Re:Proteomics in new drug development

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Dear Members:

An article from Nature Reviews Drug Discovery on Structural Proteomics is attached.

With regards

Dr. Geer M. Ishaq

Assistant Professor

Dept. of Pharmaceutical Sciences

University of Kashmir

Srinagar-190006 (J & K)

Ph: 9419970971, 9906673100

Website: http://ishaqgeer.googlepages.com

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Dear Dr.Geer!!

please see my mail... n reproduce here, i consider you master in

this subject!

>

> Dear Members:

> An article from Nature Reviews Drug Discovery on Structural

Proteomics is attached.

> With regards

>  

> Dr. Geer M. Ishaq

> Assistant Professor

> Dept. of Pharmaceutical Sciences

> University of Kashmir

> Srinagar-190006 (J & K)

> Ph: 9419970971, 9906673100

> Website: http://ishaqgeer.googlepages.com

>

>

> Add more friends to your messenger and enjoy! Go to

http://messenger./invite/

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Dear Madam Barar:

You seem to have misunderstood me somewhere. I am no master in proteomics. I found this topic interesting and just tried to keep the ball rolling by making a few posts so that other members including you would respond/contribute too. Don't know whether I committed some sort of mistake by doing that! I am totally at loss to comprehend where I went wrong. Anyway as desired, I am reproducing your mail hereunder:

With regards

Dr. Geer M. Ishaq

Few years ago, the pharmaceutical sector began to invest heavily in genomics to increase the supply of validated drug targets. The completion of the human genome sequence, has flooded the drug discovery pipeline with more than 75,000 sequences of potential targets.

But the inability to identify valid drug targets by examining gene sequence information has created a gap between genomics and drug discovery. This gap reflects the fact that in most cases, gene sequence reveals little about protein function or disease relevance.

Accordingly, the true value of the genome sequence information will only be realized after a function has been assigned to all of the encoded proteins.

Proteomics technologies have produced an abundance of drug targets, which is creating a bottleneck in drug development process.

There is an increasing need for better target validation for new drug development and proteomic technologies are contributing to it.

Identifying a potential protein drug target within a cell is a major challenge in modern drug discovery; techniques for screening the proteome are, therefore, an important tool.

Major difficulties for target identification include the separation of proteins and their detection. These technologies are compared to enable the selection of the one by matching the needs of a particular project. There are prospects for further improvement, and proteomics technologies will form an important addition to the existing genomic and chemical technologies for new target validation.

Proteomics is applicable for protein analysis and bioinformatics based analysis gives the comprehensive molecular description of the actual protein component.

Basically, proteomics is to provide functional information for all proteins. Much like genomics, proteomics is more of a concept than a defined technology, and it refers to any protein-based approach that has the capacity to provide new information about proteins on a genomewide scale.

The challenge facing proteomics is enormous, because more than 75% of the predicted proteins in multicellular organisms have no known cellular function. However, proteomics is poised to yield remarkable discoveries because this set of proteins is likely to include new enzymes, signaling molecules, and pathways that may be excellent and unanticipated therapeutic targets.

Applying proteomics technologies will not only provide validated targets for drug discovery but will also increase the efficiency of the drug discovery process downstream. For example, genomewide protein purification efforts will provide reagents for high-throughput screens, and structural proteomics efforts will provide three-dimensional structures for drug development. Clearly, proteomics is destined to bridge the gap between genomics and drug discovery.

The primary goal of proteomics is to provide functional annotations for the entire proteome. Of course, the function of a protein has many definitions, ranging from its biochemical activity to its physiological role, and so the optimal proteomics strategy must integrate many different technologies. True proteomics applications must also be unbiased in design, to be poised to discover the unknown.

Pure proteins: The biological infrastructure One key facet of proteomics is that it is firmly an experimental science. Computational methods are not yet able to predict protein function, structure, or suitability for drug development from the amino acid sequence. For many proteomics applications, such as structural proteomics and proteome-wide high-throughput screening and protein interaction studies, one requirement will be to have large quantities of thousands of purified proteins.

It will be important to couple the massive purification efforts with a quality control strategy that ensures the purity and structural integrity of the purified proteins. Proteins or protein fragments produced in heterologous expression hosts, in in vitro transcription–translation reactions, or in in vivo two-hybrid screens are commonly misfolded. Various proteins have been cloned, expressed, and analyzed yeast, bacterial, etc., It was observed that more than 50% of the proteins were insoluble or had an unstable structure . Using such improperly folded proteins for biochemical and pharmacological screens or assays would likely lead to false positive or false negative results. Proteomics scientists who use them also risk contaminating their databases with incorrect results and wasting time and money on fruitless targets. The importance of ensuring structural integrity of protein reagents often is underestimated by those more versed in molecular biology and genomics methods. In the interest of speed and throughput (and marketing!), proteomics researchers should not forget the fundamentals of protein structure and biochemistry.

The general proteomic approaches taken to identify drug targets and to evaluate drug efficacy and toxicity in the preclinical and clinical settings, generally fall into three basic categories:

a profiling approach,

a functional approach and

a structural approach.

The current 'tools' used and applied to the drug-discovery process include

the two-dimensional gel electrophoresis,

liquid chromatography,

mass spectrometry,

isotope-coded affinity tag and

protein biochips.

Clearly, proteomics has begun to set a foothold in every stage of the drug-discovery process. Future developments in this area will likely make a significant impact in our quest for better, safer and cost-effective drugs.

SOME DETAILS OF METHODS

The proteome: Target discovery Although there may be more than 100,000 proteins in humans, only a fraction of these are expressed in any given cell type. To discover and monitor the relevance of a protein to a disease-related process, it is important to catalog where, when, and to what extent a protein is expressed. DNA microarray technology, which monitors the relative abundance of mRNA in a cell, is a powerful way to accomplish this because mRNA and protein concentrations are often correlated. DNA microarray technology can measure even poorly expressed genes, ensuring a comprehensive assessment of which genes are expressed in which tissue . However, since mRNA and protein levels do not always correlate in the cell and many regulatory processes occur after transcription, a direct measure of relative protein abundance is more desirable.

Figure 1. Early proteomics methods. The traditional approach to protein detection and identification involved the use of two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), which separates proteins based on their relative mass and isoelectric point, followed by single-spot analysis via mass spectrometry.

A variety of proteomics technologies are now being used to measure differences in cellular protein abundance. Currently, the primary method is electrophoresis or chromatography coupled with mass spectrometry (MS) (Figure 1). In this method, mixtures of proteins in cellular extracts are resolved and then individual proteins are identified using MS peptide fingerprinting . Although in theory MS approaches have the potential to characterize the entire protein complement of a cell, in practice it has proved difficult to identify proteins of low abundance, because cell extracts, and the resulting mass spectra, are dominated by a few hundred very abundant proteins.

Defining the protein composition of a cell must also take into account the fact that mRNA splicing and covalent modifications generate protein isoforms that might contribute to important regulatory processes in the cell. Documenting the extent to which a protein is modified and the temporal changes in the modifications during disease can provide strategies for therapeutic intervention. Several approaches are being used to study post-translational modifications on a proteome-wide scale. Again, the most popular approach couples MS, which can detect even subtle covalent modifications, with methods to specifically enrich for modified proteins . Other strategies include the use of modification-specific antibodies .

The techniques that catalog changes in gene expression, protein levels, or modification due to disease or other cellular perturbations are powerful methods of identifying potential targets for drug discovery. However, they do not reveal the biochemical mechanism of how a gene product is related to disease or whether the protein is likely to be amenable to drug development. To address these issues, proteomics approaches that address protein function are required.

Chemical proteomics: Screens for activity and binding Most of the proteins identified through genome sequence projects have no known function, although many are expected to have catalytic activity. To link new proteins with known catalytic activities, proteome-scale screens for generic enzyme activities (e.g., protease and phosphatase) should be implemented. These screens could use purified proteins or extracts that contain the protein of interest. In one application of this concept, Phizicky and colleagues fused thousands of yeast genes to the coding sequence of glutathionyl S-transferase and expressed the set of fusion proteins in yeast . The fusion proteins were then tested for several catalytic activities, and many previously unannotated yeast open reading frames (potential coding sequences) were assigned a function (e.g., a cyclic phosphodiesterase and a cytochrome c methyltransferase).

Many of the predicted proteins may also have catalytic functions not previously characterized. Although it is impossible to screen for chemical reactions that are unknown, in theory, identifying small molecules that bind to the new proteins may elucidate clues to new activities. These ligands might be found by screening the new proteins against diverse chemical libraries using existing methods such as NMR spectroscopy , microcalorimetry, or microarrays . The general concept of ascribing function to new proteins by discovering small-molecule ligands might be referred to as chemical proteomics. Of course, chemical proteomics screens would also provide new chemical entities for drug development.

Structural proteomics: Target validation and development The primary sequence of a protein determines its three-dimensional structure, which in turn determines its function. Often, proteins of similar function share structural homology in the complete absence of obvious sequence homology. As a result, many of the newly sequenced proteins share unrecognized structural and functional homology with known proteins. Indeed, on the basis of current estimates, structural information is predicted to provide functional clues for a large proportion of unannotated proteins .

The principle that structure underlies function, often in the absence of sequence homology, has launched a new branch of functional genomics known as structural genomics or structural proteomics . The aim of structural proteomics is to provide three-dimensional information for all proteins.

Figure 2. Yeast two-hybrid system. To detect domains of interaction between two proteins (X and Y or Z), one protein (X) is genetically fused to a DNA-binding domain while the others (Y and Z) are fused to a gene expression activator. If the two proteins do not interact (X and Y), there is no expression of the reporter gene. If they do interact (X and Z), then the reporter gene is expressed.

Figure 3. Affinity chromatography. By immobilizing a ligand (L), whether protein, nucleic acid, or small molecule, to a matrix, it is possible to isolate specific proteins of interest (P) from a mixture. By initially binding the proteins at low stringency levels and slowly increasing the stringency, you can incrementally release bound proteins and thereby determine their relative affinities for the ligand.

For the pharmaceutical industry, access to structural information on a proteome-wide scale is of importance at several levels. Structural information can be used to ascribe function, thereby revealing new potential drug targets, validate targets based on homology to other proteins known to bind specific small molecules, invalidate targets with structural properties that do not lend themselves to binding to a drug, aid the development of hits into leads into drugs using structure-based methods, and perfect structure-prediction algorithms, which will eventually allow scientists to predict structure and function from sequence.

There are nascent structural proteomics projects in both the public and private sectors. The current public projects are located in Germany, Canada, Japan, and the United States with an aggregate public funding of more than U.S.$100 million.

Interaction proteomics: Target validation Protein–protein interactions lie at the heart of most cellular processes, including carbohydrate and lipid metabolism, cell-cycle regulation, protein and nucleic acid metabolism, signal transduction, and cellular architecture. A complete understanding of cellular function depends on a full characterization of the complex network of cellular protein–protein associations. More importantly, many human diseases—cancer, autoimmune disorders, and viral infections—occur because of failure or aberrations in protein–protein associations. Therefore, elucidating the complete set of interactions that involve proteins having known and potential associations with human disease will be an important step toward revealing new units of biological function and new targets for therapeutic intervention.

The technology most commonly used to discover protein–protein interactions on a genomewide scale is the yeast two-hybrid system (Figure 2). Many new protein interactions have been discovered using this system, but despite its power, it also has significant shortcomings. First, it is not uncommon to have several false positive interactions for every valid interaction, and distinguishing the wheat from the chaff is time-consuming. Second, comprehensive genomewide two-hybrid screens have identified only a fraction of known interactions. Finally, the method can characterize only bimolecular interactions—proteins that exist in large assemblies are less amenable to two-hybrid analysis.

Alternative proteomics technologies are being developed to complement the two-hybrid system. These methods reveal direct protein–protein interactions by using protein affinity chromatography (Figure 3). Protein affinity chromatography, as developed by Greenblatt, Alberts, and colleagues , has the disadvantage of requiring purified proteins as reagents, but it is superior to the two-hybrid approach because it generates fewer false positives and is more amenable to high-throughput screening. With this technique, the purified protein of interest is immobilized on a solid support, and proteins or small molecules that associate with it are identified by gel electrophoresis and mass spectrometry. This method, which has been used to discover protein interactions in prokaryotic and eukaryotic systems, can characterize protein interactions having affinities in the range of 3 µM or stronger and to purify proteins or protein complexes whose levels in cell extracts are as low as 1/100,000.

Bioinformatics: The next decade

One of the aims of genomics and proteomics is to move from an experimental to an in silico science, in which changes in cellular physiology and pharmacology can be predicted using computational methods.

To improve the predictive power of bioinformatics, the first step is clearly to complete the annotation of the proteome. We imagine that to accomplish this, a portion of the research community will temporarily adopt an approach that is hypothesis-generating rather than hypothesis-driven. In this new strategy, researchers will create databases of "unbiased", genomewide or proteome-wide experimental results. Knowledge-discovery and pattern-recognition algorithms will be applied to these data to generate new insights into and hypotheses about protein and cellular function. Hypotheses generated from this unbiased approach are likely to be of higher value than current ones based on relatively little data, and they can be tested with more traditional approaches.

As application of this knowledge-discovery concept moves from individual proteins to protein pathways and then to cellular pathways, there will be a dramatic increase in the efficiency of the drug discovery process. Within a decade or before, the pharmaceutical industry will certainly start to harvest the fruits of these new strategies.

Proteomics/Proteomics and Drug Discovery/Rational Drug Design

Serependity has, for a very long time, been a driving force in the Drug Design and Development process. A shift towards rationalization of the process has begun, accompanying the increasing understanding of corresponding areas such as human as well as pathogen biochemistry and pathophysiology. In a broad and general way the rational drug design approach involves a comprehensive study of the affected biochemical pathways, identification of key elements (generally proteins) and designing small molecules to modify/manipulate the functions of these proteins. Most of the approaches are target oriented and hence structure based i.e. drug targets are identified which is typically a component (a protein or some other biomolecule) of a relevant pathway

Structure based Drug Design

In the early 1980s, researchers were not able to take advantage of structure-based methods in the drug discovery process. This was due to a number of factors, the most important of which were a lack of computing power and docking programs that could test potential models as well as a lack of interest in the established community do in part to the aforementioned lack of tools. However, in the 1990s, computing power and available programs increased exponentially as well as the ability to obtain cheaper, more reliable x-ray crystallographic structures necessary for any type of computational study. This marked the start of a new era and the first attempts and successes were published, with the two most famous being the HIV-1 protease inhibitors and renin inhibitors (to combat hypertension) (Lunney). In contemporary drug discovery, structure-based methods are an integral part of the drug development process. This change can be attributed to the rapid advances in genomics and structural biology, as well as developments in information technology. The advancements of technology in several fields that are vital to the drug discovery process increased the pace of drug development. However, many years of research are still required until a drug which is both effective and tolerated by the human body is marketable (). Despite this new technology and increased funding by pharmeaceutical companies, the issues with finding safe, effective compounds have resulted in there being no significant increase in the rate of therapeutic agent release to the general public. ()

Approaches to Structure Based Drug Design

The de novo approach

The de novo approach involves constructing novel drug molecules from the scratch based on the receptor/target structure. This is a particularly challenging task, considering that the search space of potentially feasible structures is to the order of 10100 (Bohacek). The fundamentals of de novo drug design include assembling drug like compounds, evaluating their potential and also searching the sample space for such compounds. The design of the drug molecule is generally driven by the receptor structure or more specifically it's hypothetical interaction with the structure of the receptor. Therefore, modeling the binding site to the greatest possible degree of accuracy is one of the critical steps in the process. Once the binding site is defined, the next step is to place atoms or small molecule within it and evaluate the fit/ interaction. This is done using a scoring function. Depending on the software package used, there now exists a wide variety of scoring functions ranging from simple steric constraints to functions that approximate and evaluate binding free energy. The scoring functions are also responsible for guiding the growth and optimization of structures by assigning fitness values to the evaluated structures. Different ways of constructing complete ligand molecules (in silico) include linking, growing (Figure 1), random structure change, and molecular dynamics based methods. Linking: Atoms, functional groups or fragments are placed at pre-determined interaction sites and are joined together to yield a complete molecule. The linking groups may be either pre-defined or generated on the fly in compliance with the constraints. Growing: A single building block is chosen as a starting point or seed and placed in the interaction site of the receptor. Fragments or other groups are added to provide feasible interactions with the residues in the site. This process is continued until all fragments have been integrated into a single molecule. Molecular Dynamics: The initial fragments are scattered randomly in the interaction site and then allowed to rearrange using Molecular dynamics simulations. Scoring functions are used to evaluate the resultant structures. One of the major drawbacks of de novo algorithms is that they generally ignore synthetic feasibility of the designed structures.

The Lead optimization approach

The Lead optimization approach involves the modification and optimization of existing analogues (drug candidates) that demonstrate therapeutic potential. This technique is based on the study of the interaction between ligands and receptors. The data thus obtained is used to guide the modification of the structures of selected molecules or compounds. A large number of analogues are created and then screened through thereby improving efficacy and other desirable properties

Drug Target Selection and Identification of the Target Site

The selection of the drug target is mainly based on biological and biochemical considerations. Proteomics as a tool in this area is still relatively limited due to the sheer complexity of protein expression in any given cell. Despite this, proteomics' usefulness has grown in other areas of the drug discovery process including biomarker identification and tracking. The ideal drug target for structure-based drug design should bind a small molecule and should be closely associated with the disease. The small molecule then either changes the function of the drug target, or in case of a pathogenic organism, inhibits the function of the target. This will ideally lead to the cell death of the pathogen. In the latter case, the drug target should only be present in the diseased cells or pathogen and should have a unique function that allows for and encourages this selectivity. Furthermore, the uniqueness of the drug target guarantees that another pathway cannot restore the function of an inhibited target. The structure-based search for anti-cancer and autoimmune drugs is much more challenging, since the drug targets regulate essential cell functions. Hence, these targets are not unique and isolated; inhibition of their function not only affects mutant or over/under-activated cells, but also normal cells. For example, the phosphoinositide 3-kinase (PI3K) pathway is both involved in cellular growth and has been directly linked with pancreatic cancer activation (Reddy). Trying to separate convoluted pathways such as this make the drug discovery process much more complex than with other diseases. Another option for targeting in cancer is DNA. Cisplatin and Bleomycin cross-link and cleave DNA respectively and are used to slow down cell division, particularly in cancerous tissues. (Singh)

The ligand binding site of a drug target should be a pocket containing both hydrogen donors and acceptors, as well as hydrophobic residues. In many cases, the selected target location is the active site of an enzyme, as with sildenafil citrate (Viagra), which targets the catalytic subunit of NADPH (); however, it can also be an assembly or regulatory site, as is the case with the phosphotransferase regulatory domain the Bacillus subtilis Spo0f protein, a histidine kinase (Dai-Fu). Even protein-protein interaction sites, which are often large and planar, have been selected as target sites (2-oxoglutarate, a naturally occuring molecule, affects the monomer-monomer interactions of GlnK, an ammonia transporter [AcrB] inhibitor (, Stroud).

Proteomics as a Tool to Discover Biomarkers

`Biomarker' is short for `biological marker.' A biomarker is a molecule, indicator, or test that can be used to measure such processes as disease progression, infection stage, and drug efficacy, as well as other various biological functions. They can also be used as part of safety studies for therapeutic agents. Although the most common biomarkers in use today are small molecules and proteins, the growing fields of pharmacogenetics and pharmacogenomics are attempting to utilize genotypes, haplotypes, and single nucleotide polymorphisms as biomarkers ().

With the growing emphasis on biomarkers as indicators, the National Institutes of Health (NIH), began a `Biomarkers and Surrogate Endpoint Working Group. This organization has set up a classification system for biomarkers. Type 0 biomarkers are more symptomatic in nature and track disease progression over its complete history. They are used in phase 0 clinical studies. These clinical studies use well developed assay techniques in highly regulated populations for specific durations. The goal of these studies is to achieve simple positive or negative results for the drug or system being studied. Type 1 biomarkers are used to track any type of compound injected into the biological system. The most familiar aspects are drug trials, where the researchers are looking for a specific effect. The effect observed may be positive or negative. Type II biomarkers are used to determine a `surrogate endpoint.' A surrogate endpoint, as defined by the FDA, "is a marker – a laboratory measurement or physical sign – that is used in the therapeutic trials as a substitute for a clinically meaningful endpoint that is a direct measure of how a patient feels, functions or survives and is expected to predict the effect of the therapy." In other words, a surrogate endpoint moves beyond the concept of a single biomarker and into the realm where many or no biomarkers may be sufficient. Other symptoms including overall health and mortality may be studied to determine the efficacy of a treatment regimen. Although surrogate endpoints are still in the early stages, two examples that have been accepted are blood pressure and cholesterol, which have a firm connection to cardiovascular health and mortality ().

In addition to the above effects, a biomarker should correlate well with the disease condition and minimize the number of false positives, as well as false negatives. Thus, a biomarker should be able to accurately discriminate between a normal and an infected condition with high reproducibility. The challenge in proteomics research is to identify unique biomarkers from complex biological mixtures that unequivocally correlate with the disease condition. Biomarkers can be utilized for many purposes. Also, established biomarkers are useful as risk factor indicators, capable of providing information to show that a person is susceptible to a disease. QT prolongation, a measure of change in the ventricular electrical cycle, is used as a assessment of a patient's chances for survival after heart attack, as is troponin T, a cardiac enzyme whose levels rise following a heart attack. 5-hydroxytryptophan is a metabolic precursor to seratonin, and has been found to localize around neoplasms in neuroendocrine tissue. Labelling of these molecules allows for visualization of these events by fluorodeoxyglucose positron emission tomography (FDG-PET), which is used in visualizing many types of malignancies. Another PET application uses SPA-RQ, a neurokinin-1 binder, whose injection is used to track binding of the drug Aprepitant™, a drug used in chemotherapy patients to control vomiting ().

There are currently many more biomarkers being studied and in development, and this number will continue to grow as our understanding of the body evolves. The combination of tracer molecules and imaging techniques, as in 5-hydroxytryptophan, creates a powerful method for visualizing areas that should not be touched surgically except as a last resort. The growing use of proteomics to find biomarkers is important as well. Recent advances in analytical techniques such as mass spectrometry and column chromatography have allowed for more comprehensive studies of protein expression. In addition, 2-dimensional electrophoresis provides an overall view of expression, in similar fashion to gene studies. Proteomics studies using these techniques have identified ovarian cancer, macular degeneration, and lipoprotein composition. Development of these techniques will undoubtedly increase in the future ().

References

Asano, T.; Yao, Y.; Shin, S.; McCubrey, J.; Abbruzzese, J. L.; Reddy, S. A. Cancer Res. 2005, 65, 9164-9168.

Bleicher, K. H.; Bohm, H. J.; Muller, K.; Alanine, A. I. Nat. Rev. Drug Discov. 2003, 2, 369-378.

Bohacek, R. S.; Mc, C.; Guida, W. C. "The art and practice of structure-based drug design: a molecular modelling perspective." Med. Res. Rev. 1996, 16, 3–50

Cai, X. H.; Zhang, Q.; Shi, S. Y.; Ding, D. F. Acta Biochim. Biophys. Sin. (Shanghai) 2005, 37, 293-302.

, R.; Hargreaves, R. Nat. Rev. Drug Discov. 2003, 2, 566-580.

Gruswitz, F.; O'Connell, J.,3rd; Stroud, R. M. Proc. Natl. Acad. Sci. U. S. A. 2007, 104, 42-47.

, M. A. Nat. Rev. Drug Discov. 2003, 2, 831-838.

Lunney, E. Structure-Based Design and Two Aspartic Proteases. http://www.netsci.org/Science/Cheminform/feature01.html.

Muzaffar, S.; Shukla, N.; Srivastava, A.; Angelini, G. D.; , J. Y. Br. J. Pharmacol. 2005, 146, 109-117.

Singh, S.; Malik, B. K.; Sharma, D. K. Bioinformation 2006.

IMPORTANT

Toxicity and safety issues remain a significant problem for drug development efforts by pharmaceutical and biotechnology companies.

Exisiting early biomarkers of toxicity are insufficient and this is demonstrated by the high failure rate of candidate therapeutics due to toxicity problems.

It is anticipated that the advent of 'omic' technologies should facilitate a comprehensive understanding of the perturbation of biological systems by toxic insults and, as such, will lead to better predictive models of toxicity for use in drug development.

The field of proteomics continues to develop rapidly and it is already evident that proteomic approaches have much to contribute to the field of 'systems toxicology' and to the development of novel biomarkers of toxicity. The key proteomic approaches are reviewed, their applications in pharmaceutical toxicology are described and what shape future developments in this arena are to be considered. > >> > Dear Members:> > An article from Nature Reviews Drug Discovery on Structural > Proteomics is attached.> > With regards> >  > > Dr. Geer M. Ishaq> > Assistant Professor> > Dept. of Pharmaceutical Sciences> > University of Kashmir> > Srinagar-190006 (J & K)> > Ph: 9419970971, 9906673100> > Website: http://ishaqgeer.googlepages.com> > > > > > Add more friends to your messenger and enjoy! Go to > http://messenger./invite/> >>

[Guest guest]

Hi all,

I appreciate the services of Geer to keep the ship afloat!

Vijay

> > >

> > > Dear Members:

> > > An article from Nature Reviews Drug Discovery on Structural

> > Proteomics is attached.

> > > With regards

> > > Â

> > > Dr. Geer M. Ishaq

> > > Assistant Professor

> > > Dept. of Pharmaceutical Sciences

> > > University of Kashmir

> > > Srinagar-190006 (J & K)

> > > Ph: 9419970971, 9906673100

> > > Website: http://ishaqgeer.googlepages.com

> > >

> > >

> > > Add more friends to your messenger and enjoy! Go to

> > http://messenger./invite/

> > >

> >

>