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2018-08-21 18:41pm
학회 논문지
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발간년도 : [2018]

 
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논문명(한글) [Vol.13, No.1] Structural Similarity Evaluation between Proteins Based on 3D Model Shape Analysis
논문투고자 Sung-Hwan Chun, Yoo-Joo Choi, Jung-Keun Suh
논문내용 Over 130,000 protein structures are solved and deposited in the PDB with molecular structural models of high resolution determined by X-ray diffraction or NMR studies. Those conventional methods for structural determination and similarity comparison are not applicable for therapeutic proteins due to their own technical limitations. However, assessing structural comparability for therapeutic proteins is critical during biopharmaceutical development or manufacturing processes. Currently, those assessments were done by various spectroscopic methods but those methods are not giving specific structural information. Moreover, recent developments such as single angle X-ray scattering (SAXS) techniques can provide low-resolution structural models for proteins more easily than those for X-ray diffraction or NMR studies. These enable us to develop fast and reliable method for the similarity assessment of low-resolution surface models of therapeutic proteins using geometric shape descriptor. Our descriptor consists of two features, local and global. The local feature calculates the distance distribution for each vertex from the center in 510 bins. For global feature, the ratio, x to y axis and x to z axis from the surface model of proteins were determined. A geometric shape descriptor is then constructed by combining local and global features with weights. We applied this geometric shape descriptor to assess structural similarity for the therapeutic protein, insulin models. Our geometric shape descriptor can clearly classify human insulin models and insulin analog models which having locally different structures. The performance of the geometric shape descriptor was evaluated by comparing to the conventional method of the root mean square deviation measure (RMSD) which computes the minimum average distance between the backbones of superimposed. The result shows that the performance of the global shape descriptor is comparable to that of RMSD. This provides potential applications to classify protein structure and compare low-resolution protein structures for EM and SAXS.
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