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SAXS-Guided Metadynamics
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik.ORCID-id: 0000-0002-2662-6373
Stockholms universitet, Naturvetenskapliga fakulteten, Institutionen för biokemi och biofysik. Stockholms universitet, Science for Life Laboratory (SciLifeLab).ORCID-id: 0000-0002-2734-2794
Visa övriga samt affilieringar
Antal upphovsmän: 52015 (Engelska)Ingår i: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 11, nr 7, s. 3491-3498Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The small-angle X-ray scattering (SAXS) methodology enables structural characterization of biological macromolecules in solution. However, because SAXS provides low-dimensional information, several potential structural configurations can reproduce the experimental scattering profile, which severely complicates the structural refinement process. Here, we present a bias-exchange metadynamics refinement protocol that incorporates SAXS data as collective variables and therefore tags all possible configurations with their corresponding free energies, which allows identification of a unique structural solution. The method has been implemented in PLUMED and combined with the GROMACS simulation package, and as a proof of principle, we explore the Trp-cage protein folding landscape.

Ort, förlag, år, upplaga, sidor
2015. Vol. 11, nr 7, s. 3491-3498
Nationell ämneskategori
Kemi
Forskningsämne
biokemi med inriktning mot bioinformatik
Identifikatorer
URN: urn:nbn:se:su:diva-119740DOI: 10.1021/acs.jctc.5b00299ISI: 000358104800057OAI: oai:DiVA.org:su-119740DiVA, id: diva2:849231
Tillgänglig från: 2015-08-27 Skapad: 2015-08-24 Senast uppdaterad: 2022-02-23Bibliografiskt granskad
Ingår i avhandling
1. Peering Beyond the Noise in Experimental Biophysical Data
Öppna denna publikation i ny flik eller fönster >>Peering Beyond the Noise in Experimental Biophysical Data
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2019. s. 53
Nyckelord
Cryo-EM, mitochondrial ribosome, SAXS, molecular dynamics, metadynamics
Nationell ämneskategori
Biokemi och molekylärbiologi
Forskningsämne
biokemi med inriktning mot bioinformatik
Identifikatorer
urn:nbn:se:su:diva-167809 (URN)978-91-7797-711-7 (ISBN)978-91-7797-712-4 (ISBN)
Disputation
2019-05-24, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 14:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2019-04-29 Skapad: 2019-04-04 Senast uppdaterad: 2022-02-26Bibliografiskt granskad

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Kimanius, DariLindahl, Erik

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Kimanius, DariLindahl, Erik
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Institutionen för biokemi och biofysikScience for Life Laboratory (SciLifeLab)
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