scholarly journals Monochromatic multicomponent fluorescence sedimentation velocity for the study of high-affinity protein interactions

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Huaying Zhao ◽  
Yan Fu ◽  
Carla Glasser ◽  
Eric J Andrade Alba ◽  
Mark L Mayer ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Ampa Receptors ◽  
Cell Biology ◽  
Analytical Ultracentrifugation ◽  
Fluorescent Proteins ◽  
Protein Complexes ◽  
Sedimentation Velocity ◽  
Excitation Wavelength ◽  
Amino Terminal ◽  
High Affinity

The dynamic assembly of multi-protein complexes underlies fundamental processes in cell biology. A mechanistic understanding of assemblies requires accurate measurement of their stoichiometry, affinity and cooperativity, and frequently consideration of multiple co-existing complexes. Sedimentation velocity analytical ultracentrifugation equipped with fluorescence detection (FDS-SV) allows the characterization of protein complexes free in solution with high size resolution, at concentrations in the nanomolar and picomolar range. Here, we extend the capabilities of FDS-SV with a single excitation wavelength from single-component to multi-component detection using photoswitchable fluorescent proteins (psFPs). We exploit their characteristic quantum yield of photo-switching to imprint spatio-temporal modulations onto the sedimentation signal that reveal different psFP-tagged protein components in the mixture. This novel approach facilitates studies of heterogeneous multi-protein complexes at orders of magnitude lower concentrations and for higher-affinity systems than previously possible. Using this technique we studied high-affinity interactions between the amino-terminal domains of GluA2 and GluA3 AMPA receptors.

Assembly states of the nucleosome assembly protein 1 (NAP-1) revealed by sedimentation velocity and non-denaturing MS

2011 ◽  
Vol 436 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Masanori Noda ◽  
Susumu Uchiyama ◽  
Adam R. McKay ◽  
Akihiro Morimoto ◽  
Shigeki Misawa ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Analytical Ultracentrifugation ◽  
Three Dimensional ◽  
Sedimentation Velocity ◽  
Dimensional Structure ◽  
Histone H2a ◽  
Nucleosome Assembly ◽  
Nucleosome Assembly Protein ◽  
Nucleosome Assembly Protein 1

Proteins often exist as ensembles of interconverting states in solution which are often difficult to quantify. In the present manuscript we show that the combination of MS under nondenaturing conditions and AUC-SV (analytical ultracentrifugation sedimentation velocity) unambiguously clarifies a distribution of states and hydrodynamic shapes of assembled oligomers for the NAP-1 (nucleosome assembly protein 1). MS established the number of associated units, which was utilized as input for the numerical analysis of AUC-SV profiles. The AUC-SV analysis revealed that less than 1% of NAP-1 monomer exists at the micromolar concentration range and that the basic assembly unit consists of dimers of yeast or human NAP-1. These dimers interact non-covalently to form even-numbered higher-assembly states, such as tetramers, hexamers, octamers and decamers. MS and AUC-SV consistently showed that the formation of the higher oligomers was suppressed with increasing ionic strength, implicating electrostatic interactions in the formation of higher oligomers. The hydrodynamic shapes of the NAP-1 tetramer estimated from AUC-SV agreed with the previously proposed assembly models built using the known three-dimensional structure of yeast NAP-1. Those of the hexamer and octamer could be represented by new models shown in the present study. Additionally, MS was used to measure the stoichiometry of the interaction between the human NAP-1 dimer and the histone H2A–H2B dimer or H3–H4 tetramer. The present study illustrates a rigorous procedure for the analysis of protein assembly and protein–protein interactions in solution.

scholarly journals Complex+: Aided Decision-Making for the Study of Protein Complexes

2019 ◽  
Author(s):  
Mehrnoosh Oghbaie ◽  
Petr Šulc ◽  
David Fenyö ◽  
Michael Pennock ◽  
John LaCava
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Cell Biology ◽  
Protein Complexes ◽  
Conclusive Evidence ◽  
Computational Techniques ◽  
Protein Assemblies ◽  
Data Types ◽  
Research Strategies ◽  
Protein Interactome

AbstractProteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective – motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

scholarly journals Author response: Monochromatic multicomponent fluorescence sedimentation velocity for the study of high-affinity protein interactions

2016 ◽  
Author(s):  
Huaying Zhao ◽  
Yan Fu ◽  
Carla Glasser ◽  
Eric J Andrade Alba ◽  
Mark L Mayer ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Sedimentation Velocity ◽  
Author Response ◽  
High Affinity

scholarly journals Use of fluorescence-detected sedimentation velocity to study high-affinity protein interactions

2017 ◽  
Vol 12 (9) ◽  
pp. 1777-1791 ◽  
Author(s):  
Sumit K Chaturvedi ◽  
Jia Ma ◽  
Huaying Zhao ◽  
Peter Schuck
Keyword(s):  
Protein Interactions ◽  
Sedimentation Velocity ◽  
High Affinity

scholarly journals Investigating High Affinity Protein Self-Association by Fluorescence Optical Sedimentation Velocity Analytical Ultracentrifugation

2014 ◽  
Vol 106 (2) ◽  
pp. 151a
Author(s):  
Suvendu Lomash ◽  
Huaying Zhao ◽  
Carla Glasser ◽  
Mark L. Mayer ◽  
Peter Schuck
Keyword(s):  
Analytical Ultracentrifugation ◽  
Sedimentation Velocity ◽  
High Affinity ◽  
Self Association

scholarly journals Towards a structurally resolved human protein interaction network

2021 ◽  
Author(s):  
David F Burke ◽  
Patrick Bryant ◽  
Inigo Barrio-Hernandez ◽  
Danish Memon ◽  
Gabriele Pozzati ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Cell Biology ◽  
Molecular Mechanisms ◽  
Protein Complexes ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Human Interactions ◽  
Pathogenic Variants ◽  
Binary Complexes

All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.

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