Using AutoDock

AutoDock is a suite of automated docking tools. It is designed to predict how small molecules, such as substrates or drug candidates, bind to a receptor of known 3D structure.AutoDock actually consists of two main programs: AutoDock performs the docking of the ligand to a set of grids describing the target protein; AutoGrid pre-calculates these grids.In addition to using them for docking, the atomic affinity grids can be visualised. This can help, for example, to guide organic synthetic chemists design better binders.

Autodock Docking

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A Bioinformatics Approach to Clinical Sequencing (Lecture)

Dr. Rhodes has a 12-year track record in cancer genomics and bioinformatics, with a significant focus on building systems to enable cutting-edge cancer research. He founded a successful cancer genomics and informatics company, Compendia Bioscience, recently acquired by Life Technologies. To build Compendia, he recruited and trained a talented team of 30 clinical scientists, bioinformaticists, cancer biologists, data curators and software engineers. As a graduate student, he led the development of Oncomine (Rhodes et al, PNAS, 2004; Rhodes et al, Nature Genetics, 2005), a cancer microarray database and data-mining application. Through the use of Oncomine, he contributed to several important discoveries, including the discovery of gene fusions in prostate cancer (Tomlins, Rhodes, et al., Science, 2005), SPINK1 in prostate cancer (Tomlins, Rhodes et al, Cancer Cell, 2008) and AGTR1 in breast cancer (Rhodes et al, PNAS, 2009). Compendia has since extensively expanded the Oncomine platform, and Oncomine is now widely recognized in academia and pharma as the leading cancer genomics data mining resource, with several hundred citations, and 15,000+ users. As Head of Medical Science Informatics at Life Technologies, he has turned his focus to next-generation sequencing data analysis, and leads several collaborations with pharmaceutical companies aimed at discovering novel oncogenic mutations and gene fusions from thousands of exomes and transcriptomes from The Cancer Genome Atlas. In his role as an Adjunct Assistant Professor at the U. of Michigan, he contributes to cancer genome analyses of prostate cancers (Grasso et al, Nature, 2012) and participates in and advises the clinical sequencing initiative MI-ONCOSEQ. (Ref:Bioconference)

Slides

Eukaryotic translation Animation

Eukaryotic translation is the process by which messenger RNA is translated into proteins in eukaryotes.
Initiation The cap-dependent initiation

Initiation of translation involves the interaction of certain key proteins with a special tag bound to 5'-end of an mRNA molecule. The protein factors bind the small ribosomal subunit (also referred to as the 40S subunit), and these initiation factors hold the mRNA in place. The eukaryotic Initiation Factor 3 (eIF3) is associated with the small ribosomal subunit, and plays a role in keeping the large ribosomal subunit from prematurely binding. eIF3 also interacts with the eIF4F complex which consists of three other initiation factors: eIF4A, eIF4E and eIF4G. eIF4G is a scaffolding protein which directly associates with both eIF3 and the other two components. eIF4E is the cap-binding protein. It is the rate-limiting step of cap-dependent initiation, and is often cleaved from the complex by some viral proteases to limit the cell's ability to translate its own transcripts. This is a method of hijacking the host machinery in favor of the viral (cap-independent) messages. eIF4A is an ATP-dependent RNA helicase, which aids the ribosome in resolving certain secondary structures formed by the mRNA transcript. There is another protein associated with the eIF4F complex called the Poly-A Binding Protein (PABP), which binds the poly-A tail of most eukaryotic mRNA molecules. This protein has been implicated in playing a role in circularization of the mRNA during translation.

This pre-initiation complex (43S subunit, or the 40S and mRNA) accompanied by the protein factors move along the mRNA chain towards its 3'-end, scanning for the 'start' codon (typically AUG) on the mRNA, which indicates where the mRNA will begin coding for the protein. In eukaryotes and archaea, the amino acid encoded by the start codon is methionine. The initiator tRNA charged with Met forms part of the ribosomal complex and thus all proteins start with this amino acid (unless it is cleaved away by a protease in subsequent modifications). The Met-charged initiator tRNA is brought to the P-site of the small ribosomal subunit by eukaryotic Initiation Factor 2 (eIF2). It hydrolyzes GTP, and signals for the dissociation of several factors from the small ribosomal subunit which results in the association of the large subunit (or the 60S subunit). The complete ribosome (80S) then commences translation elongation, during which the sequence between the 'start' and 'stop' codons is translated from mRNA into an amino acid sequence -- thus a protein is synthesized.

The cap-independent initiation

The best studied example of the cap-independent mode of translation initiation in eukaryotes is the Internal Ribosome Entry Site IRES approach. What differentiates cap-independent translation from cap-dependent translation is that cap-independent translation does not require the ribosome to start scanning from the 5' end of the mRNA cap until the start codon. The ribosome can be trafficked to the start site by ITAFs (IRES trans-acting factors) bypassing the need to scan from the 5' end of the untranslated region of the mRNA. This method of translation has been recently discovered, and has found to be important in conditions that require the translation of specific mRNAs, despite cellular stress or the inability to translate most mRNAs. Examples include factors responding to apoptosis, stress-induced responses.

Neighbor-Joining Tree in phylogenetic analysis

In bioinformatics, neighbor joining is a bottom-up clustering method for the creation of phenetic trees (phenograms), created by Naruya Saitou and Masatoshi Nei. Usually used for trees based on DNA or protein sequence data, the algorithm requires knowledge of the distance between each pair of taxa (e.g., species or sequences) to form the tree Neighbor joining takes as input a distance matrix specifying the distance between each pair of taxa. The algorithm starts with a completely unresolved tree, whose topology corresponds to that of a star network, and iterates over the following steps until the tree is completely resolved and all branch lengths are known: Based on the current distance matrix calculate the matrix Find the pair of taxa for which has its lowest value. Add a new node to the tree, joining these taxa to the rest of the tree. In the figure at right, f and g are joined to the tree by the new node u. Calculate the distance from each of the taxa in the pair to this new node. Calculate the distance from each of the taxa outside of this pair to the new node. Start the algorithm again, replacing the pair of joined neighbors with the new node and using the distances calculated in the previous step.

Dotplot plotting

Dot plot is a graphical method that allows the comparison of two biological sequences and identify regions of close similarity between them.

Introduction One way to visualize the similarity between two protein or nucleic acid sequences is to use a similarity matrix, known as a dot plot. These were introduced by Gibbs and McIntyre in 1970 and are two-dimensional matrices that have the sequences of the proteins being compared along the vertical and horizontal axes. For a simple visual representation of the similarity between two sequences, individual cells in the matrix can be shaded black if residues are identical, so that matching sequence segments appear as runs of diagonal lines across the matrix. Some idea of the similarity of the two sequences can be gleaned from the number and length of matching segments shown in the matrix. Identical proteins will obviously have a diagonal line in the center of the matrix. Insertions and deletions between sequences give rise to disruptions in this diagonal. Regions of local similarity or repetitive sequences give rise to further diagonal matches in addition to the central diagonal. Because of the limited protein alphabet, many matching sequence segments may simply have arisen by chance. One way of reducing this noise is to only shade runs or 'tuples' of residues, e.g. a tuple of 3 corresponds to three residues in a row. This is effective because the probability of matching three residues in a row by chance is much lower than single-residue matches. It can be seen from Figures 3.3h,c that the number of diagonal runs in the matrix has been considerably reduced by looking for 2-tuples or 3-tuples.

Python for bioinformatics