1 / 10

Seeing the results of a mutation with a vertex-weighted hierarchical graph

Seeing the results of a mutation with a vertex-weighted hierarchical graph. Debra Knisley and Jeff Knisley Institute for Quantitative Biology Department of Mathematics and Statistics East Tennessee State University Johnson City, TN USA. Reducing the clutter in the view.

raquel
Download Presentation

Seeing the results of a mutation with a vertex-weighted hierarchical graph

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Seeing the results of a mutation with a vertex-weighted hierarchical graph Debra Knisley and Jeff Knisley Institute for Quantitative Biology Department of Mathematics and Statistics East Tennessee State University Johnson City, TN USA

  2. Reducing the clutter in the view • Graphs have been used to represent protein structures where each amino acid is represented as a vertex and two vertices are connected by an edge in the graph if the corresponding residues are within a specified distance threshold. • Topological features of protein structures exhibit many desirable network properties such as high clustering coefficients and short average path lengths [2]. • However, due to the size of such graphs, this method of representing protein structures has not been an effective visualization tool,

  3. Reducing the clutter in the view The left graph is a graph of scTIM where each vertex is residue. Thus there are 247 vertices and a large number of edges. The graph On the right is a vertex-weighted representation of scTIM

  4. Obtaining the 19 vertices

  5. Creating a subdomain graph Two residues are connected in the graph representing a Subdomain if they are within 7 angstroms in the 3D structure

  6. Obtaining the edges If two residues are with 7 angstroms in the 3D PDB file, then we place an edge. If there are at least two edges between two sub Domains, the we connect the respective subdomains.

  7. Determining Vertex weights We modify the definition of some standard graphical invariants to include vertex weights. For example, the domination number of a graph is the minimum cardinality among all dominating sets in the graph. The weighted domination number is defined as the minimum sum of the weights of the vertices among All dominating sets. Weights for the amino acid structures: Weighted upper domination, weighted domination, weighted diameter, circumference, average weighted degree, weighted periphery, Plr, Chrg, Hydpthy, stablty, ss-stability, vanderWaal, chargetransf, chargedonar, averhydrophocitiy, coilconformation, IsoElectric, Balaban index, RofGyr, ShapeIndex, an EIIP

  8. Applying the weights Using Cytoscape to view the results, we can see the results that a change in a residue(s) can have on the structure of a protein. Assuming the close correlation of structure to function, we can determine which changes at the residue level have the potential to produce significant functional consequences.

  9. A set of potential drivers identified by the graph-theoretic approach • 1. N28K and T29Del • 2. V51R • 3. N78I, V80P, Q82M and K89D • 4. K107L, T113V, K114A, F115H • 5. G118E and Q119H • 6. L140E and D141E • 7. G197S, D198A, K199E,S202E and E203S

  10. References SaraswathiVishveshware, K. V. Brinda and N. Kannan, Protein Structures: Insights from graph theory, Journal of Theoretical and Computational Chemistry, Vol. 1, No. 1 (2002) 2. NikolayDokholyan, Lewlyn Lee, Feng Ding and Eugene Shakhnovich, Topological Determinants of Protein Folding, PNASJune 25, 2002 vol. 99 no. 13 3.Cytoscape, http://www.cytoscape.org

More Related