From integrative structural biology to cell biology

Šali, Andrej

doi:10.1016/j.jbc.2021.100743

Cited by 60 publications

(52 citation statements)

References 134 publications

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“…3 D and H and 5 C , D , G , and H ). In an attempt to rationalize these observations, we mapped the structure of the fibrillar Aβ–sTREM2 complex, by using integrative structure modeling ( 48 , 49 ) based on XL-MS data ( 50 ). Aβ40 fibrils were formed using a procedure that produces relatively homogenous fibrils ( 32 , 51 ) that are structurally similar to a published threefold symmetric solid NMR structure of Aβ40 ( 33 ).…”

Section: Resultsmentioning

confidence: 99%

Soluble TREM2 inhibits secondary nucleation of Aβ fibrillization and enhances cellular uptake of fibrillar Aβ

Belsare

Mondal

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Triggering receptor expressed on myeloid cells 2 (TREM2) is a single-pass transmembrane receptor of the immunoglobulin superfamily that is secreted in a soluble (sTREM2) form. Mutations in TREM2 have been linked to increased risk of Alzheimer’s disease (AD). A prominent neuropathological component of AD is deposition of the amyloid-β (Aβ) into plaques, particularly Aβ40 and Aβ42. While the membrane-bound form of TREM2 is known to facilitate uptake of Aβ fibrils and the polarization of microglial processes toward amyloid plaques, the role of its soluble ectodomain, particularly in interactions with monomeric or fibrillar Aβ, has been less clear. Our results demonstrate that sTREM2 does not bind to monomeric Aβ40 and Aβ42, even at a high micromolar concentration, while it does bind to fibrillar Aβ42 and Aβ40 with equal affinities (2.6 ± 0.3 µM and 2.3 ± 0.4 µM). Kinetic analysis shows that sTREM2 inhibits the secondary nucleation step in the fibrillization of Aβ, while having little effect on the primary nucleation pathway. Furthermore, binding of sTREM2 to fibrils markedly enhanced uptake of fibrils into human microglial and neuroglioma derived cell lines. The disease-associated sTREM2 mutant, R47H, displayed little to no effect on fibril nucleation and binding, but it decreased uptake and functional responses markedly. We also probed the structure of the WT sTREM2–Aβ fibril complex using integrative molecular modeling based primarily on the cross-linking mass spectrometry data. The model shows that sTREM2 binds fibrils along one face of the structure, leaving a second, mutation-sensitive site free to mediate cellular binding and uptake.

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Section: Resultsmentioning

confidence: 99%

Soluble TREM2 inhibits secondary nucleation of Aβ fibrillization and enhances cellular uptake of fibrillar Aβ

Belsare

Mondal

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…In that respect, we hope that making this prediction pipeline available as a web server will help biologists identify relevant QS of proteins and will help them investigate the QS of specific proteins. Together, these approaches will help bridge the gap between the sequence and structure space by adding a third dimension to proteomes and interactomes ( Aloy and Russell, 2006 ; Rolland et al, 2014 ; Elofsson, 2021 ; Postic et al, 2021 ; Sali, 2021 ), thus making 3D proteomics or “structuromics” ( Levy and Vogel, 2021 ) accessible.…”

Section: Discussionmentioning

confidence: 99%

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

Dey

Prilusky

Levy

2022

Front. Mol. Biosci.

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The identification of physiologically relevant quaternary structures (QSs) in crystal lattices is challenging. To predict the physiological relevance of a particular QS, QSalign searches for homologous structures in which subunits interact in the same geometry. This approach proved accurate but was limited to structures already present in the Protein Data Bank (PDB). Here, we introduce a webserver (www.QSalign.org) allowing users to submit homo-oligomeric structures of their choice to the QSalign pipeline. Given a user-uploaded structure, the sequence is extracted and used to search homologs based on sequence similarity and PFAM domain architecture. If structural conservation is detected between a homolog and the user-uploaded QS, physiological relevance is inferred. The web server also generates alternative QSs with PISA and processes them the same way as the query submitted to widen the predictions. The result page also shows representative QSs in the protein family of the query, which is informative if no QS conservation was detected or if the protein appears monomeric. These representative QSs can also serve as a starting point for homology modeling.

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“…Model validation follows five steps ( Sali, 2021 ; Saltzberg et al, 2021 ; Viswanath et al, 2017 ): (i) selection of models for validation; (ii) estimation of sampling precision; (iii) estimation of model precision, (iv) quantification of the match between the model and information used to compute it, and (v) quantification of the match between the model and information not used in the computation. Next, we discuss each one of these validations separately.…”

Section: Methodsmentioning

confidence: 99%