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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Clustering of peptide fragment structures reveals nature’s building block approach
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
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
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
Distribution of clusters By Cluster size.
No. of clusters
No. of clusters
Avg. information content of the cluster
No. of peptides in a cluster
Twisted -strand (S.184.108.40.206.389)
Known -hairpin (S.220.127.116.11.19)
Acid Proteases: Active site loop conformation I (F.b.18.104.22.168.7870)
Acid Proteases: Active site loop conformation II (F.b.22.214.171.124.3460)