Deformable Complex Network is an approach for determining and refining macro biomolecule 3D structure with high accuracy by decoding low resolution (>4A) X-ray diffraction data, in an automated fashion.

There exist other methods that involve computation followed by manual adjustment and interference on a PC screen, sometimes wearing a pair of stereo glasses. Those are perfect for experimental biologist (not physicist).

Below is a brief overview. Detailed explanation is at the bottom.

Theory:
Network – a set of atom pairs and triplets, selected after several rounds’ criteria according to index distance and spacial distance, adjustable by end users.

Complex – network is simultaneously subject to both inter-atomic stretching and bending potentials with length and angle deviation from equilibriums. Potentials are usually harmonic, but can be stronger like quartic.

Deformable – equilibriums are changable over time. This change results in a deformable network. The deformation can be local, or large and collective. The change is guided by reference value (derived from a prior information from related structure ), lastest equilibrium value, and instantaneous deviated value.

Protocol:
sampling – molecular dynamics in torsion angle space.

conformational search – N rounds of simulated annealing to overcome extremely complicated total energy landscape.

parameters optimization – grid search for optimization of three primary controlling paramters.

How to play? With these algorithms:

generation and parsing – dan.f enbond.f nbonds.f noe.f cns.f

header – cns.inc noe.inc pick.inc

task input – task.inp

Complete Content:
DCN-manuscript-PNAS-2012