did our dear lecturers go for this one? xD
Link
Monday, March 17, 2008
Thursday, February 21, 2008
Virtual Human
Just read about this possibility of having a virtual human. I always thought this will be the ultimate goal of systems biology. However I have reservation on the actual use of the 'tool'. I sincerely hope it will be for the betterment of the human beings.
Monday, February 18, 2008
Biofuel... a friend or foe?
I kept coming across articles on biofuel recently. While it 'presented' itself as 'green', sometimes I wonder if it is really helping the environment. Nature news reported that 'biofuels might create more emissions than they save'. I believe there are some good things in biofuel but maybe we should seriously consider other modes -making use of the waste or readily available materials to create fuel than converting native land to agriculture for biofuel. No human can 'rebuild' the native land. Once it is lost, it is gone.
Human Proteinpedia
Finally... a central place for researchers to annotate and share protein information. I just read about the article in Feb 2008 Nature Biotechnology. The long list of authors with the who and who in the proteomics fields should enable Human Proteinpedia to be useful and updated.
Thursday, January 31, 2008
Structural DNA Nanotechnology
Few weeks ago, I posted an entry on DNA origami. I thought the idea was neat and creative but little did I know there are already a lot of other stuffs going on in the world.
The field - Structural DNA Nanotechnology (SDN). The person - Paul Rathemund. He has two beautiful smiley faces on his website. Quoted from his Nature paper, "an obvious application of patterned DNA origami would be the creation of a 'nanobreadboard', to which diverse components could be added. The attachment of proteins, for example, might allow novel biological experiments.."
Just few days ago, I come across this exciting news - a group of scientists at Arizona State University's Biodesign Institute have developed the world's first gene detection platform made up entirely from self-assembled DNA nanostructures. This 'breakthrough' make possible a water soluble nanoarray which can help to build a more sensitive DNA probes.
The field - Structural DNA Nanotechnology (SDN). The person - Paul Rathemund. He has two beautiful smiley faces on his website. Quoted from his Nature paper, "an obvious application of patterned DNA origami would be the creation of a 'nanobreadboard', to which diverse components could be added. The attachment of proteins, for example, might allow novel biological experiments.."
Just few days ago, I come across this exciting news - a group of scientists at Arizona State University's Biodesign Institute have developed the world's first gene detection platform made up entirely from self-assembled DNA nanostructures. This 'breakthrough' make possible a water soluble nanoarray which can help to build a more sensitive DNA probes.
Wednesday, December 12, 2007
Secondary protein structure prediction
Secondary structure means?
In biochemistry and structural biology,secondary structure is the general three-dimensional form of local segments of biopolymers such as proteins and nucleic acids (DNA/RNA).
It does not, however, describe specific atomic positions in three-dimensional space, which are considered to be tertiary structure.
Protein Structure Prediction
-One of the most important goals pursued by bioinformatics and theoretical chemistry.
-Aim is to predict the three-dimensional structure of proteins from their amino acid sequences, sometimes including additional relevant information such as the structures of related proteins.
-It deals with the prediction of a protein’s tertiary structure from its primary structure.
-High importance in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).
Some Examples of predictions are:
-Ab initio protein modelling
(Ab initio protein modelling methods seek to build three-dimensional protein models "from scratch", i.e., based on physical principles rather than (directly) on previously solved structures.)
-Comparative protein modelling
o Homology modelling (based on the reasonable assumption that two homologous proteins will share very similar structures.)
oProtein threading (scans the amino acid sequence of an unknown structure against a database of solved structures)
-Side Chain geometry prediction.
(Even structure prediction methods that are reasonably accurate for the peptide backbone often get the orientation and packing of the amino acid side chains wrong.
Methods that specifically address the problem of predicting side chain geometry include dead-end elimination and the self-consistent mean field method. Both discretize the continuously varying dihedral angles that determine a side chain's orientation relative to the backbone into a set of rotamers with fixed dihedral angles. The methods then attempt to identify the set of rotamers that minimize the model's overall energy. Rotamers are the side chain conformations with low energy. Such methods are most useful for analyzing the protein's hydrophobic core, where side chains are more closely packed; they have more difficulty addressing the looser constraints and higher flexibility of surface residues.)
In biochemistry and structural biology,secondary structure is the general three-dimensional form of local segments of biopolymers such as proteins and nucleic acids (DNA/RNA).
It does not, however, describe specific atomic positions in three-dimensional space, which are considered to be tertiary structure.
Protein Structure Prediction
-One of the most important goals pursued by bioinformatics and theoretical chemistry.
-Aim is to predict the three-dimensional structure of proteins from their amino acid sequences, sometimes including additional relevant information such as the structures of related proteins.
-It deals with the prediction of a protein’s tertiary structure from its primary structure.
-High importance in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).
Some Examples of predictions are:
-Ab initio protein modelling
(Ab initio protein modelling methods seek to build three-dimensional protein models "from scratch", i.e., based on physical principles rather than (directly) on previously solved structures.)
-Comparative protein modelling
o Homology modelling (based on the reasonable assumption that two homologous proteins will share very similar structures.)
oProtein threading (scans the amino acid sequence of an unknown structure against a database of solved structures)
-Side Chain geometry prediction.
(Even structure prediction methods that are reasonably accurate for the peptide backbone often get the orientation and packing of the amino acid side chains wrong.
Methods that specifically address the problem of predicting side chain geometry include dead-end elimination and the self-consistent mean field method. Both discretize the continuously varying dihedral angles that determine a side chain's orientation relative to the backbone into a set of rotamers with fixed dihedral angles. The methods then attempt to identify the set of rotamers that minimize the model's overall energy. Rotamers are the side chain conformations with low energy. Such methods are most useful for analyzing the protein's hydrophobic core, where side chains are more closely packed; they have more difficulty addressing the looser constraints and higher flexibility of surface residues.)
MudPIT
Introduction
Before Multidimensional Protein Identification Technology (MudPIT) came about, Liquid Chromatography (LC) and Mass Spectrometry (MS) are used separately to fractionate and then identify protein composition from biological sample.
Disadvantage of using Liquid Chromatography
- Loss of material that commonly occurs in chromatographic processes
Disadvantage of using Mass Spectrometry (gel-based)
Although gel-based methods are widely used when it comes to the identification of protein, this method has several drawbacks:
- Problem identifying hydrophobic proteins
- Difficulty detecting low-level proteins (dye staining is not sensitive)
- Long experiment duration
- Inability to be automated
- Biological sample need to undergo solubilization
These problem decrease the sensitivity of the protein identification process and MudPIT seeks to address these problems by improving the separation and identification of proteins.
So what is MudPIT?
Multidimensional Protein Identification Technology, or MudPIT is a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm.
Advantage of MudPIT
- Eliminate the problems of gel-based approach when it comes to MS
- More sensitive and thus able to detect low abundance proteins
- two-dimensional chromatography technique reduces sample loss
Workflow of MudPIT
1. Preparation of protein sample
2. Digest protein sample to peptides
3. Peptides are then seperated into two liquid column chromatography steps:
-strong cationic exchange
-reversed-phase high performance liquid chromatography (HPLC)
3. Acquire tandem mass spectra of peptide
4. Search mass spectra against a protein sequence database
5. Identification of protein in the sample using SEQUEST
source:
Technologies and Strategies for Reseach and Development
DRUG Discovery & Development
Before Multidimensional Protein Identification Technology (MudPIT) came about, Liquid Chromatography (LC) and Mass Spectrometry (MS) are used separately to fractionate and then identify protein composition from biological sample.
Disadvantage of using Liquid Chromatography
- Loss of material that commonly occurs in chromatographic processes
Disadvantage of using Mass Spectrometry (gel-based)
Although gel-based methods are widely used when it comes to the identification of protein, this method has several drawbacks:
- Problem identifying hydrophobic proteins
- Difficulty detecting low-level proteins (dye staining is not sensitive)
- Long experiment duration
- Inability to be automated
- Biological sample need to undergo solubilization
These problem decrease the sensitivity of the protein identification process and MudPIT seeks to address these problems by improving the separation and identification of proteins.
So what is MudPIT?
Multidimensional Protein Identification Technology, or MudPIT is a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm.
Advantage of MudPIT
- Eliminate the problems of gel-based approach when it comes to MS
- More sensitive and thus able to detect low abundance proteins
- two-dimensional chromatography technique reduces sample loss
Workflow of MudPIT
1. Preparation of protein sample
2. Digest protein sample to peptides
3. Peptides are then seperated into two liquid column chromatography steps:
-strong cationic exchange
-reversed-phase high performance liquid chromatography (HPLC)
3. Acquire tandem mass spectra of peptide
4. Search mass spectra against a protein sequence database
5. Identification of protein in the sample using SEQUEST
Some projects using MudPIT:
1) Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae
2)Food Standards Agency
This site is researching on the feasibility of using MudPIT as an alternative to gel-based approach for the rigorous safety assessment of GM plants.
3)Chloroplast proteomics: potentials and challenges
This is a site on botany research, this research is about the analysis of chloroplast proteome.
source:
Technologies and Strategies for Reseach and Development
DRUG Discovery & Development
http://www.hupo.org/educational/past_congresses/2007_seoul/3_MacCoss_color.pdf
Nature Publishing Group
posted by Alvin
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