Uncertainty in integrative structural modeling

Abstract

Integrative structural modelling 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.

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

“…In such cases, integrative modeling is increasingly being used to compute structural models based on complementary experimental data and theoretical information (Figures 1 and 2; Table 1) (Alber et al, 2007; Alber et al, 2008; Robinson et al, 2007; Russel et al, 2012; Sali et al, 2003; Sali et al, 1990; Schneidman-Duhovny et al, 2014; Ward et al, 2013). Structural biology is no stranger to integrative models.…”

“…Thus, the same part of a system can be described with multiple representations and different parts of a system can be represented differently. An optimal representation facilitates accurate formulation of spatial restraints together with efficient and complete sampling of good-scoring solutions, while retaining sufficient detail (without over fitting) such that the resulting models are maximally useful for subsequent biological analysis (Schneidman-Duhovny et al, 2014). Second, a model can be multi-state, specifying multiple discrete states of the system required to explain the input information (each state may differ in structure and/or composition) (Molnar et al, 2014; Pelikan et al, 2009).…”

“…, NMR-derived ensembles currently available in the PDB (Clore and Gronenborn, 1991; Snyder et al, 2005; Snyder et al, 2014) and the ensembles generated from SAXS (Tria et al, 2015)). This aspect of the representation allows us to describe model uncertainty and to assess the completeness of input information; such ensembles are distinct from multiple states that represent actual variations in the structure, as implied by experimental information that cannot be accounted for by a single representative structure (Schneidman-Duhovny et al, 2014; Schroder, 2015). …”

Coming to Grips with Ambiguity: Ion Mobility-Mass Spectrometry for Protein Quaternary Structure Assignment

“…Most of these questions surround the potential errors introduced when high levels of coarse graining are applied, the interpretation of structural ensembles generated from IM-MS modeling approaches, and the confidence levels associated with IM-MS structures in a general sense. [40] Additionally, questions remain regarding the extent of structural rearrangement apparent in some proteins and complexes in the gas-phase; a topic that has been investigated in detail elsewhere. [35,41] In this Critical Insight, we seek to critically evaluate the information content of IM-MS for protein quaternary structure assignment in cases where we can assume a strong memory of solution-phase structure.…”

Hybrid methods for macromolecular structure determination: experiment with expectations

“…When building a model the uncertainty in the atomic coordinates needs to be taken into account. A good overview of uncertainty in integrative structural modeling is given in Ref [58]. The uncertainty can be represented by an ensemble of models as usual in NMR, or by B-factors in crystallography.…”