Uncertainty in integrative structural modeling

Dina Schneidman-Duhovny*, Riccardo Pellarin, Andrej Sali

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

75 Scopus citations

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.

Original languageAmerican English
Pages (from-to)96-104
Number of pages9
JournalCurrent Opinion in Structural Biology
Volume28
Issue number1
DOIs
StatePublished - Oct 2014
Externally publishedYes

Bibliographical note

Funding Information:
We acknowledge support from NIH R01 GM083960 and NIH U54 GM103511 (A.S.). We acknowledge support from Swiss National Science Foundation (SNSF) grants PA00P3_139727 and PBZHP3-133388 (to R.P.).

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