Structural Similarity Evaluation between Proteins Based on 3D Model Shape Analysis (3차원 형상 분석 기반 단백질 유사성 분석)

Sung Hwan Chun (전성환); Choi Yoo Joo (최유주); 서정근

doi:10.34163/jkits.2018.13.1.009

Structural Similarity Evaluation between Proteins Based on 3D Model Shape Analysis

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2018, 13(1), pp.87-103
DOI : 10.34163/jkits.2018.13.1.009
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Published : February 28, 2018

Sung Hwan Chun ¹, Choi Yoo Joo ¹, 서정근 ¹

¹서울미디어대학원대학교

Accredited

ABSTRACT

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.

KEYWORDS

Protein similarity evaluation, Protein surface model, Shape descriptor, root mean square deviation measure (RMSD), Small-angle X-ray scattering (SAXS)

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Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Structural Similarity Evaluation between Proteins Based on 3D Model Shape Analysis

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* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

Structural Similarity Evaluation between Proteins Based on 3D Model Shape Analysis

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