Experimental and in silico Alphafold2 derived structures of the SNX-RGS proteins suggest a new class of lipid transfer protein

Recent advances in protein structure prediction using machine learning such as AlphaFold2 and RosettaFold presage a revolution in structural biology. Genome-wide predictions of protein structures are providing unprecedented insights into their architecture and intradomain interactions, and applications have already progressed towards assessing protein complex formation. Here we present detailed analyses of the sorting nexin proteins that contain regulator of G-protein signalling domains (SNX-RGS proteins), providing a key example of the ability of AlphaFold2 to reveal novel structures with previously unsuspected biological functions. These large proteins are conserved in most eukaryotes and are known to associate with lipid droplets (LDs) and sites of LD-membrane contacts, with key roles in regulating lipid metabolism. Previous studies indicate they possess five domains, including an N-terminal transmembrane domain that anchors them to the endoplasmic reticulum, an RGS domain, a lipid interacting phox homology (PX) domain and two additional domains named the PXA and PXC domains of unknown structure and function. Here we report the crystal structure of the RGS domain of sorting nexin 25 (SNX25) and show that the AlphaFold2 prediction closely matches the experimental structure. Analysing the full-length SNX-RGS proteins across multiple homologues and species we find that the distant PXA and PXC domains in fact fold into a single unique structure that notably features a large and conserved hydrophobic pocket. The nature of this pocket strongly suggests a role in lipid or fatty acid binding, and we propose that these molecules represent a new class of conserved lipid transfer proteins.

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The seven transmembrane domain protein MoRgs7 functions in surface perception and undergoes coronin MoCrn1-dependent endocytosis in complex with Gα subunit MoMagA to promote cAMP signaling and appressorium formation in Magnaporthe oryzae

10.1101/435511 ◽

2018 ◽

Author(s):

Xiao Li ◽

Kaili Zhong ◽

Ziyi Yin ◽

Jiexiong Hu ◽

Lianwei Li ◽

...

Keyword(s):

G Protein ◽

Magnaporthe Oryzae ◽

Transmembrane Domain ◽

Surface Hydrophobicity ◽

Camp Signaling ◽

Actin Binding ◽

Rgs Proteins ◽

Appressorium Formation ◽

Rgs Domain ◽

Tm Domain

AbstractRegulator of G-protein signaling (RGS) proteins primarily function as GTPase-accelerating proteins (GAPs) to promote GTP hydrolysis of Gα subunits, thereby regulating G-protein mediated signaling. RGS proteins could also contain additional domains such as GoLoco to inhibit GDP dissociation. The rice blast fungus Magnaporthe oryzae encodes eight RGS and RGS-like proteins (MoRgs1 to MoRgs8) that have shared and distinct functions in growth, appressorium formation and pathogenicity. Interestingly, MoRgs7 and MoRgs8 contain a C-terminal seven-transmembrane domain (7-TM) motif typical of G-protein coupled receptor (GPCR) proteins, in addition to the conserved RGS domain. We found that MoRgs7, together with Gα MoMagA but not MoRgs8, undergoes endocytic transport from the plasma membrane to the endosome upon sensing of surface hydrophobicity. We also found that MoRgs7 can interact with hydrophobic surfaces via a hydrophobic interaction, leading to the perception of environmental hydrophobic cues. Moreover, we found that MoRgs7-MoMagA endocytosis is regulated by actin patch-associated protein MoCrn1, linking it to cAMP signaling. Our studies provided evidence suggesting that MoRgs7 could also function in a GPCR-like manner to sense environmental signals and it, together with additional proteins of diverse functions, promotes cAMP signaling required for developmental processes underlying appressorium function and pathogenicity.Author summaryThe 7-TM domain is considered the hallmark of GPCR proteins, which activate G proteins upon ligand binding and undergo endocytosis for regeneration or recycling. Among eight RGS and RGS-like proteins of M. oryzae, MoRgs7 and MoRgs8 contain the 7-TM domain in addition to the RGS domain. We found that MoRgs7 can form hydrophobic interactions with the hydrophobic surface. This interaction is important in sensing hydrophobic cues by the fungus. We also found that, in response to surface hydrophobicity, MoRgs7 couples with Gα subunit MoMagA to undergo endocytosis leading to the activation of cAMP signaling. Moreover, we found that such an endocytic event requires functions of the actin-binding protein MoCrn1. Our results revealed MoRgs7 functions as a GPCR-like receptor protein to sense surface cues and activate signaling required for pathogenesis, providing new insights into G-protein regulatory mechanisms in this and other pathogenic fungi.

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Homology modelling of an antimicrobial protein, Ace-AMP1, from lipid transfer protein structures

Folding and Design ◽

10.1016/s1359-0278(97)00025-4 ◽

1997 ◽

Vol 2 (3) ◽

pp. 183-192 ◽

Cited By ~ 14

Author(s):

Jéroˆme Gomar ◽

Patrick Sodano ◽

Marius Ptak ◽

Franc¸oise Vovelle

Keyword(s):

Lipid Transfer Protein ◽

Homology Modelling ◽

Protein Structures ◽

Antimicrobial Protein ◽

Lipid Transfer ◽

Transfer Protein

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Friend or Foe: Hybrid proline-rich proteins determine how plants interact with and respond to beneficial and pathogenic microbes

10.1101/2021.08.26.457806 ◽

2021 ◽

Author(s):

Zeeshan Zahoor Banday ◽

Nicolas M Cecchini ◽

Allison T Scott ◽

Ciara T Hu ◽

Rachael C Filzen ◽

...

Keyword(s):

Pseudomonas Syringae ◽

Transmembrane Domain ◽

Lipid Transfer Protein ◽

Family Members ◽

Subcellular Location ◽

Protein Domain ◽

Lipid Transfer ◽

Growth Responses ◽

Signal Molecule ◽

Systemic Immunity

Plant plastids generate signals, including some derived from lipids, that need to be mobilized to effect signaling. We used informatics to discover potential plastid membrane proteins involved in microbial responses. Among these are proteins co-regulated with the systemic immunity component AZI1, a hybrid proline-rich protein (HyPRP) and HyPRP superfamily members. HyPRPs have a transmembrane domain, a proline-rich region (PRR) and a lipid transfer protein domain. The precise subcellular location(s) and function(s) is unknown for most HyPRP family members. As predicted by informatics, a subset of HyPRPs have a pool of protein that targets plastid outer envelope membranes (OEMs) via a mechanism that requires the PRR. Additionally, two HyPRPs may be associated with thylakoid membranes. Most of the plastid and non-plastid localized family members also have pools that localize to endoplasmic reticulum, plasma membrane or plasmodesmata. HyPRPs with plastid pools regulate, positively or negatively, systemic immunity against the pathogen Pseudomonas syringae. HyPRPs also regulate the interaction with the plant growth promoting rhizobacteria Pseudomonas simiae WCS417 in the roots to influence colonization, root system architecture and/or biomass. Thus, HyPRPs have broad and distinct roles in immune, development and growth responses to microbes and reside at sites that may facilitate signal molecule transport.

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Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

Plant Tissue Culture and Biotechnology ◽

10.3329/ptcb.v19i2.5439 ◽

1970 ◽

Vol 19 (2) ◽

pp. 217-226

Author(s):

S. M. Minhaz Ud-Dean ◽

Mahdi Muhammad Moosa

Keyword(s):

Protein Structure ◽

Dihedral Angle ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Protein Structures ◽

Angle Distribution ◽

Ramachandran Plot ◽

Specific Data ◽

Specific Distribution ◽

Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

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Lipid transfer protein allergy in poland: a dominant role for mugwort pollen as a primary sensitizer.

10.26226/morressier.5acc8ad1d462b8028d89ac4c ◽

2018 ◽

Author(s):

Adam Wawrzenczyk

Keyword(s):

Lipid Transfer Protein ◽

Dominant Role ◽

Lipid Transfer ◽

Transfer Protein

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Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

10.26434/chemrxiv.13299197.v1 ◽

2020 ◽

Author(s):

Lim Heo ◽

Collin Arbour ◽

Michael Feig

Keyword(s):

Molecular Dynamics ◽

Molecular Dynamics Simulation ◽

Structure Prediction ◽

Protein Structures ◽

Conformational Space ◽

Dynamics Simulation ◽

Model Refinement ◽

Protein Model ◽

Lower Accuracy ◽

Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

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