Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality

Aug 2, 2026

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Machine learning map modification tools are assessed for their differential impact on cryogenic electron microscopy (cryoEM) densities. The perspective evaluates how ML-based methods affect biomacromolecules versus ligands, reporting generally improved density quality for biomacromolecules alongside unpredictable outcomes for ligands. Effects are characterized through both quantitative map-quality metrics and qualitative inspection of modified maps. The work emphasizes the power of ML in cryoEM while also outlining risks that may arise from unexamined use.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

CryoEM progress and workflow growth

Machine learning across cryoEM pipeline

Map modification benefits and potential risks

UC San Diego UC San Diego Previously Published Works Title Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality. Permalink [https://escholarship.org/uc/item/68n0c77j](https://escholarship.org/uc/item/68n0c77j) Authors Berkeley, Raymond Cook, Brian Herzik, Mark Publication Date 2024 DOI 10.3389/fmolb.2024.1404885 Peer reviewed [eScholarship.org](eScholarship.org) Powered by the California Digital Library University of California TYPE Perspective PUBLISHED 18 April 2024 DOI 10.3389/fmolb.2024.1404885 OPEN ACCESS EDITED BY Edward T. Eng, New York Structural Biology Center, United States REVIEWED BY Kiefer Ramberg, Trinity College Dublin, Ireland *CORRESPONDENCE Mark A. Herzik Jr, [mherzik@ucsd.edu](mherzik@ucsd.edu) †These authors have contributed equally to this work RECEIVED 21 March 2024 ACCEPTED 03 April 2024 PUBLISHED 18 April 2024 CITATION Berkeley RF, Cook BD, Herzik MA Jr. (2024), Machine learning approaches to cryoEM density modiﬁcation differentially affect biomacromolecule and ligand density quality. Front. Mol. Biosci. 11:1404885 . doi: 10.3389/fmolb.2024.1404885 COPYRIGHT © 2024 Berkeley, Cook and Herzik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Machine learning approaches to cryoEM density modiﬁcation differentially affect biomacromolecule and ligand density quality Raymond F. Berkeley†, Brian D. Cook† and Mark A. Herzik Jr. *† Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, United States The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modiﬁcation tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modiﬁed maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use. KEYWORDS cryogenic electron microscopy, cryoEM, density modiﬁcation, model building, machine learning 1 Introduction Over the past decade, cryogenic electron microscopy (cryoEM) has matured into a leading method for determining the three-dimensional (3-D) structures of dynamic macromolecular complexes (DiIorio and Kulczyk, 2022) . Reductions in the cost of GPU-accelerated computing coupled with improvements in both microscope and detector hardware have facilitated the acquisition of high-quality, high-resolution cryoEM data (Nakane et al., 2020; Yip et al., 2020; Fréchin et al., 2023) . Hardware improvements have been complemented by concurrent developments in cryoEM data processing software, drastically improving both the performance and accessibility of the cryoEM data processing pipeline (Scheres, 2012; Punjani et al., 2017; Tegunov and Cramer, 2019) . These efforts have enabled the democratization of cryoEM, allowing researchers with a range of scientiﬁc backgrounds to determine high-resolution structures of diverse biomacromolecules using cryoEM (Baldwin et al., 2018) . The

FAQs

What is the main focus of this perspective on cryoEM density modification?

It evaluates how machine learning map modification tools differentially affect cryoEM densities, comparing impacts on biomacromolecules versus ligands.

How do machine learning methods typically influence biomacromolecule density quality?

Machine learning tools generally improve densities for biomacromolecules, yielding better map quality.

Why are ligand results described as unpredictable?

The perspective notes that ML-based map modification can produce unpredictable behavior for ligands, reflected in both quantitative quality metrics and qualitative changes in modified maps.

Aug 2, 2026

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