PROTEIN STRUCTURE SIMILARITY CALCULATION AND VISUALIZATION CMPS 561-FALL 2014 SUMI SINGH SXS5729

Protein Structure 2 RPDFCLEPPYAGACRARIIRYFYNAKAGLCQ Primary Structure Sequence of Amino Acids. Not enough for functional prediction. Tertiary Structure (3D Structure) Formed by 3D folding pattern of the protein. It makes protein functional.

Comparing protein 3D structures- get functional insight 3 Structure of 1QLQ Structure of 4HHB Compare structures of two DIFFERENT proteins

Significance of comparing protein 3D structures Structural similarity between two proteins means functional similarities Predict binding site Predict drug interaction 4

Structural elements represented by quintuple of features 5 Labels represent Primary Structure (amino acids sequence) Theta represents orientation Length represents size/scale Tertiary/ 3D structure

Structural alphabet (key) generation 6 Assign labels to amino acids in triple Perform rule based label arrangement Calculate Angle and Length Quintuple Label 2 Label 1 d13 Label 3 d23 d12 θ1θ1 Representative Length (D) Mapping from structure space into unique key (integer space)

Output of the key generation system For every protein millions of keys are generated each representing some special feature. The protein structure is represented and stores as unique KEY-COUNT pair.

Learning goals

Familiarizing with complex research problem and the process of solving it including reading and understanding published research papers and using them in problem solving. Parallel implementation of algorithm(s) and demonstrate the speedup from serial to parallel. Visualizing the output.

Task Outline

Calculate pairwise similarity between two proteins implemented in PARALLEL (moduleA) 11 Similarity Computation Jaccard Coefficient that allows (unique or count={0,1}) set as its arguments Jaccard-Tanimoto Coefficient that allows multi-sets (count>1) as its arguments TSR Key-Count Set representing 1QLQ Structure of 1QLQ TSR Keys-Count Set representing 4HHB Structure of 4HHB

Input to moduleA There may be some keys that present in one protein while absent in other as they represent unique features. All input files will be given as key-count pairs that will be the input to the system. Keys are integers representing the unique structural feature. All keys for a given protein will have corresponding count >=1.

Output from moduleA Display/write the pairwise similarity between each protein file as lower triangular matrix for comparison purpose You will be given a set of proteins and you have to calculate all by all pairwise similarity between them.

Input to moduleB or visualization module and the output The all by all pairwise similarity calculated in moduleA will be used as input to moduleB. Output should be connectivity graph (as shown in next slide) between all proteins. Each edge must display the similarity value. Preferred output will be each edge length weighted as similarity value between the two connecting proteins.

Construct structural similarity graph (moduleB) Method for finding the global structural connectivity between proteins that contain a specific domain of interest. 15

Final system Construct similarity graph. Should integrate moduleA and moduleB. If given a set of proteins should be able to find all by all similarity between them, display the lower triangular similarity matrix.

What do you get from me?

1.Training protein structure (key-count) file with their precalcuated similarity values, both Jaccard and Jaccard Tanimoto -- around 50 proteins -- you can use these to evaluate your system 2. Test set (50 proteins), only key-count pairs and no similarity values. 3. All the files will be text files. 4. Time taken by me to calculate the all by all similarity on the test and training set using an optimized serial algorithm for comparison with your parallel implementation.

You can use Hadoop-mapreduce for moduleA. Visualization can be done on GEPHI Information on Jaccard and Jaccard-Tanimoto can be found in the following paper: Lower triangular matrix: