1. A Geometric Clustering Algorithm
and Its Applications to Structural Data
Guido Muscioni
Lorenzo Semeria
Shuangxi zhu

2. Outline Context introduction Datasets description The algorithm
Results
From: “A Geometric Clustering Algorithm and Its Applications to Structural Data”, Shutan Xu, Shuxue Zou, and Lincong Wang

3. context
Incredible growth of complex structured data in molecular biology
Nuclear Magnetic Resonance (NMR) spectroscopy
Protein-ligand Docking
Molecular Dynamics (MD) simulations

4. Nuclear Magnetic Resonance spectroscopy
NMR machine at the École polytechnique fédérale de Lausanne
Based on magnetic property of atoms
Determine properties of:
Atoms
Molecules
Produces a unique firm for each molecules

5. Protein-ligand docking
Protein modelling technique
Predict the structure of a protein
Image from:

6. Molecular dynamics simulation
Based on strength between molecules
Computing and updating the position based on the iteractions between molecules
N-body simulation techniques

7. dataset NMR dataset Protein-ligand dataset Based on SiR5
Two set of intermediates:
Computed on 101 residues
Large amount of them
22 set of poses (retrieved by GOLD suite)
500 poses available for each initial protein-ligand complex

8. algorithm Cluster based algorithm RSDM
Similarity measure between two structure in the same created cluster.

9. Steps Check for dmax Seed another cluster Reclustering based on d
Create two more seeds
Initializing two cluster
Cluster the former
dcc<dmax
yes
Cluster the latter no

10. Number of structures Ns
results
Metrics:
Number of structures Ns
VDW energy
NOE violation

11. BASELINE
Complete link
Geometric clustering
Average link VS
K-medoid

12. Result-nmr
Complete link
Geometric clustering
Average link VS
K-medoid

13. Both show problems, the level of confidence of the results is not high
Result-Protein-lingand
Better identification of clusters
Sometimes fails in recognizing the best cluster
Geometric clustering
GOLD score VS
Both show problems, the level of confidence of the results is not high

14. Discussion Pros & What we liked Cons & What we disliked
Algorithm is not clearly explained (elements are assigned to new clusters with no apparent criteria)
Complexity is high (O (n^2 * log(n) ) )
A prior knowledge is needed to have a good result
The datasets may not be big enough to be realistic
The datasets may not represent real data#
Second part in the result section does not have a good description
It’s simple (it is a “standard” iterative clustering algorithm)
The results are better than other clustering algorithms for these problems
Not only implementing new algorithm, but It also came up with a relatively new scoring function for best cluster selection

15. Questions