Clustering of peptide fragment structures reveals nature’s building block approach. Ashish V. Tendulkar Research Scholar Kanwal Rekhi School of I.T. I.I.T. Bombay. Guide: Prof. P. Wangikar Co-guide: Prof. Sunita Sarawagi. Outline. Terms Objectives Approach Results Conclusion. Terms.
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Ashish V. Tendulkar
Kanwal Rekhi School of I.T.
Guide: Prof. P. Wangikar
Co-guide: Prof. Sunita Sarawagi
All overlapping octapeptide fragments from PDB_95 building block approach
Geometric invariant based representation of each peptide as a point in 56-dimensional space and clustering
Dense cluster of peptides in a 56-dimensional box
Training regime to decide the tolerance window Wi in each dimension based on known superimposable peptides.
Categorization of clusters
“Functional” clusters with majority of peptides drawn from a single SCOP superfamily.
Hierarchical clustering based on closeness of centroinds of clusters
C building block approach4
b) Tetrahedron_gap_1: constructed from alternate C atoms.
a) Tetrahedron_gap_0: constructed from consecutive C atoms.
c) Geometric invariants associated with a tetrahedron
Distribution of clusters By Information Content building block approach
Distribution of clusters By Cluster size.
No. of clusters
No. of clusters
Avg. information content of the cluster
No. of peptides in a cluster
Structural Clusters building block approach
Twisted -strand (S.22.214.171.124.389)
Known -hairpin (S.126.96.36.199.19)
Functional Clusters building block approach
Acid Proteases: Active site loop conformation I (F.b.188.8.131.52.7870)
Acid Proteases: Active site loop conformation II (F.b.184.108.40.206.3460)