Участник:Andrey Ryazanov
Материал из MachineLearning.
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=== Весна 2016, 16-й семестр === | === Весна 2016, 16-й семестр === | ||
| - | '''Inverse Protein Folding Problem via Quadratic | + | '''Inverse Protein Folding Problem via Quadratic Programming''', Рязанов А. В., Карасиков М. Е., Грудинин С. В. |
This paper presents a method of reconstruction a primary structure of a protein that folds into a given geometrical shape. This method predicts the primary structure of a protein and restores its linear sequence of amino acids in the polypeptide chain using the tertiary structure of a molecule. Unknown amino acids are determined according to the principle of energy minimization. This study represents inverse folding problem as a quadratic optimization problem and uses different relaxation techniques to reduce it to the problem of convex optimizations. Computational experiment compares the quality of these approaches on real protein structures. | This paper presents a method of reconstruction a primary structure of a protein that folds into a given geometrical shape. This method predicts the primary structure of a protein and restores its linear sequence of amino acids in the polypeptide chain using the tertiary structure of a molecule. Unknown amino acids are determined according to the principle of energy minimization. This study represents inverse folding problem as a quadratic optimization problem and uses different relaxation techniques to reduce it to the problem of convex optimizations. Computational experiment compares the quality of these approaches on real protein structures. | ||
Версия 10:54, 19 сентября 2016
Рязанов Андрей Владимирович
МФТИ, ФУПМ
Кафедра "Интеллектуальные системы"
Направление "Интеллектуальный анализ данных"
andrei.ryazanov@phystech.edu
Отчет о научно-исследовательской работе
Весна 2016, 16-й семестр
Inverse Protein Folding Problem via Quadratic Programming, Рязанов А. В., Карасиков М. Е., Грудинин С. В.
This paper presents a method of reconstruction a primary structure of a protein that folds into a given geometrical shape. This method predicts the primary structure of a protein and restores its linear sequence of amino acids in the polypeptide chain using the tertiary structure of a molecule. Unknown amino acids are determined according to the principle of energy minimization. This study represents inverse folding problem as a quadratic optimization problem and uses different relaxation techniques to reduce it to the problem of convex optimizations. Computational experiment compares the quality of these approaches on real protein structures.
Публикация:
Ryazanov A., Karasikov M., Grudinin S. Inverse Protein Folding Problem via Quadratic Programming, принята к публикации в сборнике трудов конференции ИТиС 2016.
Доклады:
Ryazanov A., Karasikov M., Grudinin S. Inverse Protein Folding Problem via Quadratic Programming, Традиционная молодежная Школа "Управления, информация и оптимизация".
Гранты:
РФФИ 16-37-00111
