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Accelerated cryo-EM structure determination with parallelisation using GPUs in RELION-2
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0002-2662-6373
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0002-2734-2794
Number of Authors: 42016 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 5, article id e18722Article in journal (Refereed) Published
Abstract [en]

By reaching near-atomic resolution for a wide range of specimens, single-particle cryo-EM structure determination is transforming structural biology. However, the necessary calculations come at large computational costs, which has introduced a bottleneck that is currently limiting throughput and the development of new methods. Here, we present an implementation of the RELION image processing software that uses graphics processors (GPUs) to address the most computationally intensive steps of its cryo-EM structure determination workflow. Both image classification and high-resolution refinement have been accelerated more than an order-of-magnitude, and template-based particle selection has been accelerated well over two orders-of-magnitude on desktop hardware. Memory requirements on GPUs have been reduced to fit widely available hardware, and we show that the use of single precision arithmetic does not adversely affect results. This enables high-resolution cryo-EM structure determination in a matter of days on a single workstation.

Place, publisher, year, edition, pages
2016. Vol. 5, article id e18722
National Category
Biological Sciences
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-141500DOI: 10.7554/eLife.18722ISI: 000394244600001OAI: oai:DiVA.org:su-141500DiVA, id: diva2:1087112
Available from: 2017-04-05 Created: 2017-04-05 Last updated: 2022-03-23Bibliographically approved
In thesis
1. Peering Beyond the Noise in Experimental Biophysical Data
Open this publication in new window or tab >>Peering Beyond the Noise in Experimental Biophysical Data
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Experimental protein structure determination methods make up a fundamental part of our understanding of biological systems. Manual interpretation of the output from these methods has been made obsolete by the sheer size and complexity of the acquired data. Instead, computational methods are becoming essential for this task and with the advent of high-throughput methods the efficiency and robustness of these methods are a major concern. This work focuses on the computational challenge of efficiently extracting statistically supported information from noisy or significantly reduced experimental data.

Small-angle X-ray scattering (SAXS) is a method capable of probing structural information with many experimental benefits compared to alternative methods. However, the acquired data is a noisy reduction of a large set of structural features into a low-dimensional signal-mixture, which significantly limits its interpretability. Due to this SAXS has this far been limited to conclusions about large-scale structural features, like radius of gyration or the oligomeric state of the sample. In this thesis I present an approach where SAXS data is used to guide molecular dynamics simulations to explore experimentally relevant conformational states. The experimental data is fed into the simulations through a metadynamics protocol, which explores the experimental data through conformational sampling subject to thermodynamic restraints. I show how this approach makes it possible to use SAXS to produce atomic-resolution models and make further-reaching conclusions about the underlying biological system, in particular by showcasing de novo folding of a small protein.

Another experimental method that generates noisy and reduced data is cryogenic electron microscopy (cryo-EM). Due to recent development in the field, the computational burden has become a considerable bottleneck, which greatly limits the throughput of the method. I present computational techniques to alleviate this burden through the use of specialized algorithms capable of efficient execution on graphics processing units (GPUs). This work improves the computational efficiency of the entire pipeline by several orders of magnitude and significantly advances the overall efficiency and applicability of the method. I show how this enables the development of improved algorithms with increased capabilities for extracting relevant biological information form the data. Several such improvements are presented that significantly increase the resolution of the refinement results and provide additional information about the dynamics of the system. Additionally, I present an application of these methods to data collected on a biogenesis intermediate of the mitochondrial ribosome. The new structures provide insights into the timing of the rRNA folding and protein incorporation as well as the role of two previously unknown assembly factors during the final stages of ribosome maturation.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2019. p. 53
Keywords
Cryo-EM, mitochondrial ribosome, SAXS, molecular dynamics, metadynamics
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-167809 (URN)978-91-7797-711-7 (ISBN)978-91-7797-712-4 (ISBN)
Public defence
2019-05-24, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 14:00 (English)
Opponent
Supervisors
Available from: 2019-04-29 Created: 2019-04-04 Last updated: 2022-02-26Bibliographically approved
2. Fast and reliable alignment and classification of biological macromolecules in electron microscopy images
Open this publication in new window or tab >>Fast and reliable alignment and classification of biological macromolecules in electron microscopy images
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In the last century, immense progress has been made to charter and understand a wide range of biological phenomena. The origin of genetic inheritance was determined, showing that DNA holds genes that determine the architecture of proteins, utilized by the cell for most functions. Mapping of the human genome eventually revealed around 20000 genes, showing a vast complexity of biology at its most fundamental level.

To study the molecular structure, function and regulation of proteins, spectroscopic techniques and microscopy are employed. Until just over a decade ago, the determination of atomic detail of biomolecules like proteins was limited to those that were small or possible to crystallize. However recent technological advances in cryogenic electron microscopy (cryo-EM) now allows it to routinely reach resolutions where it can provide a wealth of new information on molecular biological phenomena by permitting new targets to be structurally characterized.

In cryo-EM, biological molecules are suspended in thin vitreous sheet of ice and imaged in projection. Collecting millions of such images permits the reconstruction of the original molecular structure, by appropriate alignment and averaging of the particle images. This however requires immense computational effort, which just a few years ago was prohibitive to full use of the image data.

In this thesis, I describe the development of fast algorithms for processing of cryo-EM data, utilizing GPUs by exposing the inherent parallelism of its alignment and classification. The acceleration of this processing has changed how biological research can utilize cryo-EM data. The drastically reduced processing time now allows more extensive processing, development of new and more demanding processing tools, and broader access to cryo-EM as a method for biological investigation. As an example of what is now possible, I show the processing of the fungal pyruvate dehydrogenase complex (PDC), which poses unique processing challenges. Through extensive processing, new biological information can be inferred, reconciling numerous previous findings from biochemical research. The processing of PDC also exemplifies current limitations to established.

Place, publisher, year, edition, pages
Stockholm: Institutionen för biokemi och biofysik, Stockholms Universitet, 2020. p. 100
Keywords
cryo-EM, electron microscopy, GPU, parallel processing, protein structure
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-179802 (URN)978-91-7911-050-5 (ISBN)978-91-7911-051-2 (ISBN)
Public defence
2020-04-24, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Manuscript.

Available from: 2020-04-01 Created: 2020-03-10 Last updated: 2022-02-26Bibliographically approved

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Kimanius, DariForsberg, Björn O.Lindahl, Erik

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