Top Bioinformatics Challenges (Chris Burge et al.)

The Top Bioinformatics Challenges according to Chris Burge at MIT and his colleagues are as follows...

• Precise, predictive model of transcription initiation and termination: ability to predict where and when transcription will occur in a genome
• Precise, predictive model of RNA splicing/alternative splicing: ability to predict the splicing pattern of any primary transcript
• Precise, quantitative models of signal transduction pathways:ability to predict cellular response to external stimuli
• Determining effective protein-DNA, protein-RNA and protein-protein recognition codes
• Accurate ab initio structure prediction
• Rational design of small molecule inhibitors of proteins
• Mechanistic understanding of protein evolution: understanding exactly how new protein functions evolve
• Mechanistic understanding of speciation: molecular details of how speciation occurs
• Continued development of effective gene ontologies-systematic ways to describe the functions of any gene or protein
• (Infrastructure and education challenge)
• Education: development of appropriate bioinformatics curricula for secondary, undergraduate and graduate education.

Inferring function from structure

Structure and function can be transferred between similar sequences because they have been conserved over long periods of time. Above 40% sequence identity, homologous proteins tend to have the same function.

Function: Biochemical: the chemical interactions ocurring in a protein; Biological: the role within the cell of the protein; Phenotypic: the role played by the protein in the organism as a whole.

EC (Enzyme Commission) provides a widely used protein functiona classification scheme. There are several databases containing funcional information: SWISS-PROT, GenProtEC, etc. There exist also multifunctional proteins. Gene Ontologies uses a controlled vocabulary for describing the roles of genes and gene products in any organism: (biological, molecular, cellular).

Functional information which can be obtained from 3D protein structures

• Basic structure: in the form of a PDB file.
• Protein-ligand complexes: can provide the biochemical function of the protein.

Relationship between structure and function

Protein structural classification is not of much help since some structures are under-represented. Furthermore, as the number of folds in limited in nature, similar structures can have totally different functions. Most folds have a homologous familiy associated with them, and it is expected that family members will have related function. There are, however, examples of divergence of function.

Analogues: some functions have different structural solutions (examples of convergent evolution).

Assigning function from structure

Ab initio prediction: a protein-ligand binding site (active site) is often found to be the largest cleft in the protein.

Structural comparisons: using structural databases such as CATH or SCOP. It is the most powerful method. Sometimes structural similarit can be the result of convergent evolution.

Structural motifs: detailed knowledge of the active site is required. Six methods:

SITE and SITE-Match: correlates an alignment with PDB and SWISS-PROT files.
TESS: 3D Template Search and Superposition.
Fuzzy Functional Forms (FFFs): derives FFFs from 3D structural information.
SPASM, RIGOR: tools for studying constellations of small number of residues.
Molecular Recognition: searches for similar spatial arrangements of atoms around a particular chemical moiety in proteins by superposing them.
Protein Side Chain Patterns: detects active site in proteins via recurring amino acid side-chain patterns.

Modern Drug Discovery

Target identification:

• usually through biological or genetic investigation.
• An assay to look for modulators (either inhibitors, antagonists, or agonists) of the target activity.
• High-throughput screen (HTS).
• Elaboration of the initial small molecule hit through medicinal chemistry: combinatorial chemistry, quantitative structure activity relationships (QSAR), computer-aided drug design (CADD) and structure-based drug design.
• Lead optimization into a candidate drug: multidimensional optimization problem searching within the relatively limited chemical space of analogs of the lead compound.
• Large-scale production methods, preclinical animal safety studies, clinical trials.

Structural Bioinformatics on Drug DiscoveryTarget Assessment:

Target druggability: the energetically optimal protein would be spherical, with all its hydrophobic residues pointing inward.

A quantitative approach is the rule of five (Lipinski):A compound is likely to show poor absorption or permeation if:

• It has more than five hydrogen bond donors
• The molecular weight is over 500
• The Clog P (calculated octanol/water partition coefficient) is over five
• The sum of nitrogens and oxygens is over 10
• Weak inhibition (<100nm)>

Another physicochemical complementary properties: surface area and volume of the pocket, hydrophobicity and hydrophilic character, curvature and shape of the pocket.

Target Triage: computer-aided target selection (CATS), based on the importance of a gene for the organism, the occurrence of the gene in multiple target species, specificity or inhibition by reference to sequence similarity, and easiness of assay. The group of residues lining a ligand-binding site are of moreimportance than long-range interaction and conformational changes.

Target Validation: knock-out of the gene of interest or RNA antisense technology to inactivate the gene.Lead Identification: structural bioinformatics can be used for function and ligand prediction. Using structural similarity to find chemical leads (usually, when the proteins share less than 30% sequence identity the active sites are nonidentical).

Virtual screening: to derive a pharmacophore describing the functionally important sites in a ligand-binding site, and docking and scoring. Creating a chemical library.

Lead Optimization: repeated cycles of determining the structure of the target in complex with a number of lead compounds and their analogs. Structural bioinformatics should be used to design suitable constructs of the outset of the project. Alignment and secondary structure prediction on multiple alignments. Homology modeling and the use of a surrogate protein (an orthologous protein from another species or a similar member of the same gene family).

ADMET Modeling: additional parameters that can affect the biopharmaceutical and safety properties of the drug: in vivo absorption, distribution, metabolism, excretion, and toxicology. Sequence-structure relationship and protein homology modeling can be used in ADMET modeling.

Bioinformatics Market Is On Explosion

Author: James Marriot

In the current scenario, bioinformatics market size stands at approximately $840 million. It's poised for a rapid growth with increase of around $1.82 billion by 2007. The Compound Annual Growth Rate - CAGR of the Bioinformatics Market is likely to be around 15 % up till 2010.In the last few years, bioinformatics has achieved acceptance among other markets particularly biotechnology, pharmaceutical, agricultural biotechnology and industrial biotechnology.

The bioinformatics industry is among one of the largest sector within super-computing and stands to benefit appreciably from acceleration of FPGA-based applications. Bioinformatics involves annotation, recording, analysis, storage, and searching/extracting of molecular information such as protein sequences, gene sequences, cell activity and variation in genetic structures.

Bioinformatics is mainly used in area of life sciences like molecular medicine, drug discovery, microbial genome applications, comparative studies, agriculture and others. The contribution of Bioinformatics to these areas of life sciences is evident by its application in the research fields e.g. proteomics, genomics.

In the near future the contribution of genomics in the field of drug discovery and development is predicted to be one of the highest. Over the long haul, increasing number of new drugs designed would be genomic-related. However, after human genome mapping the use of proteomics is likely to rise at a higher rate than that of genomics, as it is proteins, not genes, which the researchers would use in the coming years for developing new drugs.

The growth of the Bioinformatics market is stable as 20% of the use is based upon proteomics and genomics. Many Research and development centers across the world are looking for a base to transfer the data, information and knowledge to facilitate an informatics-based decision support system.

However, biotechnology and pharmaceutical companies will continue to look at the tools for understanding the association between biological data types. As bioinformatics is broadly more useful to discovery and development, the end users will adopt solutions that streamline tracking, access, sharing of data and interpretation of different data types with least impact on resources. In consequence, hundreds of suppliers are embarking upon the bioinformatics bandwagon.

For further information about bioinformatics market please read the report "Bioinformatics Market Update (2006)" published by RNCOS at http://www.rncos.com/Report/COM31.htm

Bioinformatics data plays vital role: Kalam

Thiruvananthapuram, Sep 21:

Stressing that the convergence of Bioscience and Information Technology into Bioinformatics has given thrust to genomics-based drug discovery and development, President A P J Abdul Kalam said there was a growing need for new informatics tools to help manage the influx of data from genomics and turn that data into tomorrow's drugs.

''Bioinformatics data plays a vital role and is emerging as a business model for the medical and pharmaceutical sector. It has a major role in key areas such as gene prediction, data mining, protein structure modeling and prediction, protein folding and stability, macromolecular assembly and modeling of complex biological systems,'' he said during an Interaction with Siddha and Ayurvedic medical college students at the Santhigiri Ashram at Pothencode on the outskirts of the city.

Mr Kalam said the need of the hour was to network the existing facilities and expertise with commitment and conviction to augment and facilitate the pace of research and development in the field of Indian traditional medicine. ''Our country is rich in human resources, particularly of scientists, doctors, technologists and engineers. The basic infrastructure is available for advanced research.'' he added.

The President said that there were tremendous opportunities for technologists to work for the objective of ''Integrated Health For All'' in a mission mode approach.

Researchers Simulate Complete Structure of Virus -- On A Computer

In their quest to study life, biologists apply engineering knowledge somewhat differently: They "reverse engineer" life forms, test fly them in the computer, and see if they work in silico the way they do in vivo. This technique previously had been employed for small pieces of living cells, such as proteins, but not for an entire life form until now.

The accomplishment, performed by computational biologists at the University of Illinois at Urbana-Champaign and crystallographers at the University of California at Irvine, is detailed in the March issue of the journal Structure.

Deeper understanding of the mechanistic properties of viruses, the researchers say, could not only contribute to improvements in public health, but also in the creation of artificial nanomachines made of capsids -- a small protein shell that contains a viral building plan, a genome, in the form of DNA or RNA.

For their first attempt to reverse engineer a life form in a computer program, computational biologists selected the satellite tobacco mosaic virus because of its simplicity and small size.
The satellite virus they chose is a spherical RNA sub-viral agent that is so small and simple that it can only proliferate in a cell already hijacked by a helper virus -- in this case the tobacco mosaic virus that is a serious threat to tomato plants.

A computer program was used to reverse engineer the dynamics of all atoms making up the virus and a small drop of salt water surrounding it. The virus and water contain more than a million atoms altogether.

The necessary calculation was done at Illinois on one of the world's largest and fastest computers operated by the National Center for Supercomputing Applications. The computer simulations provided an unprecedented view into the dynamics of the virus.

The computer simulations were carried out in Schulten's Theoretical and Biophysics Group's lab at the Beckman Institute for Avanced Science and Technology.

Phasing Out Theoretical Model Depositions to the PDB Archive

Effective October 15, 2006, PDB depositions will be restricted to atomic coordinates that are substantially determined by experimental measurements on specimens containing biological macromolecules. This policy was recommended and endorsed by a working group comprised of structural and computational biologists and endorsed by the wwPDB advisory committee. Thus, theoretical model depositions (such as models determined purely in silico using, for example, homology or ab initio methods) will no longer be accepted.

Theoretical models that have been previously released or that will be released from now until October 15, 2006 will continue to be publicly available via the existing models archive at ftp://ftp.rcsb. org/pub/pdb/ data/structures/ models/current/.

A summary of the implementation plan for the phasing out of theoretical models is available in HTML and PDF formats. A paper describing the outcome of the Workshop on Archiving Structural Models of Biological Macromolecules will be available in the August 16 issue of Structure1.

Questions about this transition should be sent to info@wwpdb.org.

Systems Biology: the 21st Century Science

Systems biology is the study of an organism, viewed as an integrated and interacting network of genes, proteins and biochemical reactions which give rise to life. Instead of analyzing individual components or aspects of the organism, such as sugar metabolism or a cell nucleus, systems biologists focus on all the components and the interactions among them, all as part of one system. These interactions are ultimately responsible for an organism´s form and functions. For example, the immune system is not the result of a single mechanism or gene. Rather the interactions of numerous genes, proteins, mechanisms and the organism´s external environment, produce immune responses to fight infections and diseases.

Systems biology emerged as the result of the genetics "catalog" provided by the Human Genome project, and a growing understanding of how genes and their resulting proteins give rise to biological form and function.

A traditional approach to studying biology and human health has left us with a limited understanding of how the human body operates, and how we can best predict, prevent, or remedy potential health problems. Biologists, geneticists, and doctors have had limited success in curing complex diseases such as cancer, HIV because traditional biology generally looks at only a few aspects of an organism at a time.

The individual function and collective interaction of genes, proteins and other components in an organism are often characterized together as an interaction network. Indeed, understanding this interplay of an organism´s genome and environmental influences from outside the organism (nature and nurture) is crucial to developing a — systems — understanding of an organism that will ultimately transform our understanding of human health and disease.Systems biology is still in its infancy; we are at the turning point in our understanding of what the future holds for biology and human medicine.

Pharmacogenomics and personalized medicine: mapping of future value creation

People have been talking about personalized medicine, to a point that it almost gets old. Some are firm believers in this revolution and are actively devoting effort to advance it, others wonder whether this is yet another hype, and yet many others may have an impression that, as promising a future as it paints, it may only become reality in another lifetime.Personalized medicine addresses current unfilled needs in the healthcare world by calling for the right treatment for the right individual at the right time. In current drug therapeutics world, it is widely observed that a drug doesn't work for all the patients all of the time. Drugs do not have the desired outcome in 30%–40% of patients, blockbuster drugs are often efficacious in 40%–60% of the patients, and it is not unusual to see chemotherapy working for only 30% of cancer patients (from industry expert interviews, the American Medical Association. In addition, drugs can at times cause adverse drug reactions (ADRs), with some more severe than others.

There are different levels of variation among individuals that could account for the varying outcomes of drug therapy, such as patients' different drug absorption, distribution, metabolism, and excretion (ADME) profiles measurable at organ, tissue, or cellular levels and more fundamental differences at molecular levels, [i.e., analyses of protein, RNA (gene expression analysis), and DNA (genotyping)]. Genotyping and gene expression analysis are current key component technologies of pharmacogenomics (PGx) that currently serves as the major driving force for the personalized medicine revolution.

PERSONALIZED MEDICINE: WHEN WILL IT HAPPEN?

Nanoarrays

Scientists have developed a new type of protein array for studying interactions between proteins and other molecules on an extremely small scale. Size is what sets these arrays apart—they are built on a nano-scale (one nanometer equals one-billionth of a meter). The arrays are coated surfaces containing proteins that can be exposed to other proteins and structures in order to study their interactions.

"The ability to make protein nanoarrays on a surface with well-defined feature size, shape, and spacing should increase the capabilities of researchers studying the fundamental interactions between biological structures (cells, complementary proteins, and viruses) and surfaces patterned with proteins," the researchers write in Science Express. Chad A. Mirkin, of Northwestern University in Evanston, Illinois, led the research.

The method used to create the protein nanoarrays is called dip-pen lithography. This technique involves using an instrument to modify the surface of the arrays, which is a type of gold film. The dip-pen lithography allows researchers to create high-resolution patterns on the surface, thereby giving the arrays their sensitivity.

In a related study, Mirkin and two colleagues at Northwestern's Institute for Nanotechnology developed a new method of detecting small amounts of DNA in a solution. The method relies on an electrical current to indicate the presence of complementary strands of DNA. When complementary DNA strands bind, gold nanoparticles form a bridge that conducts an electric signal. A photographic solution containing silver can be added to further amplify the signal.

The Backrub Motion: How Protein Backbone Shrugs When a Sidechain Dances

Surprisingly, the frozen structures from ultra-high-resolution protein crystallography reveal a prevalent, but subtle, mode of local backbone motion coupled to much larger, two-state changes of sidechain conformation. This “backrub” motion provides an influential and common type of local plasticity in protein backbone. Concerted reorientation of two adjacent peptides swings the central sidechain perpendicular to the chain direction, changing accessible sidechain conformations while leaving flanking structure undisturbed. Alternate conformations in sub-1 Å crystal structures show backrub motions for two-thirds of the significant Cβ shifts and 3% of the total residues in these proteins (126/3882), accompanied by two-state changes in sidechain rotamer. The Backrub modeling tool is effective in crystallographic rebuilding. For homology modeling or protein redesign, backrubs can provide realistic, small perturbations to rigid backbones. For large sidechain changes in protein dynamics or for single mutations, backrubs allow backbone accommodation while maintaining H bonds and ideal geometry

Fold Recognition

Overview:

Fold recognition (FR) is the name give to the process of assigning a known structure (a template) to a sequence of unknown structure (the query). The methodologies used include

• Straight sequence methods (like BLAST and PSI-BLAST)
• Sequence methods incorporating structure, including:- predicted and known secondary structure- structural environments- structural alignments
• "True" threading approaches
  The difference between "sequence" based methods and methods using threading is not always clear. In principle the sequence based method defines the "fitness" of the query onto the template from on the primary structure of the query and template sequences, respectively. Threading methods on the other hand defines the "fitness" of the query from the structural environment of the template structure. However as you saw from the list above some sequence based methods also incorporates structural information of the template in the alignment so the borderline is not very clear. The most powerful method are neither "true" sequence based nor "true" threading method, but some mixture of the two.

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Strategy for the Drug Discovery

Comparative protein modelling

Comparative protein modelling uses previously solved structures as starting points, or templates. This is effective because it appears that although the number of actual proteins is vast, there is a limited set of tertiary structural motifs to which most proteins belong. It has been suggested that there are only around 2000 distinct protein folds in nature, though there are many millions of different proteins.

These methods may also be split into two groups:

Homology modelling is based on the reasonable assumption that two homologous proteins will share very similar structures. Given the amino acid sequence of an unknown structure and the solved structure of a homologous protein, each amino acid in the solved structure is mutated, computationally, into the corresponding amino acid from the unknown structure.

Protein threading scans the amino acid sequence of an unknown structure against a database of solved structures. In each case, a scoring function is used to assess the compatibility of the sequence to the structure, thus yielding possible three-dimensional models.

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