title: A geometric buildup algorithm for the solution of the distance geometry problem using least-squares approximation.
abstract: We propose a new geometric buildup algorithm for the solution of the distance geometry problem in protein modeling, which can prevent the accumulation of the rounding errors in the buildup calculations successfully and also tolerate small errors in given distances. In this algorithm, we use all instead of a subset of available distances for the determination of each unknown atom and obtain the position of the atom by using a least-squares approximation instead of an exact solution to the system of distance equations. We show that the least-squares approximation can be obtained by using a special singular value decomposition method, which not only tolerates and minimizes small distance errors, but also prevents the rounding errors from propagation effectively, especially when the distance data is sparse. We describe the least-squares formulations and their solution methods, and present the test results from applying the new algorithm for the determination of a set of protein structures with varying degrees of availability and accuracy of the distances. We show that the new development of the algorithm increases the modeling ability, and improves stability and robustness of the geometric buildup approach significantly from both theoretical and practical points of view.
authors.affiliation: Department of Mathematics, Program on Bioinformatics and Computational Biology, Iowa State University, Ames, IA, USA. atilla@iastate.edu
authors.forename: Atilla
authors.initials: A
authors.lastname: Sit
authors.forename: Zhijun
authors.initials: Z
authors.lastname: Wu
authors.forename: Yaxiang
authors.initials: Y
authors.lastname: Yuan
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D016014:Linear Models; D055641:Mathematical Concepts; D008958:Models, Molecular; D015394:Molecular Structure; D017711:Nonlinear Dynamics; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins