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![Geometry of a simplified molecule in two-dimensional space for illustration. (a) The molecule formed by the union of atom disks. Voronoi diagram is in dashed lines. (b) The shape enclosed by the boundary polygon is tessellated by the Delaunay triangulation. (c) The alpha shape of the molecule is formed by removing those Delaunay edges and triangles whose corresponding Voronoi edges and Voronoi vertices do not intersect with the body of the molecule. A molecular void can be seen, and is represented in the alpha shape by two empty triangles (Adapted from [5]).](profile/Jie-Liang-7/publication/40452151/figure/fig1/AS:341730521042955@1458486313984/Geometry-of-a-simplified-molecule-in-two-dimensional-space-for-illustration-a-The.png)
Geometry of a simplified molecule in two-dimensional space for illustration. (a) The molecule formed by the union of atom disks. Voronoi diagram is in dashed lines. (b) The shape enclosed by the boundary polygon is tessellated by the Delaunay triangulation. (c) The alpha shape of the molecule is formed by removing those Delaunay edges and triangles whose corresponding Voronoi edges and Voronoi vertices do not intersect with the body of the molecule. A molecular void can be seen, and is represented in the alpha shape by two empty triangles (Adapted from [5]).
Source publication
Proteins contain thousands or more atoms and have complex shapes. We discuss here the computation of protein packing defects, in the form of voids and pockets, from experimentally resolved protein structures, and the nature of their distribution and scaling behavior, as well as their origin. We further discuss how evolutionary selection pressure du...
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Citations
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Background
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Methods
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Conclusion
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Protein function and dynamics are closely related to its sequence and
structure. However prediction of protein function and dynamics from its
sequence and structure is still a fundamental challenge in molecular biology.
Protein classification, which is typically done through measuring the
similarity be- tween proteins based on protein sequence or physical
information, serves as a crucial step toward the understanding of protein
function and dynamics. Persistent homology is a new branch of algebraic
topology that has found its success in the topological data analysis in a
variety of disciplines, including molecular biology. The present work explores
the potential of using persistent homology as an indepen- dent tool for protein
classification. To this end, we propose a molecular topological fingerprint
based support vector machine (MTF-SVM) classifier. Specifically, we construct
machine learning feature vectors solely from protein topological fingerprints,
which are topological invariants generated during the filtration process. To
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unbound M2 channels. Additionally, we examine the use of MTF-SVM for the
classification of hemoglobin molecules in their relaxed and taut forms and
obtain about 80% accuracy. The identification of all alpha, all beta, and
alpha-beta protein domains is carried out in our next study using 900 proteins.
We have found a 85% success in this identifica- tion. Finally, we apply the
present technique to 55 classification tasks of protein superfamilies over 1357
samples. An average accuracy of 82% is attained. The present study establishes
computational topology as an independent and effective alternative for protein
classification.
