title: Uncertainty in integrative structural modeling.
abstract: Integrative structural modeling uses multiple types of input information and proceeds in four stages: (i) gathering information, (ii) designing model representation and converting information into a scoring function, (iii) sampling good-scoring models, and (iv) analyzing models and information. In the first stage, uncertainty originates from data that are sparse, noisy, ambiguous, or derived from heterogeneous samples. In the second stage, uncertainty can originate from a representation that is too coarse for the available information or a scoring function that does not accurately capture the information. In the third stage, the major source of uncertainty is insufficient sampling. In the fourth stage, clustering, cross-validation, and other methods are used to estimate the precision and accuracy of the models and information.
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA. Electronic address: dina@salilab.org.
authors.forename: Dina
authors.initials: D
authors.lastname: Schneidman-Duhovny
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Pellarin
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA. Electronic address: sali@salilab.org.
authors.forename: Andrej
authors.initials: A
authors.lastname: Sali
mesh_terms: D008958:Models, Molecular; D008962:Models, Theoretical; D015394:Molecular Structure