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A dimensionality reduction approach to modeling protein flexibility

A dimensionality reduction approach to modeling protein flexibility

A dimensionality reduction approach to modeling protein flexibility. By Miguel L. Teodoro , George N. Phillips J* and Lydia E. Kavraki Rice University and University of Wisconsin-Madison* Presented by Zhang Jingbo. Outline. Motivation, Background and Our goal Protein flexibility

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1k views • 32 slides
Predictions of protein flexibility: first-order measures

Predictions of protein flexibility: first-order measures

Predictions of protein flexibility: first-order measures. Julio A. Kovacs, Pablo Chacon, and Ruben Abagyan Proteins: Structure, Function, and Bioinformatics 56 :661-668 (2004). the study of molecular flexibility connection between structure and function. Two different approaches:

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287 views • 17 slides
molecular dynamics

molecular dynamics

Molecular dynamics (MD) simulation is a computer simulation technique that allows one to predict the time dependent evolution of a system of interacting particles using laws of physics. Studying the dynamic development of a biological system with the consideration of protein flexibility is of vital importance, since many biological phenomena involving proteins are very dynamic processes, which include transport, molecular recognition, enzyme catalysis, and allosteric regulation. The static models produced by NMR, X-ray crystallography, and homology modeling offer valuable insights into macromolecular structure, while MD simulation can provide atomic-level information about protein conformational changes and binding thermodynamics under predefined physiological conditions (e.g., temperature, pressure), allowing for the study of a protein system at a timescale that is not accessible by current experimental approaches.

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1. Introduction Proteins play an essential role in nearly all cell functions.

1. Introduction Proteins play an essential role in nearly all cell functions.

Protein Flexibility Modeling Using Kernel Methods X ue-wen Chen and Jeremy Chen Bioinformatics and Computational Life Science Laboratory, ITTC Department of Computer Science, The University of Kansas, Lawrence, KS 66045 E?mail: xwchen@ku.edu. 3. Relevant Work Collective mode based

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Modeling Protein Flexibility with Spatial and Energetic Constraints

Modeling Protein Flexibility with Spatial and Energetic Constraints

INPUT. PROGRAM. OUTPUT. Protein (Native). Initialize Protein and Features. Conformations. Features. PCA Vector, Step Size. Move Features by PCA. Closure Satisfaction, Energies, RMSD. Energy Cutoff. Use CCD to Satisfy Features. Rewind to Previous Conformation. Minimize Energy.

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170 views • 1 slides

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