Speaker: Mehdi Maadooliat, PhD, Assistant Professor Department of Mathematics, Statistics and Computer Science Marquette University, Milwaukee, WI

Event Date: 8/19/2015

Event Times: 12:05 PM - 1:00 PM

Location: Froehlke Auditorium
Marshfield Clinic

Bio: Mehdi Maadooliat received the Ph.D. degree in Statistics from the Texas A&M University, where he also served as a post-doctoral fellow. He is working as an Assistant Professor in the Department of Mathematics, Statistics and Computer Science at Marquette University. His primary research interests include functional data analysis, bioinformatics and machine learning with an application in protein structure prediction and classification.

Abstract: In this work we developed a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a shared set of bivariate spline basis functions that are determined by the observed data. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating blockwise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities separately. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions and the recently proposed distributions by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well.

*This presentation will be available for viewing online at your convenience via Marshfield Clinic’s MediaSite: http://mediasite01a.mfldclin.edu/Mediasite/Play/1374ef4e5f834dea89dcbf390eb7d6d01d