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 Crystal structure and functional implication of bacterial STING

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Tzu-Ping Ko ◽  
Yu-Chuan Wang ◽  
Chia-Shin Yang ◽  
Mei-Hui Hou ◽  
Chao-Jung Chen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Driving Force ◽  
Binding Mode ◽  
Innate Immune ◽  
Ligand Specificity ◽  
Protein Protein Interactions ◽  
Recognition Mechanism ◽  
Cyclic Dinucleotides ◽  
Phage Propagation ◽  
Complex Crystal

AbstractMammalian innate immune sensor STING (STimulator of INterferon Gene) was recently found to originate from bacteria. During phage infection, bacterial STING sense c-di-GMP generated by the CD-NTase (cGAS/DncV-like nucleotidyltransferase) encoded in the same operon and signal suicide commitment as a defense strategy that restricts phage propagation. However, the precise binding mode of c-di-GMP to bacterial STING and the specific recognition mechanism are still elusive. Here, we determine two complex crystal structures of bacterial STING/c-di-GMP, which provide a clear picture of how c-di-GMP is distinguished from other cyclic dinucleotides. The protein-protein interactions further reveal the driving force behind filament formation of bacterial STING. Finally, we group the bacterial STING into two classes based on the conserved motif in β-strand lid, which dictate their ligand specificity and oligomerization mechanism, and propose an evolution-based model that describes the transition from c-di-GMP-dependent signaling in bacteria to 2’3’-cGAMP-dependent signaling in eukaryotes.

Glycoprotein Ibalpha Inhibitor Complex Crystal Structure at High Resolution.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 774-774
Author(s):  
P.A Mcewan ◽  
Robert K Andrews ◽  
Jonas Emsley
Keyword(s):  
Crystal Structure ◽  
Shear Stress ◽  
Protein Interactions ◽  
Platelet Adhesion ◽  
Peptide Sequence ◽  
Leucine Rich Repeat ◽  
Protein Protein Interactions ◽  
Leucine Rich Repeats ◽  
Von Willebrand ◽  
Complex Crystal

Abstract Abstract 774 Introduction: The platelet Glycoprotein Ib/V/IX (GpIb/V/IX) complex is considered a major target for anticoagulant therapy. The primary function of the receptor is to mediate platelet adhesion to von Willebrand factor (VWF) bound to damaged sub-endothelium. This represents the first critical step for platelet adhesion under conditions of high fluid shear stress. GpIb/V/IX is implicated in a number of thrombotic pathological processes such as stroke or myocardial infarction and the bleeding disorders Bernard-Soulier syndrome, platelet type von Willebrand disease (Pt-VWD) and thrombotic thrombocytopenic purpura. We have successfully determined the structure of the GpIbalpha N-terminal domain in complex with a potent (sub nM) 11meric peptide inhibitor (OS1) of the interaction with VWF. Methods: We have determined the crystal structure to 1.8Å resolution using molecular replacement. Results. The peptide sequence CTERMALHNLC was readily identifiable bound to GpIbalpha between the extended regulatory (R) loop and the concave surface of the leucine rich repeats. The peptide adopts one and a half turns of an alpha-helix and contacts three subsites (S1, S2 and S3). S1 and S2 reside within the leucine rich repeats and S3 has a unique feature as this subsite involves contact with the regulatory R-loop stabilizing it in a well defined conformation with helical character. This loop alters conformation between an extended beta-hairpin in the VWF-A1 bound structure and a more compact largely disordered structure in the unliganded structure. In this regard, the Pt-VWD mutations of GpIbalpha, G233V and M239V, which reside in the R-loop act by inducing a beta-conformation and thus result in a high affinity form of the receptor. Conclusions: These studies provide a strategy for targeting the GpIbalpha-VWF interaction using small molecules or alpha-helical peptides exploiting the GpIbalpha subsites described here and acting allosterically to stabilise a low affinity conformation of the receptor with an alpha helical R-loop. Ligand mimetic peptide complex crystal structures for the platelet receptors integrin aIIbb3 with RGD, and alpha2beta1 with a collagen peptide have been described and the former are currently in therapeutic use for treatment of thromboemboletic disorders. Targeting the GpIbalpha-VWF interaction may provide anti-thrombotic drugs which affect platelet adhesion under high shear stress without compromising normal processes of platelet adhesion and aggregation which may be required for normal hemostasis to function. Targeting protein-protein interactions is considered one of the great contemporary challenges in drug discovery. The understanding of how the S1S2S3 subsites provide very effective inhibition of a large protein-protein interaction has wide applicability. LRR proteins are an extended family mediating protein-protein interactions involved in a variety of disease processes such as sepsis, asthma, immunodeficiencies, atherosclerosis, alzheimers (leucine rich repeat kinase) and leukaemia (leucine rich repeat phosphatase). The structural fit of the helical curvature of the peptide with the arc of the leucine rich repeats may provide a basis for further development of alpha-helical peptide mimetics targeting other members of the LRR family which utilize the concave face. Disclosures: No relevant conflicts of interest to declare.

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.

scholarly journals pyProGA—A PyMOL plugin for protein residue network analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255167
Author(s):  
Vladimir Sladek ◽  
Yuta Yamamoto ◽  
Ryuhei Harada ◽  
Mitsuo Shoji ◽  
Yasuteru Shigeta ◽  
...  
Keyword(s):  
Open Source Software ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Structural Data ◽  
Pair Interaction ◽  
Differential Analysis ◽  
Protein Protein Interactions ◽  
Network Methods ◽  
Free Open Source ◽  
Pymol Plugin

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.

scholarly journals The Viral Macrodomain Counters Host Antiviral ADP-Ribosylation

Viruses ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 384 ◽  
Author(s):  
Yousef M. O. Alhammad ◽  
Anthony R. Fehr
Keyword(s):  
Immune Responses ◽  
Protein Interactions ◽  
Innate Immune ◽  
Innate Immune Responses ◽  
Protein Protein Interactions ◽  
Granule Formation ◽  
Inflammatory Cytokine Production ◽  
Adp Ribosylation ◽  
Antiviral Responses ◽  
Host Defense Response

Macrodomains, enzymes that remove ADP-ribose from proteins, are encoded by several families of RNA viruses and have recently been shown to counter innate immune responses to virus infection. ADP-ribose is covalently attached to target proteins by poly-ADP-ribose polymerases (PARPs), using nicotinamide adenine dinucleotide (NAD+) as a substrate. This modification can have a wide variety of effects on proteins including alteration of enzyme activity, protein–protein interactions, and protein stability. Several PARPs are induced by interferon (IFN) and are known to have antiviral properties, implicating ADP-ribosylation in the host defense response and suggesting that viral macrodomains may counter this response. Recent studies have demonstrated that viral macrodomains do counter the innate immune response by interfering with PARP-mediated antiviral defenses, stress granule formation, and pro-inflammatory cytokine production. Here, we will describe the known functions of the viral macrodomains and review recent literature demonstrating their roles in countering PARP-mediated antiviral responses.

Divide-and-Link Peptide Docking: A Fragment-Based Peptide Docking Protocol

2021 ◽  
Author(s):  
Lu Sun ◽  
Tingting Fu ◽  
Dan Zhao ◽  
Hongjun Fan ◽  
Shijun Zhong
Keyword(s):  
Protein Folding ◽  
Drug Design ◽  
Protein Interactions ◽  
Structural Data ◽  
Peptide Drug ◽  
Protein Protein Interactions ◽  
Peptide Docking ◽  
Docking Protocol ◽  
Peptide Interaction

Protein-peptide interaction is crucial for various important cellular regulations, and also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained...

scholarly journals Molecular recognition of ternary complexes: a new dimension in the structure-guided design of chemical degraders

2017 ◽  
Vol 61 (5) ◽  
pp. 505-516 ◽  
Author(s):  
Scott J. Hughes ◽  
Alessio Ciulli
Keyword(s):  
Molecular Recognition ◽  
Drug Design ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Structural Data ◽  
Ternary Complexes ◽  
Protein Protein Interactions ◽  
New Paradigm ◽  
Design And Optimization ◽  
Three Body

Molecular glues and bivalent inducers of protein degradation (also known as PROTACs) represent a fascinating new modality in pharmacotherapeutics: the potential to knockdown previously thought ‘undruggable’ targets at sub-stoichiometric concentrations in ways not possible using conventional inhibitors. Mounting evidence suggests these chemical agents, in concert with their target proteins, can be modelled as three-body binding equilibria that can exhibit significant cooperativity as a result of specific ligand-induced molecular recognition. Despite this, many existing drug design and optimization regimens still fixate on binary target engagement, in part due to limited structural data on ternary complexes. Recent crystal structures of protein complexes mediated by degrader molecules, including the first PROTAC ternary complex, underscore the importance of protein–protein interactions and intramolecular contacts to the mode of action of this class of compounds. These discoveries have opened the door to a new paradigm for structure-guided drug design: borrowing surface area and molecular recognition from nature to elicit cellular signalling.

scholarly journals ADAR1 limits stress granule formation through both translation-dependent and translation-independent mechanisms

2021 ◽  
Author(s):  
Giulia A. Corbet ◽  
James M. Burke ◽  
Roy Parker
Keyword(s):  
Immune Response ◽  
Stress Response ◽  
Protein Interactions ◽  
Binding Activity ◽  
Innate Immune ◽  
Protein Protein Interactions ◽  
Granule Formation ◽  
Translation Inhibition ◽  
Translation Repression ◽  
Dsrna Binding

Stress granules (SGs) are cytoplasmic assemblies of RNA and protein that form when translation is repressed during the integrated stress response (ISR). SGs assemble from the combination of RNA-RNA, RNA-protein, and protein-protein interactions between mRNPs. The protein Adenosine deaminase acting on RNA 1 (ADAR1) recognizes and modifies dsRNAs within cells to prevent an aberrant innate immune response. ADAR1 localizes to SGs, and since RNA-RNA interactions contribute to SG assembly and dsRNA induces SGs, we examined how ADAR1 affects SG formation. First, we demonstrate that ADAR1 depletion triggers SGs by allowing endogenous dsRNA to activate the ISR through PKR activation and translation repression. However, we also show that ADAR1 limits SG formation independently of translation inhibition. ADAR1 repression of SGs is independent of deaminase activity, but dependent on dsRNA-binding activity, suggesting a model where ADAR1 binding limits RNA-RNA and/or RNA-protein interactions necessary for recruitment to SGs. Given that ADAR1 expression is induced during viral infection, these findings have implications for ADAR1's role in the antiviral response.

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.

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