PPubMed29 Sep 2016Cited 16×

An efficient algorithm for protein structure comparison using elastic shape analysis

S. Srivastava, S. B. Lal, D. C. Mishra, U. B. Angadi, K. K. Chaturvedi, S. N. Rai, A. Rai

Abstract

The concept of shape elastic metric has been employed for calculating deformation and quantifying the difference between two 3D structures of proteins. This concept of shape and shape metric was developed by Kendall for quantification and modelling of shapes. This includes analysis of shapes, detecting and tracking patterns in the images, classification and clustering of images, finding trajectory and path of objects, morphological changes in objects, etc. Further, it has been observed that SRVF and elastic metric performed better in comparison to their counterparts during its applications in

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A new elastic‑shape‑analysis algorithm speeds up protein structure comparison by 80‑90% while keeping accuracy comparable to existing methods.

What’s new

i.Developed an efficient elastic shape analysis algorithm using SRVF and auxiliary side‑chain properties
ii.Implemented the method as an R package and a web‑based tool called ProtSComp
iii.Showed 80‑90% reduction in runtime with no loss of accuracy versus CE, jFATCAT, and ESA
iv.Integrated molecular properties (hydrophobicity, polarity, mass, etc.) to improve alignment

How they did it

Protein structures are encoded as square‑root velocity functions (SRVF) that include 3D coordinates and side‑chain information. Optimal rotation is obtained via singular value decomposition and optimal matching via dynamic programming, after which a geodesic distance serves as the dissimilarity score. The algorithm was implemented in R, packaged, and evaluated on two SCOP benchmark datasets against CE, jFATCAT, and ESA using clustering metrics.

What they found

Running time reduced by 80‑90% compared with CE, jFATCAT and ESA
Classification accuracy up to 94.11% when using Cα coordinates plus hydrophobicity
Precision, recall, F‑measure and Rand index improved for several variants (e.g., RI = 0.9557 for ESA‑MC‑BB + HP)

Caveats

✕Clustering results depend on the chosen clustering algorithm, so performance is not unique
✕For very small proteins (≤100 residues) other methods like MUSTANG can be slightly faster
✕The method still relies on heuristic steps such as dynamic‑programming alignment

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