Biomaterial Database

Solving the molecular distance geometry problem with inaccurate distance data. 2013

Michael Souza, and Carlile Lavor, and Albert Muritiba, and Nelson Maculan

Department of Statistics and Applied Mathematics, Federal University of Ceará, Ceará, 60455-760, Brazil. michael@ufc.br

We present a new iterative algorithm for the molecular distance geometry problem with inaccurate and sparse data, which is based on the solution of linear systems, maximum cliques, and a minimization of nonlinear least-squares function. Computational results with real protein structures are presented in order to validate our approach.

UI	MeSH Term	Description	Entries
D008958	Models, Molecular	Models used experimentally or theoretically to study molecular shape, electronic properties, or interactions; includes analogous molecules, computer-generated graphics, and mechanical structures.	Molecular Models,Model, Molecular,Molecular Model
D011506	Proteins	Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.	Gene Products, Protein,Gene Proteins,Protein,Protein Gene Products,Proteins, Gene
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D016018	Least-Squares Analysis	A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.	Rietveld Refinement,Analysis, Least-Squares,Least Squares,Analyses, Least-Squares,Analysis, Least Squares,Least Squares Analysis,Least-Squares Analyses,Refinement, Rietveld
D017711	Nonlinear Dynamics	The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.	Chaos Theory,Models, Nonlinear,Non-linear Dynamics,Non-linear Models,Chaos Theories,Dynamics, Non-linear,Dynamics, Nonlinear,Model, Non-linear,Model, Nonlinear,Models, Non-linear,Non linear Dynamics,Non linear Models,Non-linear Dynamic,Non-linear Model,Nonlinear Dynamic,Nonlinear Model,Nonlinear Models,Theories, Chaos,Theory, Chaos
D019295	Computational Biology	A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets.	Bioinformatics,Molecular Biology, Computational,Bio-Informatics,Biology, Computational,Computational Molecular Biology,Bio Informatics,Bio-Informatic,Bioinformatic,Biologies, Computational Molecular,Biology, Computational Molecular,Computational Molecular Biologies,Molecular Biologies, Computational