alexa Extract The Thermodynamic And Kinetic Information From Protein Simulations Using Dimensionality Reduction | 72384
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

Like us on:

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations
700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

9th International Conference on Structural Biology

Shuanghong Huo and Gustaf H
Clark University, USA
ScientificTracks Abstracts: J Proteomics Bioinform
DOI: 10.4172/0974-276X-C1-100
Abstract
In the study of protein thermodynamics and kinetics, it is of paramount importance to characterize protein free energy landscapes. Dimensionality reduction is a valuable tool to complete the task. We have evaluated several methods of dimensionality reduction, including linear and nonlinear methods, such as principal component analysis, Isomap, locally linear embedding, and diffusion maps. A series of criteria was used to assess different aspects of the embedding qualities. Our results have shown that there is no clear winner in all aspects of the evaluation and each dimensionality-reduction method has its limitations in a certain aspect. The linear method, principal component analysis, is not worse than the nonlinear ones in some respects for our peptide system. We have also developed a mathematical formulation to demonstrate that an explicit Euclidean-based representation of protein conformation space and the local distance metric associated to it improve the quality of dimensionality reduction. For a certain sense, clustering protein conformations into macro-clusters to build a Markov state model is also an approach of dimensionality. We have tested inherent structure and geometric structure for state space discretization and demonstrated that the macro-cluster based on inherent structure give a meaningful state space discretization in terms of conformational features and kinetics.
Biography

Shuanghong Huo received her PhD in Computational Chemistry from Boston University. She did her Postdoctoral training at UC-San Francisco. She is a Professor of Chemistry and Biochemistry at Clark University, Worcester, USA. Her research interest is in protein folding, misfolding, and aggregation. Recently, her group is developing dimensionality reduction methods and graph representations of protein free energy landscapes.

image PDF   |   image HTML
 

Relevant Topics

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2018-19
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

bornova escort

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

 
© 2008- 2018 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
Leave Your Message 24x7