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Volume 10, Issue 8 (Suppl) J Proteomics Bioinform, an open access journal
ISSN: 0974-276X Structural Biology 2017
September 18-20, 2017
9th International Conference on
Structural Biology
September 18-20, 2017 Zurich, Switzerland
Extract the thermodynamic and kinetic information from protein simulations using dimensionality
reduction
Shuanghong Huo and Gustaf H
Clark University, USA
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.
Shuanghong Huo et al., J Proteomics Bioinform 2017, 10:8(Suppl)
DOI: 10.4172/0974-276X-C1-0100