Progress on the UNTANGLE Challenge for revealing cooperative motions

TL;DR

The UNTANGLE Challenge aims to extract cooperative motions from electron density data using geometry restraints, with new algorithms improving structural modeling and electron density interpretation.

Contribution

New algorithms and tools have emerged to address the UNTANGLE Challenge, improving the extraction of cooperative motions from electron density data.

Findings

New algorithms have improved the extraction of cooperative motions from electron density data.

The UNTANGLE Challenge has been updated based on community feedback and experience.

Progress has been made toward untangling multi-conformer models to improve geometry scores and reveal biologically relevant motions.

Abstract

Since announcing the UNTANGLE Challenge last year several ground-breaking new algorithms have emerged to meet it. I will review these new tools and discuss their efficacy, both with the challenge data and how they can make real-world structures better. The Challenge itself will also be evaluated and updated, based on experience and feedback from the community. The Challenge posits that cooperative motions of functional dynamics can be extracted from ordinary, average electron density data using nothing more than established geometry restraint information. It consists of a simple 2-state system with 11 levels of increasing difficulty. The model with the best score reflects the true, underlying cooperative motion of the system, and the more untangled the model gets it both becomes closer it is to this true ensemble and it also becomes easier to detect the tangles. A full solution has yet to be found, but great strides have been made. In the near future, untangling multi-conformer models will lead to better geometry scores and lower R factors, and also reveal more detail in electron density maps as well as more biologically relevant cooperative motions.