/**
\page intro Introduction to integrative modeling

To understand the function of a macromolecular assembly, we must know the
structure of its components and the interactions between them. However,
direct experimental determination of such a structure is generally rather
difficult. While multiple methods do exist for structure determination,
each has a drawback. For example, crystals suitable for X-ray crystallography
cannot always be produced, especially for large assemblies of multiple
components. Cryo-electron microscopy (cryo-EM), on the other hand, can be
used to study large assemblies, but it is generally limited to worse than
atomic resolution. Finally, proteomics techniques, such as yeast two-hybrid
and mass spectrometry, yield information about the interactions between
proteins, but not the positions of these proteins within the assembly or
the structures of the proteins themselves.

One approach to solve the structures of proteins and their assemblies
is by "integrative" or "hybrid" modeling, in which information from different methods
is considered simultaneously during the modeling procedure. The approach
is briefly outlined here for clarity; it has been
<a href="https://www.ncbi.nlm.nih.gov/pubmed/20507923">covered in greater
detail</a> previously. These methods can include:
- Experimental techniques, such as:
  - X-ray crystallography
  - nuclear magnetic resonance (NMR) spectroscopy
    (CSP, [NOE](@ref IMP::isd::NOERestraint), J-couplings)
  - electron microscopy (EM) ([2D class averages](@ref IMP::em2d)
    or [3D maps](@ref IMP::em))
  - footprinting
  - [Immunoprecipitation pull-down](@ref IMP::core::ConnectivityRestraint)
  - [Cysteine cross-linking](@ref IMP::isd::CysteineCrossLinkRestraint)
  - [Chemical cross-linking](@ref IMP::isd::CrossLinkMSRestraint)
  - [FRET spectroscopy](@ref IMP::isd::FretRestraint)
  - [small angle X-ray scattering (SAXS)](@ref IMP::saxs)
  - proteomics
- Theoretical sources of information, such as:
  - template structures used in comparative/homology modeling
  - scoring functions used in molecular docking
  - statistical preferences
  - physics-based energy functions

The integrative approach has several advantages:
- Synergy among the input data minimizes the drawbacks of sparse, noisy,
  ambiguous and incoherent datasets. Each individual piece of data contains
  little structural information, but by simultaneously fitting a model to all
  data derived from independent experiments, the degeneracy of the structures
  that fit the data can be markedly reduced.
- This approach has the potential to produce all structures that are consistent
  with the data, not only one structure.
- An analysis of the structures allows us to estimate the precision of both
  the data and the structures.
- This approach can make the process of structure determination more efficient,
  by indicating which measurements would be most informative.

\section efforts Example modeling efforts

Hybrid structures based on our integrative approach:
 - The E. coli ribosome, the first eukaryotic ribosome from S. cerevisiae
 - The first mammalian ribosome from C. lupus48 and a fungal ribosome
 - The E. coli Hsp90
 - The eukaryotic chaperonin TRiC/CCT
 - The actin/scruin complex
 - Ryr1 voltage gated channel
 - The baker’s yeast [nuclear pore complex](https://salilab.org/npc) (NPC)
 - The [Nup84 complex](https://salilab.org/nup84/)
 - Transport through the NPC
 - Microtubule nucleation
 - [The 26S proteasome](https://www.ncbi.nlm.nih.gov/pubmed/19233204)
 - [PCS9K-Fab complex](@ref idock_pcsk9)
 - The yeast spindle pole body
 - Chromatin globin domain
 - The lymphoblastoid cell genome

\section procedure Integrative modeling procedure

The integrative modeling procedure used here is shown below.

<img class="textfigure" src="intmodel.png"
     alt="Integrative modeling procedure">

The first step in the procedure is to collect all experimental,
statistical, and physical information that describes the system of
interest.

A suitable representation for the system is then chosen and
the available information is translated to a set of spatial restraints
on the components of the system. For example, in the case of characterizing
the molecular architecture of the <a href="https://salilab.org/npc/">nuclear
pore complex</a> (NPC), atomic
structures of the protein subunits were not available, but the approximate
size and shape of each protein was known, so each protein was represented
as a ‘string’ of connected spheres consistent with the protein size and
shape. A simple distance between two proteins can be restrained by a
harmonic function of the distance, while the fit of a model into a 3D
cryo-EM density map can be restrained by the cross-correlation between
the map and the computed density of the model.

Next, the spatial restraints
are summed into a single scoring function that can be sampled using a
variety of optimizers, such as conjugate gradients, molecular dynamics,
Monte Carlo, and inference-based methods. This sampling generates an
ensemble of models that are as consistent with the input information as
possible.

In the final step, the ensemble is analyzed to determine,
for example, whether all of the restraints have been satisfied or certain
subsets of data conflict with others. The analysis may generate a
consensus model, such as the probability density for the location of
each subunit in the assembly, and yield a measure of the uncertainty in the
solutions.
*/
