ArticleAlgorithms for molecular biology : AMB2016
An efficient algorithm for protein structure comparison using elastic shape analysis.
Article in Algorithms for molecular biology : AMB, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A review of visualisations of protein fold networks and their relationship with sequence and function.Biological reviews of the Cambridge Philosophical Society · 2023Review
- Image-based effective feature generation for protein structural class and ligand binding prediction.PeerJ. Computer science · 2020Article
- Simplifying Transforms for General Elastic Metrics on the Space of Plane Curves.SIAM journal on imaging sciences · 2020Article
- Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia.BMC bioinformatics · 2018Article
- Genetic Code Optimization for Cotranslational Protein Folding: Codon Directional Asymmetry Correlates with Antiparallel Betasheets, tRNA Synthetase Classes.Computational and structural biotechnology journal · 2017Article
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7 authors.
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Abstract
backgroundProtein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques.
resultsAn efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80-90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free.
conclusionsThe methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy (Table 2). The proposed algorithm is considering different criteria of representing protein structures using 3D coordinates of atoms and inclusion of residue wise molecular properties as auxiliary information.
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