Computational Crystallization

TL;DR

This paper reviews computational approaches to macromolecular crystallization, emphasizing the use of theoretical models, data analysis, and the potential of complete datasets to improve prediction and understanding of crystallization outcomes.

Contribution

It introduces the use of solubility phase diagrams and highlights the importance of complete data for enhancing computational crystallization predictions.

Findings

Tools based on binary outcomes are common but lose information.

Complete data can reveal new biological insights.

Analysis of sparse data is crucial for progress.

Abstract

Crystallization is a key step in macromolecular structure determination by crystallography. While a robust theoretical treatment of the process is available, due to the complexity of the system, the experimental process is still largely one of trial and error. In this article, efforts in the field are discussed together with a theoretical underpinning using a solubility phase diagram. Prior knowledge has been used to develop tools that computationally predict the crystallization outcome and define mutational approaches that enhance the likelihood of crystallization. For the most part these tools are based on binary outcomes (crystal or no crystal), and the full information contained in an assembly of crystallization screening experiments is lost. The potential of this additional information is illustrated by examples where new biological knowledge can be obtained and where a target can be sub-categorized to predict which class of reagents provides the crystallization driving force. Computational analysis of crystallization requires complete and correctly formatted data. While massive crystallization screening efforts are under way, the data available from many of these studies are sparse. The potential for this data and the steps needed to realize this potential are discussed.