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A parametrically constrained optimization method for fitting sedimentation velocity experiments.
Gorbet, Gary; Devlin, Taylor; Hernandez Uribe, Blanca I; Demeler, Aysha K; Lindsey, Zachary L; Ganji, Suma; Breton, Sabrah; Weise-Cross, Laura; Lafer, Eileen M; Brookes, Emre H; Demeler, Borries.
Afiliação
  • Gorbet G; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Devlin T; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Hernandez Uribe BI; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Demeler AK; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Lindsey ZL; Texas A&M University, Department of Mechanical Engineering, College Station, Texas.
  • Ganji S; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Breton S; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Weise-Cross L; University of North Carolina at Chapel Hill, Department of Pathology and Laboratory Medicine, Chapel Hill, North Carolina.
  • Lafer EM; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Brookes EH; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas.
  • Demeler B; The University of Texas Health Science Center at San Antonio, Department of Biochemistry, San Antonio, Texas. Electronic address: demeler@biochem.uthscsa.edu.
Biophys J ; 106(8): 1741-50, 2014 Apr 15.
Article em En | MEDLINE | ID: mdl-24739173
ABSTRACT
A method for fitting sedimentation velocity experiments using whole boundary Lamm equation solutions is presented. The method, termed parametrically constrained spectrum analysis (PCSA), provides an optimized approach for simultaneously modeling heterogeneity in size and anisotropy of macromolecular mixtures. The solutions produced by PCSA are particularly useful for modeling polymerizing systems, where a single-valued relationship exists between the molar mass of the growing polymer chain and its corresponding anisotropy. The PCSA uses functional constraints to identify this relationship, and unlike other multidimensional grid methods, assures that only a single molar mass can be associated with a given anisotropy measurement. A description of the PCSA algorithm is presented, as well as several experimental and simulated examples that illustrate its utility and capabilities. The performance advantages of the PCSA method in comparison to other methods are documented. The method has been added to the UltraScan-III software suite, which is available for free download from http//www.ultrascan.uthscsa.edu.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Ultracentrifugação / Algoritmos Tipo de estudo: Health_economic_evaluation Limite: Animals Idioma: En Revista: Biophys J Ano de publicação: 2014 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Ultracentrifugação / Algoritmos Tipo de estudo: Health_economic_evaluation Limite: Animals Idioma: En Revista: Biophys J Ano de publicação: 2014 Tipo de documento: Article