Spherical-harmonic decomposition for molecular recognition in electron-density maps
International Journal of Data Mining and Bioinformatics
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An important problem in high-throughput protein crys- tallography is constructing a protein model from an electron-density map. Previous work by some of this pa- per's authors [1] describes an automated approach to this otherwise time-consuming process. An important step in the previous method requires searching the density map for many small template protein-fragments. This previous ap- proach uses Fourier convolution to quickly match some ro- tation of the template to the entire density map. This pa- per proposes an alternate approach that makes use of the spherical-harmonic decomposition of the template and of some region in the density map. This new framework allows us to mask specific regions of the map, enabling a first-pass filter to eliminate a majority of the density map without re- quiring an expensive rotational search. We show our new template matching method improves accuracy and reduces running time, compared to our previous approach. Protein models constructed using this matching also show signifi- cant accuracy improvement.