Title :
Bayesian Statistics Analysis for Spin Coupling Constant and Thermostability of Proteins
Author :
Wenjin Zhou ; Rossetto, Allison M.
Author_Institution :
Dept. of Comput. Sci. & Eng., Oakland Univ., Rochester, MI, USA
Abstract :
We present the use of Bayesian statistics to find the thermo stability and spin coupling constant of a protein. The spin-coupling constant provides high-level structure information about bond angles and rotation in a protein. Thermo stability is an important factor in protein efficacy. Modeling thermo stability permits finding the mutation temperature of a protein, which is crucial since it affects function. We have used Bayesian statistics (MCMC) to find the missing parameters for these two models. Our predictive models using the parameters found with this method show good results.
Keywords :
Bayes methods; biothermics; bond angles; molecular biophysics; proteins; statistical analysis; thermal stability; Bayesian statistics analysis; MCMC; bond angles; high-level structure information; mutation temperature; predictive models; protein efficacy; protein rotation; protein thermostability; spin coupling constant; spin-coupling constant; Bayes methods; Markov processes; Mathematical model; Monte Carlo methods; Proteins; Temperature distribution; Thermal stability; Bayesian Statistics; MCMC; Proteins; Spin-Coupling; Thermostability;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.271