scholarly journals Biological insights from SMA-extracted proteins

2021 ◽  
Author(s):  
Lucas Unger ◽  
Alejandro Ronco-Campaña ◽  
Philip Kitchen ◽  
Roslyn M. Bill ◽  
Alice J. Rothnie
Keyword(s):  
Membrane Proteins ◽  
Lipid Bilayer ◽  
Protein Interactions ◽  
Divalent Cations ◽  
Structural Data ◽  
Protein Protein Interactions ◽  
Binding Assays ◽  
Real Time Measurement ◽  
Low Concentrations ◽  
Binding Pockets

In the twelve years since styrene maleic acid (SMA) was first used to extract and purify a membrane protein within a native lipid bilayer, this technological breakthrough has provided insight into the structural and functional details of protein–lipid interactions. Most recently, advances in cryo-EM have demonstrated that SMA-extracted membrane proteins are a rich-source of structural data. For example, it has been possible to resolve the details of annular lipids and protein–protein interactions within complexes, the nature of lipids within central cavities and binding pockets, regions involved in stabilising multimers, details of terminal residues that would otherwise remain unresolved and the identification of physiologically relevant states. Functionally, SMA extraction has allowed the analysis of membrane proteins that are unstable in detergents, the characterization of an ultrafast component in the kinetics of electron transfer that was not possible in detergent-solubilised samples and quantitative, real-time measurement of binding assays with low concentrations of purified protein. While the use of SMA comes with limitations such as its sensitivity to low pH and divalent cations, its major advantage is maintenance of a protein's lipid bilayer. This has enabled researchers to view and assay proteins in an environment close to their native ones, leading to new structural and mechanistic insights.

scholarly journals TransINT: an interface-based prediction of membrane protein-protein interactions

2019 ◽  
Author(s):  
G. Khazen ◽  
A. Gyulkhandanian ◽  
T. Issa ◽  
R.C. Maroun
Keyword(s):  
Membrane Proteins ◽  
Protein Interactions ◽  
Transmembrane Protein ◽  
Structural Data ◽  
Supplementary Information ◽  
Amino Acid Residues ◽  
Protein Protein Interactions ◽  
Binary Complexes ◽  
Pharmacological Targets ◽  
New Compounds

ABSTRACTMotivationBecause of their number and diversity, membrane proteins and their complexes represent potential pharmacological targets par excellence for a variety of diseases, with very important implications for the design and discovery of new compounds modulating the interaction. However, experimental structural data are very scarce. To overcome this problem, we devised a computational approach for the prediction of alpha-helical transmembrane protein higher-order structures through data mining, sequence analysis, motif search, extraction, identification and characterization of the amino acid residues at the interface of the complexes.ResultsOur template motif-based approach using experimental recognition sites predicts thousands of binary complexes across species between membrane proteins.Availability and ImplementationThe TransINT online database of the annotated predicted interactions can be accessed on https://transint.shinyapps.io/transint/[email protected] informationavailable at Bioinformatics online.

scholarly journals LRRpredictor—A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 286 ◽  
Author(s):  
Eliza C. Martin ◽  
Octavina C. A. Sukarta ◽  
Laurentiu Spiridon ◽  
Laurentiu G. Grigore ◽  
Vlad Constantinescu ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Data ◽  
Innate Immune ◽  
Protein Protein Interactions ◽  
Ensemble Of Classifiers ◽  
Immune Receptor ◽  
Motif Detection ◽  
Protein Architecture ◽  
Recognition Specificity ◽  
Leucine Rich Repeats

Leucine-rich-repeats (LRRs) belong to an archaic procaryal protein architecture that is widely involved in protein–protein interactions. In eukaryotes, LRR domains developed into key recognition modules in many innate immune receptor classes. Due to the high sequence variability imposed by recognition specificity, precise repeat delineation is often difficult especially in plant NOD-like Receptors (NLRs) notorious for showing far larger irregularities. To address this problem, we introduce here LRRpredictor, a method based on an ensemble of estimators designed to better identify LRR motifs in general but particularly adapted for handling more irregular LRR environments, thus allowing to compensate for the scarcity of structural data on NLR proteins. The extrapolation capacity tested on a set of annotated LRR domains from six immune receptor classes shows the ability of LRRpredictor to recover all previously defined specific motif consensuses and to extend the LRR motif coverage over annotated LRR domains. This analysis confirms the increased variability of LRR motifs in plant and vertebrate NLRs when compared to extracellular receptors, consistent with previous studies. Hence, LRRpredictor is able to provide novel insights into the diversification of LRR domains and a robust support for structure-informed analyses of LRRs in immune receptor functioning.

scholarly journals NMR Spectroscopy of supramolecular chemistry on protein surfaces

2020 ◽  
Vol 16 ◽  
pp. 2505-2522
Author(s):  
Peter Bayer ◽  
Anja Matena ◽  
Christine Beuck
Keyword(s):  
Nmr Spectroscopy ◽  
Supramolecular Chemistry ◽  
Molecular Interactions ◽  
Protein Interactions ◽  
Atomic Resolution ◽  
Protein Protein Interactions ◽  
Protein Surfaces ◽  
Interaction Partners ◽  
Binding Pockets ◽  
Nmr Methods

As one of the few analytical methods that offer atomic resolution, NMR spectroscopy is a valuable tool to study the interaction of proteins with their interaction partners, both biomolecules and synthetic ligands. In recent years, the focus in chemistry has kept expanding from targeting small binding pockets in proteins to recognizing patches on protein surfaces, mostly via supramolecular chemistry, with the goal to modulate protein–protein interactions. Here we present NMR methods that have been applied to characterize these molecular interactions and discuss the challenges of this endeavor.

scholarly journals Divisome under Construction: Distinct Domains of the Small Membrane Protein FtsB Are Necessary for Interaction with Multiple Cell Division Proteins

2009 ◽  
Vol 191 (8) ◽  
pp. 2815-2825 ◽  
Author(s):  
Mark D. Gonzalez ◽  
Jon Beckwith
Keyword(s):  
Cell Division ◽  
Membrane Proteins ◽  
Protein Interactions ◽  
Cell Envelope ◽  
Coiled Coil ◽  
Main Function ◽  
Protein Protein Interactions ◽  
Molecular Scaffold ◽  
C Terminus ◽  
Under Construction

ABSTRACT Cell division in bacteria requires the coordinated action of a set of proteins, the divisome, for proper constriction of the cell envelope. Multiple protein-protein interactions are required for assembly of a stable divisome. Within the Escherichia coli divisome is a conserved subcomplex of inner membrane proteins, the FtsB/FtsL/FtsQ complex, which is necessary for linking the upstream division proteins, which are predominantly cytoplasmic, with the downstream division proteins, which are predominantly periplasmic. FtsB and FtsL are small bitopic membrane proteins with predicted coiled-coil motifs, which themselves form a stable subcomplex that can recruit downstream division proteins independently of FtsQ; however, the details of how FtsB and FtsL interact together and with other proteins remain to be characterized. Despite the small size of FtsB, we identified separate interaction domains of FtsB that are required for interaction with FtsL and FtsQ. The N-terminal half of FtsB is necessary for interaction with FtsL and sufficient, when in complex with FtsL, for recruitment of downstream division proteins, while a portion of the FtsB C terminus is necessary for interaction with FtsQ. These properties of FtsB support the proposal that its main function is as part of a molecular scaffold to allow for proper formation of the divisome.

scholarly journals Engineering an improved light-induced dimer (iLID) for controlling the localization and activity of signaling proteins

2014 ◽  
Vol 112 (1) ◽  
pp. 112-117 ◽  
Author(s):  
Gurkan Guntas ◽  
Ryan A. Hallett ◽  
Seth P. Zimmerman ◽  
Tishan Williams ◽  
Hayretin Yumerefendi ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Design ◽  
Protein Interactions ◽  
Mammalian Cell Culture ◽  
Large Change ◽  
Small Gtpase ◽  
Protein Protein Interactions ◽  
Binding Assays ◽  
Light Stimulation ◽  
Inducible Protein

The discovery of light-inducible protein–protein interactions has allowed for the spatial and temporal control of a variety of biological processes. To be effective, a photodimerizer should have several characteristics: it should show a large change in binding affinity upon light stimulation, it should not cross-react with other molecules in the cell, and it should be easily used in a variety of organisms to recruit proteins of interest to each other. To create a switch that meets these criteria we have embedded the bacterial SsrA peptide in the C-terminal helix of a naturally occurring photoswitch, the light-oxygen-voltage 2 (LOV2) domain from Avena sativa. In the dark the SsrA peptide is sterically blocked from binding its natural binding partner, SspB. When activated with blue light, the C-terminal helix of the LOV2 domain undocks from the protein, allowing the SsrA peptide to bind SspB. Without optimization, the switch exhibited a twofold change in binding affinity for SspB with light stimulation. Here, we describe the use of computational protein design, phage display, and high-throughput binding assays to create an improved light inducible dimer (iLID) that changes its affinity for SspB by over 50-fold with light stimulation. A crystal structure of iLID shows a critical interaction between the surface of the LOV2 domain and a phenylalanine engineered to more tightly pin the SsrA peptide against the LOV2 domain in the dark. We demonstrate the functional utility of the switch through light-mediated subcellular localization in mammalian cell culture and reversible control of small GTPase signaling.

scholarly journals Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions

2020 ◽  
Author(s):  
Mayank Baranwal ◽  
Abram Magner ◽  
Jacob Saldinger ◽  
Emine S. Turali-Emre ◽  
Shivani Kozarekar ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Intracellular Signaling ◽  
Metabolic Regulation ◽  
Selection Process ◽  
3D Structure ◽  
Structural Data ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Attention Network ◽  
Interaction Sites

AbstractDevelopment of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. In this study, we address this problem and describe a PPI analysis method based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, unsupervised prediction of the interaction sites by Struct2Graph for phenol-soluble modulins are found to be in concordance with the previously reported binding sites for this family.Author summaryPPIs are the central part of signal transduction, metabolic regulation, environmental sensing, and cellular organization. Despite their success, most strategies to decode PPIs use sequence based approaches do not generalize to broader classes of chemical compounds of similar scale as proteins that are equally capable of forming complexes with proteins that are not based on amino acids, and thus lack of an equivalent sequence-based representation. Here, we address the problem of prediction of PPIs using a first of its kind, 3D structure based graph attention network (available at https://github.com/baranwa2/Struct2Graph). Despite its excellent prediction performance, the novel mutual attention mechanism provides insights into likely interaction sites through its knowledge selection process in a completely unsupervised manner.

Sign in / Sign up

Export Citation Format

Share Document