Xtricorder: A Likelihood-Enhanced Self-Rotation Function and Application to a Machine-Learning Enhanced Matthews Prediction of Asymmetric Unit Copy Number

Analysis of crystallographic diffraction data before phasing gives the crystallographer a ‘first look’ at the nature of the problem and the context in which the structure determination will be performed. We here report the development of Xtricorder , an application that targets analysis of crystallographic data specifically for likelihood-based phasing. As well as porting many of the analyses previously available but relatively inaccessible in our Phaser codebase, Xtricorder offers a likelihood-enhanced self-rotation function. A novel and intuitive graphical representation of the self-rotation function presents the results for user inspection, and has the added advantage that, in an adapted form, is appropriate for training a convolutional neural network to enhance the standard Matthews analysis and more accurately predict the number of copies in the asymmetric unit. We investigate the usefulness of the likelihood-enhanced self-rotation function in ‘first look’ analyses, exploring the circumstances under which the self-rotation function results are useful, and discuss the application to AI-generated structure prediction. Synopsis Xtricorder is a new tool for analysing crystallographic data prior to phasing, featuring a likelihood-enhanced self-rotation function and graphical output that aids both user interpretation and machine learning-based prediction of asymmetric unit content. ### Competing Interest Statement The authors have declared no competing interest. Biotechnology and Biological Sciences Research Council, https://ror.org/00cwqg982, BB/Y009398/1