Kavli Affiliate: Ting Xu
| First 5 Authors: Yun Zhou, Boying Gong, Tao Jiang, Ting Xu, Haiyan Huang
| Summary:
Recent years have seen substantial advances in the development of
biofunctional materials using synthetic polymers. The growing problem of
elusive sequence-functionality relations for most biomaterials has driven
researchers to seek more effective tools and analysis methods. In this study,
statistical models are used to study sequence features of the recently reported
random heteropolymers (RHP), which transport protons across lipid bilayers
selectively and rapidly like natural proton channels. We utilized the
probabilistic graphical model framework and developed a generalized hidden
semi-Markov model (GHSMM-RHP) to extract the function-determining sequence
features, including the transmembrane segments within a chain and the sequence
heterogeneity among different chains. We developed stochastic variational
methods for efficient inference on parameter estimation and predictions, and
empirically studied their computational performance from a comparative
perspective on Bayesian (i.e., stochastic variational Bayes) versus frequentist
(i.e., stochastic variational expectation-maximization) frameworks that have
been studied separately before. The real data results agree well with the
laboratory experiments, and suggest GHSMM-RHP’s potential in predicting
protein-like behavior at the polymer-chain level.
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