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USING AN ABSTRACTION OF AMINO ACID TYPES TO IMPROVE THE QUALITY OF STATISTICAL POTENTIALS FOR PROTEIN STRUCTURE PREDICTION
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  • USING AN ABSTRACTION OF AMINO ACID TYPES TO IMPROVE THE QUALITY OF STATISTICAL POTENTIALS FOR PROTEIN STRUCTURE PREDICTION
  • USING AN ABSTRACTION OF AMINO ACID TYPES TO IMPROVE THE QUALITY OF STATISTICAL POTENTIALS FOR PROTEIN STRUCTURE PREDICTION
저자명
Lee. Jin-Woo
간행물명
Journal of the Korean society for industrial and applied mathematics
권/호정보
2011년|15권 3호|pp.191-199 (9 pages)
발행정보
한국산업응용수학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

In this paper, we adopt a position specific scoring matrix as an abstraction of amino acid type to derive two new statistical potentials for protein structure prediction, and investigated its effect on the quality of the potentials compared to that derived using residue specific amino acid identity. For stringent test of the potential quality, we carried out folding simulations of 91 residue A chain of protein 2gpi, and found unexpectedly that the abstract amino acid type improved the quality of the one-body type statistical potential, but not for the two-body type statistical potential which describes long range interactions. This observation could be effectively used when one develops more accurate potentials for structure prediction, which are usually involved in merging various one-body and many-body potentials.