2. Inhibitor studies indicated that cleavage of Bcl-2 may further activate

downstream caspases and contribute to amplification of the caspase cascade. Cleavage-

resistant mutants of Bcl-2 had increased protection from interleukin-3 withdrawal and

Sindbis virus-induced apoptosis. Thus, cleavage of Bcl-2 by caspases may ensure the

inevitability of cell death.(ChengEH et al.,1997)

Phosphorylation of Bcl2 at serine 70 may result from activation of a classic protein

kinase C (PKC) isoform and is required for functional suppression of apoptosis by Bcl2 in

murine growth factor-dependent cell lines (Ito, T., Deng, X., Carr, B., and May, W. S.

(1997) J. Biol. Chem. 272, 11671-11673). Human pre-B REH cells express high levels of

Bcl2 yet remain sensitive to the chemotherapeutic agents etoposide, cytosine arabinoside,

and Adriamycin. In contrast, myeloid leukemia-derived HL60 cells express less than half

the level of Bcl-2 but are >10-fold more resistant to apoptosis induced by these drugs. The

mechanism responsible for this apparent dichotomy appears to involve a deficiency of

mitochondrial PKCalpha since 1) HL60 but not REH cells contain highly phosphorylated

Bcl2; 2) PKCalpha is the only classical isoform co-localized with Bcl2 in HL60 but not

REH mitochondrial membranes; 3) the natural product and potent PKC activator

bryostatin-1 induces mitochondrial localization of PKCalpha in association with Bcl2

phosphorylation and increased REH cell resistance to drug-induced apoptosis; 4)

PKCalpha can directly phosphorylate wild-type but not phosphorylation-negative and loss

of function S70A Bcl2 in vitro; 5) stable, forced expression of exogenous PKCalpha


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induces mitochondrial localization of PKCalpha, increased Bcl2 phosphorylation and a

>10-fold increase in resistance to drug-induced cell death; and () PKCalpha-transduced

cells remain highly sensitive to staurosporine, a potent PKC inhibitor. Furthermore,

treatment of the PKCalpha transformants with bryostatin-1 leads to even higher levels of

mitochondrial PKCalpha, Bcl2 phosphorylation, and REH cell survival following

chemotherapy. While these findings strongly support a role for PKCalpha as a functional

Bcl2 kinase that can enhance cell resistance to antileukemic chemotherapy, they do not

exclude the possibility that another Bcl2 kinase(s) may also exist. Collectively, these

findings identify a functional role for PKCalpha in Bcl2 phosphorylation and in resistance

to chemotherapy and suggest a novel target for antileukemic strategies.(Ruvolo PP et

al.,1998)

Multiple signal transduction pathways are capable of modifying BCL-2 family

members to reset susceptibility to apoptosis. We used two-dimensional peptide mapping

and sequencing to identify three residues (Ser70, Ser87, and Thr69) within the

unstructured loop of BCL-2 that were phosphorylated in response to microtubule-

damaging agents, which also arrest cells at G(2)/M. Changing these sites to alanine

conferred more antiapoptotic activity on BCL-2 following physiologic death signals as

well as paclitaxel, indicating that phosphorylation is inactivating. An examination of

cycling cells enriched by elutriation for distinct phases of the cell cycle revealed that BCL-

2 was phosphorylated at the G(2)/M phase of the cell cycle. G(2)/M-phase cells proved

more susceptible to death signals, and phosphorylation of BCL-2 appeared to be

responsible, as a Ser70Ala substitution restored resistance to apoptosis. We noted that

ASK1 and JNK1 were normally activated at G(2)/M phase, and JNK was capable of

phosphorylating BCL-2. Expression of a series of wild-type and dominant-negative kinases

indicated an ASK1/Jun N-terminal protein kinase 1 (JNK1) pathway phosphorylated BCL-

2 in vivo. Moreover, the combination of dominant negative ASK1, (dnASK1), dnMKK7,

and dnJNK1 inhibited paclitaxel-induced BCL-2 phosphorylation. Thus, stress response

kinases phosphorylate BCL-2 during cell cycle progression as a normal physiologic

process to inactivate BCL-2 at G(2)/M.(Yamamoto K et al.,1999)


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The ratio of proapoptotic versus antiapoptotic Bcl-2 members is a critical

determinant that plays a significant role in altering susceptibility to apoptosis. Therefore, a

reduction of antiapoptotic protein levels in response to proximal signal transduction events

may switch on the apoptotic pathway. In endothelial cells, tumor necrosis factor alpha

(TNF-alpha) induces dephosphorylation and subsequent ubiquitin-dependent degradation

of the antiapoptotic protein Bcl-2. Here, we investigate the role of different putative

phosphorylation sites to facilitate Bcl-2 degradation. Mutation of the consensus protein

kinase B/Akt site or of potential protein kinase C or cyclic AMP-dependent protein kinase

sites does not affect Bcl-2 stability. In contrast, inactivation of the three consensus

mitogen-activated protein (MAP) kinase sites leads to a Bcl-2 protein that is ubiquitinated

and subsequently degraded by the 26S proteasome. Inactivation of these sites within Bcl-2

revealed that dephosphorylation of Ser87 appears to play a major role. A Ser-to-Ala

substitution at this position results in 50% degradation, whereas replacement of Thr74 with

Ala leads to 25% degradation, as assessed by pulse-chase studies. We further demonstrated

that incubation with TNF-alpha induces dephosphorylation of Ser87 of Bcl-2 in intact

cells. Furthermore, MAP kinase triggers phosphorylation of Bcl-2, whereas a reduction in

Bcl-2 phosphorylation was observed in the presence of MAP kinase-specific phosphatases

or the MAP kinase-specific inhibitor PD98059. Moreover, we show that oxidative stress

mediates TNF-alpha-stimulated proteolytic degradation of Bcl-2 by reducing MAP kinase

activity. Taken together, these results demonstrate a direct protective role for Bcl-2

phosphorylation by MAP kinase against apoptotic challenges to endothelial cells and other

cells.(Breitschopf K et al.,2000)

Glucocorticoids are known to induce apoptosis in lymphoid cells, and Bcl-2

overexpression can block the apoptosis-inducing action of glucocorticoids. Since

phosphorylation of Bcl-2 is implicated in regulating Bcl-2 function, we considered the role

of Bcl-2 phosphorylation in protecting lymphoid cells from glucocorticoid-induced cell

death. Five stably transfected cell lines of WEHI 7.1 cells expressing either wild-type Bcl-

2 or alanine mutants of Bcl-2 at amino acids threonine 56, serine 70, threonine 74, or

serine 87 were created. Expression of the mutant Bcl-2 proteins was documented by flow

cytometry and Western blot analysis. Mutation of Bcl-2 on T56 and S87 eliminated the


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ability of Bcl-2 to inhibit glucocorticoid-induced cell shrinkage, mitochondrial

depolarization, DNA fragmentation, and cell death. Mutation of T74 only partially

impaired the ability of Bcl-2 to block glucocorticoid-induced apoptosis whereas mutation

of S70 in Bcl-2 did not alter its ability to block glucocorticoid-induced apoptosis.(Huang

ST etal.,2002)

Bcl-2 protein play important roles in the regulation of apoptosis. We previously

reported that the phosphorylation of Bcl-2 was augmented by treatment with protein

phosphatase 2A (PP2A) inhibitor; however, the kinase responsible for Bcl-2

phosphorylation had not yet been identified. In this study, we identified extracellular-

signal-regulated kinase (ERK) as the responsible kinase for the phosphorylation of Bcl-2.

We also found that the transmembrane region (TM) deleted form of Bcl-2 (Bcl-

2DeltaTM), which was unable to localize on the mitochondria was constitutively

phosphorylated, whereas wild-type Bcl-2 that localized on the mitochondria, was present

in its hypophosphorylated form. The phosphorylation of Bcl-2DeltaTM was retarded by

treatment with MAP kinase ERK kinase (MEK) inhibitor and PP2A did not bind to Bcl-

2DeltaTM. These observations suggest that Bcl-2DeltaTM is constitutively phosphorylated

by ERK, but is not dephosphorylated by PP2A in human tumor cell lines. The

phosphorylation of Bcl-2 resulted in a reduction in anti-apoptotic function, implying that

dephosphorylation promoted the anti-apoptotic activity of Bcl-2 protein in human tumor

cell lines. Thus, the present findings suggest that ERK and PP2A are physiological

regulators of Bcl-2 phosphorylation, and these enzymes exert an influence on the anti-

apoptotic function of Bcl-2.(Tamura et al.,2004)

Abnormal apoptotic events in chronic obstructive pulmonary disease (COPD)

subvert cellular homeostasis and may play a primary role in its pathogenesis. However,

studies in human subjects are limited. p53 and bcl2 protein expression was measured by

western blot on lung tissue specimens from 43 subjects (23 COPD smokers and 20 non-

COPD smokers), using beta-actin as internal control. Additionally, p53 and bcl2

expression patterns were evaluated by immunohistochemistry in formalin-fixed, paraffin-

embedded lung tissue sections from the same individuals. Western blot analysis showed


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statistically significant increased p53 protein levels in COPD smokers in comparison with

non-COPD smokers (p = 0.038), while bcl2 protein levels were not statistically different

between the two groups. Lung immunohistochemistry showed increased ratio of positive

p53-stained type II pneumocytes/total type II pneumocytes in COPD smokers compared to

non-COPD smokers (p = 0.01), whereas the p53 staining ratio in alveolar macrophages and

in lymphocyte-like cells did not differ statistically between the two groups. On the other

hand, bcl2 expression did not differ between the two groups in all three cell types. The

increased expression of pro-apoptotic p53 in type II pneumocytes of COPD patients not

counterbalanced by the anti-apoptotic bcl2 could reflect increased apoptosis in the alveolar

epithelium of COPD patients. Our results confirm previous experiments and support the

hypothesis of a disturbance in the balance between the pro- and anti-apoptotic mediators in

COPD.(Siganaki M et al.,2010)

Full-length and truncated human BCL2 lacking the entire C-terminal hydrophobic

domain have been overexpressed in Spodoptera frugiperda insect cells with the baculovirus

expression system. Immunoblot analysis with BCL2-specific antibodies revealed that both

full-length and truncated BCL2 are expressed as multiple immunoreactive species,

suggesting posttranslational modifications. The expression of the full-length but not the

truncated BCL2 extended the survival of baculovirus-infected cells by preventing virus-

induced DNA cleavage. This result is consistent with the reported protective effect of

BCL2 against apoptosis in mammalian lymphocytes and suggests a conserved function in

evolution. Subcellular fractionation and indirect immunofluorescence studies in intact cells

demonstrated that the recombinant full-length and truncated BCL2 proteins were expressed

predominantly as nuclear membrane-associated proteins. These results imply that BCL2

must utilize hydrophobic domains other than the deleted domain for its association with the

subcellular membranes. Metabolic labeling of insect cells expressing the full-length and

the truncated form of BCL2 with 32P(i) demonstrated that BCL2 is a phosphoprotein.

(Alnemri ES et al.,1992)

PUVA is the first therapeutic choice in early stages of mycosis fungoides (MF). In this

study the effect of PUVA on bcl-2 expression in MF was assessed in 15 patients (three

stage Ia and 12 stage Ib) and 10 controls. Two biopsies were taken from each patient


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before and after 24 sessions of PUVA therapy. Histopathological assessment and

immunohistochemical staining for bcl-2 was performed and showed positive bcl-2 staining

of lymphocytes in 53% of MF cases (8/15) before PUVA, with no statistically significant

difference in the bcl-2 level before and after PUVA therapy (P value 0.3). A statistically

significant difference was found in the bcl-2 level between control samples and MF

patients' biopsies before (P value 0.02) and after PUVA therapy (P value 0.011). In

conclusion, a lack of decline in the bcl-2 level and the absence of clinical or

histopathological correlation with the bcl-2 level before and after PUVA therapy in MF

patients suggest that PUVA-induced apoptosis in MF cases may occur through pathways

other than bcl-2 inhibition.(Weshahy H et al.,2010)

Traumatic brain injury (TBI) triggers a cascade of apoptotic-related events that

includes BCL2 expression, a pro-survival protein in the apoptosis pathway. The purpose of

the study(Hoh NZ et al.,2010) was to use tagging Single Nucleotide Polymorphism (tSNP)

genotypes to screen the BCL2 gene to determine if genetic variability in the BCL2 gene

influences outcomes in 205 patients with severe TBI. Outcomes [Glasgow Outcome Scale

(GOS), Disability Rating Scale (DRS), mortality, and Neurobehavioral Rating Scale-

Revised (NRS-R)] were analyzed at 3, 6, 12, and 24 months. Multivariate analysis

demonstrates that there were 4 tSNPs of significant interest; rs17759659, rs1801018,

rs7236090, and rs949037. Presence of the variant allele for rs17759659 was associated

with poorer outcomes [GOS (p=0.001), DRS (p=0.002), higher mortality (p=0.02;

OR=4.23; CI 1.31-13.61), and NRS-R (p=0.05)]. Presence of the variant allele for

rs1801018 was associated with poorer outcomes [GOS (p=0.02), DRS (p=0.009), and

mortality (p=0.03; OR=3.86; CI 1.18-12.59)]. Being homozygous for the wild type allele

for rs7236090 was associated with favorable outcomes on NRS-R (p=0.007), while

homozygosity for the variant genotype was associated with favorable outcome on GOS

(p=0.007) and DRS (p=0.006). The homozygous variant for rs949037 was associated with

favorable outcomes [GOS (p=0.04) and DRS (p=0.03)], and homozygous wild type was

associated with increased mortality at 3 months [(p=0.005; OR 3.67; CI 1.08-12.49].

Rs17759659 for GOS is the only finding to stand up to Bonferroni Correction. These data

support for the possibility that genetic variability for pro-survival proteins, particularly


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3. MATERIALS AND METHODS

Homology modeling is an improved method based on the fact that homologous

proteins have similar 3D structures. In the case that a homologue of the protein of interestis available, with such tools as MODELLER, it's possible to build a model from the

template 3D coordinates and an alignment of amino-acids sequences. MODELLER applies

the structure of the template to the protein of interest taking into account the sequence

constraints (steric clashes, electrostatic interactions, amino acids secondary structure

propensities, etc).

3.1 STEPS IN HOMOLOGY MODELING

1. Selection of Template molecule

2. Alignment of Template with Target

3. Model Generation

4. Model Assessment

3.1.1 Template Selection

If the percentage sequence identity between the sequence of interest and a protein

with known structure is high enough (more than 25 or 30 %) simple database search

programs like FASTA or BLAST are clearly adequate to detect the homology.

3.1.2 Template Alignment

A critical step in the development of a homology model is the alignment of the

unknown sequence with the homologues. Factors to be considered when performing an

alignment are

(1) Which algorithm to use for sequence alignment

(2) Which scoring method to apply

(3) Whether and how to assign gap penalties


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3.1.3 Model Generation

Given a template and an alignment, the information contained therein must be used

to generate a three-dimensional structural model of the target, represented as a set of

Cartesian coordinates for each atom in the protein. Three major classes of model

generation methods have been proposed.

3.1.4 Fragment assembly

The original method of homology modeling relied on the assembly of a complete

model from conserved structural fragments identified in closely related solved structures.

For example, a modeling study of serine proteases in mammals identified a sharp

distinction between "core" structural regions conserved in all experimental structures in the

class, and variable regions typically located in the loops where the majority of the

sequence differences were localized. Thus unsolved proteins could be modeled by first

constructing the conserved core and then substituting variable regions from other proteins

in the set of solved structures. Current implementations of this method differ mainly in the

way they deal with regions that are not conserved or that lack a template.

3.1.5 Segment matching

The segment-matching method divides the target into a series of short segments,

each of which is matched to its own template fitted from the Protein Data Bank. Thus,

sequence alignment is done over segments rather than over the entire protein. Selection of

the template for each segment is based on sequence similarity, comparisons of alpha

carbon coordinates, and predicted steric conflicts arising from the van der Waals radii of

the divergent atoms between target and template.

3.1.6 Model Assessment

Assessment of homology models without reference to the true target structure is

usually performed with two methods: statistical potentials or physics-based energy

calculations. Both methods produce an estimate of the energy (or an energy-like analog)


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for the model or models being assessed; independent criteria are needed to determine

acceptable cutoffs. Neither of the two methods correlates exceptionally well with true

structural accuracy, especially on protein types underrepresented in the PDB, such as

membrane proteins.

3.2 NATIONAL CENTER FOR BIOTECHNOLOGY INFORMATION (NCBI)

The National Center for Biotechnology Information advances science and health by

providing access to biomedical and genomic information.

The National Center for Biotechnology Information (NCBI) is part of the

United States National Library of Medicine (NLM), a branch of the National Institutes of

Health. The NCBI is located in Bethesda, Maryland and was founded in 1988 through

legislation sponsored by Senator Claude Pepper. The NCBI houses genome sequencingdata in GenBank and an index of biomedical research articles in Pub Med Central and Pub

Med, as well as other information relevant to biotechnology. All these databases are

available online through the Entrez search engine.

3.3 BASIC LOCAL ALIGNMENT SEARCH TOOL(BLAST)


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In Bioinformatics, Basic Local Alignment Search Tool, orBLAST, is an algorithm

for comparing primary biological sequence information, such as the amino-acid sequences

of different proteins or the nucleotides of DNA sequences. A BLAST search enables a

researcher to compare a query sequence with a library or database of sequences, and

identify library sequences that resemble the query sequence above a certain threshold. Forexample, following the discovery of a previously unknown gene in the mouse, a scientist

will typically perform a BLAST search of the human genome to see if humans carry a

similar gene; BLAST will identify sequences in the human genome that resemble the

mouse gene based on similarity of sequence.


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The four programs perform the following tasks

a) Blastp

Compares an amino acid query sequence against a protein sequence database

b) Blastn

Compares a nucleotide query sequence against a nucleotide sequence database

c) Blastx

Compares the six-frame conceptual translation products of a nucleotide query

sequence (both strands) against a protein sequence database

3.3.1 Working of Blast

The fundamental unit of BLAST algorithm output is the High-scoring Segment Pair

(HSP), wherein each segment of the pair is an equal-length but arbitrarily long run of

contiguous residues for which the aggregate alignment score against the other segment in

the pair is locally maximal and, further, meets or exceeds some positive-valued threshold

or cutoff score.

A (possibly empty) set of HSPs is thus defined by two sequences, a scoring system,

and a cutoff score.

In the programmatic implementations of the BLAST algorithm described here, each

HSP consists of a segment from the query sequence and one from a database

sequence.

The cutoff score has been parameterized to permit the programs' sensitivity and

selectivity to be adjusted.

A Maximal-scoring Segment Pair (MSP) is defined by two sequences and a scoring

system and is the highest-scoring of all possible segment pairs that can be produced


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from the two sequences.The methods of are applicable to determining the statistical

significance of MSP scores in the limit of infinitely long sequences, under a

random sequence model that assumes independent and identically distributed

residues at each sequence position.

In the programs described here, statistics have been extrapolated to assessing the

significance of HSP scores obtained from comparisons of biological sequences

within the context of a database search.

The approach to similarity searching taken by the BLAST programs is first to look

for similar segments between the query sequence and a database sequence, then to

evaluate the statistical significance of any matches that were found, and finally to

report only those matches that satisfy a user-selectable threshold of significance.

3.4 PROTEIN DATA BANK (PDB):


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The PDB archive contains information about experimentally-determined structures

of proteins, nucleic acids, and complex assemblies. As a member of the PDB, the RCSB

PDB curates and annotates PDB data according to agreed upon standards.

The RCSB PDB also provides a variety of tools and resources. Users can perform

simple and advanced searches based on annotations relating to sequence, structure and

function. These molecules are visualized, downloaded, and analyzed by users who range

from students to specialized scientists.

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.

3.5 MODELLER:

Modeller is a computer program that models three-dimensional structures of

proteins and their assemblies by satisfaction of spatial restraints Modeller is most

frequently used for homology or comparative protein structure modeling: The user

provides an alignment of a sequence to be modeled with known related structures and

Modeller will automatically calculate a model with all non-hydrogen atoms.


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3.5.1 TYPES OF MODELLER:

There are 5 types in modeller

a) Basic Modeling

Model a sequence with high identity to a template. This exercise introduces the use

of MODELLER in a simple case where the template selection and target-templatealignments are not a problem

b) Advanced Modeling

Model a sequence based on multiple templates and bound to a ligand. This exercise

introduces the use of multiple templates, ligands and loop refinement in the process of

model building with MODELLER.

c) Iterative Modeling

Increase the accuracy of the modeling exercise by iterating the 4 step process. This

exercise introduces the concept of MOULDING to improve the accuracy of comparative

models.

d) Difficult Modeling


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Model a sequence based on a low identity to a template. This exercise uses

resources external to MODELLER in order to select a template for a difficult case of

protein structure prediction.

e) Modeling with Cyro-Em

Model a sequence using both template and cryo-EM data. This exercise assesses

the quality of generated models and loops by rigid fitting into cryo-EM maps, and

improves them with flexible EM fitting

The methods are applicable to determining the statistical significance of MSP

scores in the limit of infinitely long sequences, under a random sequence model

that assumes independent and identically distributed residues at each sequence

position.

4. RESULTS AND DISCUSSION

Apoptosis regulator Bcl-2 beta isoform [Homo sapiens]

Apoptosis regulator Bcl-2 beta isoform has the identity of sequence length

including 205 amino acids. So, the consequence of gap would not be considered.

Conserved domain of Apoptosis regulator Bcl-2 beta isoform was detected in NCBI and is

the same as the common secondary structures determined by GRASP package. The

sequence identity of the catalytic domain is as high as 99%, which suggests the most

important part of the sequence for catalytic activity is most conserved. The binding pocket

also has the sequence identity above 90%. Therefore, we conclude that this alignment canbe used to construct a reliable 3D model for Apoptosis regulator Bcl-2 beta isoform To

predict the structure we Blast our target sequence with the template sequence of Anti-

Apoptotic Protein Bcl-2 Complexed With An Acyl-Sulfonamide-Based Ligand have the

similar quality of Ramachandran plots, which are acceptable for the relatively low


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percentage of residues having disallowed torsional angels. Secondary structures have been

investigated by GRASP package, and we found that has more extent secondary structures

and better stereochemistry character, which allows further refinement. The quality of the

Ramachandran plot as well as the goodness factors was found to be better . And no

residues have disallowed conformations. Thus, the above analysis suggests the backbone

conformations to be better than those of the templates. Result shows that total, potential

and kinetic energies are always remained constant during the simulation and the protein

size also remained constant. It can be seen that the system remains in equilibrium during

the entire simulation. Then, we concluded that predicted structure is stable at room

temperature. In summary, the quality of the backbone conformation, the residue

interaction, the residue contact and the dynamic stability of the structure are all well within

the limits established for reliable structures. It suggests that structure of Apoptosis

regulator Bcl-2 beta isoform is obtained to characterize proteinsubstrate interactions and

to investigate the relation between the structure and function.

BLAST OUTPUT


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The Fasta Format of the Target Sequence

>gi|72198346|ref|NP_000648.2| apoptosis regulator Bcl-2 beta isoform

[Homo sapiens]

MAHAGRTGYDNREIVMKYIHYKLSQRGYEWDAGDVGAAPPGAAPAPGIFSSQPGHTPHPAASRDPVARTS

PLQTPAAPGAAAGPALSPVPPVVHLTLRQAGDDFSRRYRRDFAEMSSQLHLTPFTARGRFATVVEELFRD

GVNWGRIVAFFEFGGVMCVESVNREMSPLVDNIALWMTEYLNRHLHTWIQDNGGWVGALGDVSLG

Template sequence

>gi|268612509|pdb|1YSW|A Chain A, Solution Structure Of The Anti-

Apoptotic Protein Bcl-2 Complexed With An Acyl-Sulfonamide-Based Ligand

HAGRTGYDNREIVMKYIHYKLSQRGYEWDAGDDVEENRTEAPEGTESEVVHLTLRQAGDDFSRRYRRDFA

EMSSQLHLTPFTARGRFATVVEELFRDGVNWGRIVAFFEFGGVMCVESVNREMSPLVDNIALWMTEYLNR

HLHTWIQDNGGWDAFVELYGPSMR


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STRUCTURE


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Evaluation of residues

Residue [ 77 :ALA] ( 171.07, 159.59) in Allowed region

Residue [ 83 :GLY] ( 107.53, -50.00) in Allowed region

Residue [ 111 :ASP] ( -70.44, -66.54) in Allowed region

Residue [ 120 :HIS] ( 76.58,-177.17) in Allowed region


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Residue [ 192 :ASN] ( -70.32,-177.98) in Allowed region

Residue [ 200 :GLY] (-115.39, 93.26) in Allowed region

Residue [ 85 :ALA] ( 66.97, 110.02) in Outlier region

Residue [ 165 :GLU] ( 156.97, 24.10) in Outlier region

Residue [ 197 :GLY] ( 43.08, -90.70) in Outlier region

Number of residues in favoured region (~98.0% expected) : 194 ( 95.6%)

Number of residues in allowed region ( ~2.0% expected) : 6 ( 3.0%)

Number of residues in outlier region : 3 ( 1.5%)

5.CONCLUSION

Homology modeling was designed and developed for Apoptosis regulator Bcl-2 beta

isoform [Homo sapiens] enzyme 3D structural model using MODELLER because three

dimensional structures are not available in PDB. The structure of Apoptosis regulator Bcl-


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2 beta isoform [Homo sapiens] is important for establishing its molecular fuction. The

sequence similarity is 99% with the template and reliability of the predicted model thus

generated using MODELLER. The alignment between two proteins shows high identity

when compared with other protein . The least objective function score was selected for

model build and found the dope scores for template and least objective function score.

Ramachandran plot predicted the number of residues in the most favoured region

A,B,Land the percentage is (~98.0% expected):194 ( 95.6%) Number of residues in

allowed region ( ~2.0% expected): 6 ( 3.0%) Number of residues in outlier region:3(1.5%)

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http://www.ncbi.nlm.nih.gov/pubmed?term=%22Liu%20CY%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Wu%20MC%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Chen%20F%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Ter-Minassian%20M%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Asomaning%20K%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Zhai%20R%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Wang%20Z%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Su%20L%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Heist%20RS%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Heist%20RS%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Kulke%20MH%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Lin%20X%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Liu%20G%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Christiani%20DC%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Hoh%20NZ%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Wagner%20AK%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Alexander%20SA%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Clark%20R%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Beers%20SR%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Okonkwo%20DO%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Ren%20D%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Conley%20YP%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Conley%20YP%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Tanaka%20S%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Louie%20DC%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Kant%20JA%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Reed%20JC%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Siganaki%20M%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Koutsopoulos%20AV%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Neofytou%20E%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Vlachaki%20E%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Psarrou%20M%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Soulitzis%20N%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Pentilas%20N%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Pentilas%20N%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Schiza%20S%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Siafakas%20NM%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Tzortzaki%20EG%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Tamura%20Y%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Simizu%20S%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Osada%20H%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Huang%20ST%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Cidlowski%20JA%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Cidlowski%20JA%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Breitschopf%20K%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Haendeler%20J%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Malchow%20P%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Zeiher%20AM%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Dimmeler%20S%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Yamamoto%20K%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Ichijo%20H%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Korsmeyer%20SJ%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Ruvolo%20PP%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Deng%20X%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Carr%20BK%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22May%20WS%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Cheng%20EH%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Kirsch%20DG%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Clem%20RJ%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Ravi%20R%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Kastan%20MB%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Bedi%20A%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Ueno%20K%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Hardwick%20JM%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Monaghan%20P%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Robertson%20D%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Amos%20TA%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Dyer%20MJ%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Mason%20DY%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Greaves%20MF%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Alnemri%20ES%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Robertson%20NM%22%5BAuthor%5Dhttp://www.ncbi.nlm.nih.gov/pubmed?term=%22Fernande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Mol.modelling of BCL-2 Final