Refinement of Macromolecular Structures by the Maximum-Likelihood Method

TLDR

The paper reviews the mathematical basis of maximum likelihood for macromolecular structures, extending the likelihood function to incorporate prior phase information, experimental uncertainties, and the possibility of heterogeneous error distributions across different structural regions. The authors implement this extended likelihood function—including prior phase data and experimental uncertainties—into REFMAC, and describe a σA estimation method using free reflections whose effects are analyzed. Testing on multiple proteins shows that maximum‑likelihood refinement yields consistently better results than least‑squares refinement.

Abstract

This paper reviews the mathematical basis of maximum likelihood. The likelihood function for macromolecular structures is extended to include prior phase information and experimental standard uncertainties. The assumption that different parts of a structure might have different errors is considered. A method for estimating σA using `free' reflections is described and its effects analysed. The derived equations have been implemented in the program REFMAC. This has been tested on several proteins at different stages of refinement (bacterial α-amylase, cytochrome c′, cross-linked insulin and oligopeptide binding protein). The results derived using the maximum-likelihood residual are consistently better than those obtained from least-squares refinement.