-- %TEACHINGWEB%.ThorChristianHobaek - 09 Sep 2009
Homework assignment 1
Table of Contents
1 Biographical text
Please see my
home page for information about me.
2 Links added using plugins
The following link to the Wikipedia page about the Polio vaccine has been auto-added to this site using the Interwiki Plugin:
Wikipedia:Polio_vaccine
Another link was added using the E Fetch Plugin:
3 Interesting Pubmed paper about the polio virus
This interesting article has some recorded findings that central nervous system (CNS) injury related to the polio virus is caused by apoptosis.
4 Relation between computational and synthetic biology
4.1 Computer models of cellular process networks
Computational biology is relevant for synthetic biology because it can be used to develop algorithms and computer simulations to build knowledge about connections between cellular processes. For example, computational models can be used to predict metabolism, gene regulatory networks and signal transduction pathways, which gives the synthetic biologist a lot of useful information prior to fabrication.
4.2 Protein structure prediction
Computational models are used to predict the three-dimensional protein structure from its amino acid sequence. One of these methods is the de novo or ab into methods, which uses physical principles (for example using a suitable energy function and finding its global minimum configuration) instead of referring to already known structures. Tertiary protein structures are important for synthesizing enzymes or drugs.
5 Differences between viruses and transposons
| Viruses |
Transposons |
| Has the ability to survive outside the cell because of the gag gene |
Lacks the gag gene, so it only stays inside it hosts cell |
| Can penetrate the cell membrane because of the env gene and therefore infect other cells or organisms |
Lacks the env gene and therefore cannot spread beyond it's host cell |
| Have a protein coat to protect the viral gene |
The gene moves around in the cell without any protection |
| Viral gene consists of either DNA or RNA |
The transposon is a DNA sequence |
The ability of a virus to intrude a cell and transfer and incorporate DNA sequences into a cell's genome, can be used for treatment of disease. By extracting an existing viral gene from the protein coat and replacing it by a desired gene, this gene can be incorporated into the cell by using the virus as a way for cellular entrance. The desired gene can then replace the mutant gene in the chromosomes and thereby treat the disease. The method is called gene therapy.
Transposon can be inserted into a host chromosome to induce a mutation, called mutagenesis.
A possible physical limitation of gene therapy may be the size of the modified viral RNA or DNA. The size of the gene and the encapsulating protein cap must be low enough to allow cellular entrance.
6 Bioinformatic tools
6.1 Protein structure prediction tools
| |
Ab initio protein structure prediction |
Comparative protein structure modeling |
| Description |
Estimates the tertiary protein structure from only the amino acid sequence. The technique is based on energetic principles that governs protein folding |
Use a set of tertiary structure motif as templates for solving the protein structure, thereby basing itself on previously solved structures or threading |
| Examples |
Rosetta@home, ROBETTA, Abalone, Bhageerath, CABS, Selvita Protein Modeling Platform |
WHAT IF, TIP-STRUCTFAST, SWISS-MODEL, ROBETTA, EasyModeller, MODELLER, LIBRA I, HHpred, Geno3D, GeneSilico, ESyPred3D, CPHModel, CABS, Biskit, 3D-JIGSAW |
| Accuracy |
5 Å |
A root mean square deviation (RMSD) of ~1 Å |
| Run-time |
Slow |
Fast |
| Memory usage |
Vast |
Low |
6.2 Gene function tools
| |
Controlled vocabularies for gene/enzyme function |
Biochemical pathway databases |
| Description |
Gene ontology and EC numbers. Gene ontology is a formal representation of concepts and terms related to genetic properties. EC numbers specify a enzyme-catalyzed reaction. Each EC number is associated with a recommended name of the involved enzymes. |
Databases of networks of biochemical pathways in cells down to molecular interactions. |
| Examples |
AmiGo, Obo-Edit, ENZYME and BRENDA |
KEGG, GeneDB, EcoCyc, BioCyc and MetaCyc |
6.3 Structure analysis tools
| |
Protein/protein docking software |
Protein/small-molecule docking software |
| Description |
Predicts whether two or more selected proteins will bind, what kind of spatial conformation the complex will have and how strong the binding between proteins are. Based on purely physical principles. |
Predicts the position and orientation of a small molecule, ligand, when it binds to a protein (enzyme or receptor). |
| Application |
Make it easier to design proteins to perform designated biological functions by knowing how it interacts. Can also be used to help understand why misfolded protein complexes cause genetic disease. |
Can help pharmaceutical researchers to find suitable drug candidates by making it possible to search through a database of target proteins. |
| Examples |
RosettaDock and AutoDock |
EADock, Autodock and Molecular Docking Server |
| Memory usage |
Huge |
Huge, but less than protein/protein docking software |
6.4 Sequence analysis tools
| |
Sequence assembly software |
Gene-finding software |
| Description |
Align and merges short fragments of a longer DNA sequence to reconstruct the original sequence. |
Identifies fragments of genomic DNA that are biologically important. The extrinsic approach and the ab initio approach are the two main methods. Extrinsic approach uses known mRNA or protein sequences and searches the genome for similar sequences. If a genome has a high degree of similarity of the known mRNA, it is a strong evidence that this region is a protein-coding gene. In ab initio approach a target genome is searched for protein-coded sequences without any knowledge about the mRNA structure. It is therefore a predictive tool. |
| Examples |
ABySS, AMOS, Arachne WGA, CAP3, PCAP |
Ensembl, RefSeq, BLAST, GLIMMER, GeneMark, GENSCAN |
6.5 RNA-structure tools
| |
RNA folding software |
RNA design software |
| Description |
Predicts the tertiary RNA structure from a known nucleic acid sequence, by first predicting the secondary structure, the base pairing within the RNA. |
Use RNA folding predictions to characterize RNA structures by their function, thus making it possible to design RNA molecules for therapeutic treatments. |
| Examples |
CentroidFold, CONTRAfold, KineFold, Mfold, Pknots, PknotsRG, RNAfold, RNAshapes, RNAstructure, Sfold, UNAFold |
RNAsoft, Sfold |

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