The future of protein modeling: integrative modeling

In this paper, Russel et al. set up the grounds for the next generation of protein modeling platforms that combine computational modeling with experimental information from various sources. This will permit us to re-assess and refine published structural models as new structural information becomes available and suggest future experiments that validate the refined model in a feed-back loop.

In the integrative modeling platform, models are encoded as a collection of particles. Each particle can be used to create atomic, coarse-grained, or hierarchical representations. Models are then evaluated by a scoring function composed of terms called restraints. Each restraint corresponds to one particular experiment and measures how well a model agrees with the structural information derived from that experiment. The IMP’s scoring function includes restraints for small-angle X-ray scattering (SAXS), mass spectrometry, electron microscopy, nuclear magnetic resonance (NMR), Chemistry at Harvard Macromolecular Mechanics (CHARMM) force field, statistical potentials, alignment with related structures and 5C data. 

IMP also integrates different sampling algorithms that will allow users to refine each model so that it fulfils the restraints dictated by the different experimental information. Furthermore, IMP has been implemented so that external users can also develop new restraints, optimization algorithms and analysis methods.

A beautiful friendship: Combining X-ray crystallography and cryo-electron microscopy

Trabuco and colleagues introduce a very interesting approach that combines high-resolution data from X-ray crystallography with lower-resolution electron density maps obtained by cryo-electron microscopy (cryo-EM). This method, known as MDFF (molecular dynamics flexible fitting) incorporates two new variables into its MD potential energy function, one that corresponds to a potential derived from the EM data and another variable that aims to preserve the secondary structure of proteins and nucleic acids.

Such combination permits to take the knowledge that you can extract from cryo-EM maps further. It is important to highlight that the EM map represents an ensemble of conformational states even when dealing with a homogeneus dataset. Therefore, it is preferred to show how not just a single fitted structure but a set of differently structures fitted equally well into a single cryo-EM map. Something similar to the ensemble of structures obtained by NMR.

To our knowledge, this is the only method for flexible fitting capable of dealing with nucleic acids and proteins. The authors release newer versions from time to time (they recently incorporated symmetry restraint into the simulation. I would recommend this method to anyone who is planning to apply a flexible fitting method in his/her research.