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Related Concept Videos

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Polymers: Defining Molecular Weight01:01

Polymers: Defining Molecular Weight

Unlike small molecules with definite molecular weights, polymers are a mixture of individual polymer chains of varying lengths, each with a unique molecular weight. So, the molecular weight of a polymer is expressed as an average value based on the average size of the polymer chains. The two most common forms of averages used for polymers are the number average molecular weight and weight average molecular weight.
The number average molecular weight (Mn) is the summation of the number...
Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...
Determination of Molar Masses of Polymers II01:27

Determination of Molar Masses of Polymers II

Polymer samples typically consist of macromolecular chains with a distribution of lengths, resulting in a range of molar masses rather than a single discrete value. Conventional descriptors such as the number-average molar mass and weight-average molar mass quantify this distribution but do not fully capture polymer behavior in solution..The viscosity-average molar mass provides a more realistic description of polymer behavior in solution because it accounts for the enhanced contribution of...
Determination of Molar Masses of Polymers I01:24

Determination of Molar Masses of Polymers I

Polymerization produces macromolecules with a range of chain lengths due to the random nature of molecular growth processes. As chains form and terminate at different stages, a single polymer sample contains molecules of varying sizes rather than a uniform structure. This variability is described using average molar masses and distribution-related parameters, which together provide a comprehensive understanding of polymer characteristics.The distribution of molar masses plays a critical role in...

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Spatial Separation of Molecular Conformers and Clusters
10:37

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Validating clustering of molecular dynamics simulations using polymer models.

Joshua L Phillips1, Michael E Colvin, Shawn Newsam

  • 1Center for Computational Biology, School of Natural Sciences, University of California, Merced, California, USA. jphillips7@ucmerced.edu

BMC Bioinformatics
|November 16, 2011
PubMed
Summary

This study validates data clustering algorithms for molecular dynamics simulations by using novel polymer models. Spectral clustering reliably identifies protein conformational states, advancing biomolecular simulation analysis.

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Area of Science:

  • Computational biophysics
  • Structural bioinformatics
  • Polymer physics

Background:

  • Molecular dynamics (MD) simulations are crucial for studying biomolecular conformations.
  • Computational data clustering automates the extraction of conformational states from MD data.
  • The utility of clustering algorithms in extracting meaningful information from MD simulations requires rigorous validation.

Purpose of the Study:

  • To analyze and validate data clustering algorithms for biomolecular simulations.
  • To assess the effectiveness of clustering in identifying conformational states.
  • To develop a framework for rigorously testing clustering algorithms using well-defined models.

Main Methods:

  • Development of novel polymer models with defined dynamics based on polymer theory.
  • Application of spectral clustering to polymer models and MD simulations of intrinsically disordered proteins.
  • Comparative analysis of clustering results from models and real biomolecular simulations.

Main Results:

  • Clustering algorithms successfully detected meta-stable and transitional conformations in the polymer models.
  • Polymer model results guided the analysis of intrinsically disordered protein simulations.
  • Spectral clustering demonstrated robustness to structural alignment anomalies and discriminated protein classes.

Conclusions:

  • A framework for validating clustering algorithms in biopolymer simulations was established using analytic and dynamic polymer models.
  • Spectral clustering reliably discriminates between different structural classes of intrinsically disordered proteins.
  • This work presents the first framework utilizing model polymers to rigorously test clustering algorithms for biopolymer studies.