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PROTEIN STRUCTURE SIMILARITY CALCULATION AND VISUALIZATION CMPS 561-FALL 2014 SUMI SINGH SXS5729 Protein Structure 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. 2 Comparing protein 3D structuresget functional insight Structure of 1QLQ Structure of 4HHB Compare structures of two DIFFERENT proteins 3 Significance of comparing protein 3D structures Structural similarity between two proteins means functional similarities Predict drug interaction Predict binding site 4 Structural elements represented by quintuple of features {πΏππππ1 , πΏππππ2 , πΏππππ3 , π, π·} Labels represent Primary Structure (amino acids sequence) Tertiary/ 3D structure Theta represents orientation Length represents size/scale 5 Structural alphabet (key) generation Generate all possible triples of amino acids πΆ3π Assign labels to amino acids in triple Perform rule based label arrangement Label3 {πΏππππ1 , πΏππππ2 , πΏππππ3 , π, π·} Quintuple Mapping from structure space into unique key (integer space) d23 d13 Label1 d12 Calculate Angle and Length Label2 Representative Length (D) 6 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) Structure of 1QLQ TSR Key-Count Set representing 1QLQ Structure of 4HHB TSR Keys-Count Set representing 4HHB 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 11 Input to moduleA 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. There may be some keys that present in one protein while absent in other as they represent unique features. Output from moduleA You will be given a set of proteins and you have to calculate all by all pairwise similarity between them. Display/write the pairwise similarity between each protein file as lower triangular matrix for comparison purpose 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. 84 % 1A06 80% 84% 1AD5 85% 1FMK 85% 74% 74% 1FGK 74% 1ERK 83% 75% 75% 1CKI 15 Final system 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. Construct similarity graph. 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 http://gephi.github.io/ Information on Jaccard and Jaccard-Tanimoto can be found in the following paper: http://csis.pace.edu/ctappert/dps/d861-12/session4-p2.pdf Lower triangular matrix: http://en.wikipedia.org/wiki/Triangular_matrix