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 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.

Structure-aware protein–protein interaction site prediction using deep graph convolutional network

2021 ◽  
Author(s):  
Qianmu Yuan ◽  
Jianwen Chen ◽  
Huiying Zhao ◽  
Yaoqi Zhou ◽  
Yuedong Yang
Keyword(s):  
Protein Interactions ◽  
Spatial Information ◽  
Screening Tools ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Convolutional Network ◽  
Source Codes ◽  
Site Prediction ◽  
Protein Protein Interaction ◽  
Mapping Techniques

Abstract Motivation Protein–protein interactions (PPI) play crucial roles in many biological processes, and identifying PPI sites is an important step for mechanistic understanding of diseases and design of novel drugs. Since experimental approaches for PPI site identification are expensive and time-consuming, many computational methods have been developed as screening tools. However, these methods are mostly based on neighbored features in sequence, and thus limited to capture spatial information. Results We propose a deep graph-based framework deep Graph convolutional network for Protein–Protein-Interacting Site prediction (GraphPPIS) for PPI site prediction, where the PPI site prediction problem was converted into a graph node classification task and solved by deep learning using the initial residual and identity mapping techniques. We showed that a deeper architecture (up to eight layers) allows significant performance improvement over other sequence-based and structure-based methods by more than 12.5% and 10.5% on AUPRC and MCC, respectively. Further analyses indicated that the predicted interacting sites by GraphPPIS are more spatially clustered and closer to the native ones even when false-positive predictions are made. The results highlight the importance of capturing spatially neighboring residues for interacting site prediction. Availability and implementation The datasets, the pre-computed features, and the source codes along with the pre-trained models of GraphPPIS are available at https://github.com/biomed-AI/GraphPPIS. The GraphPPIS web server is freely available at https://biomed.nscc-gz.cn/apps/GraphPPIS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Group I Metabotropic Glutamate Receptors and Interacting Partners: An Update

2022 ◽  
Vol 23 (2) ◽  
pp. 840
Author(s):  
Li-Min Mao ◽  
Alaya Bodepudi ◽  
Xiang-Ping Chu ◽  
John Q. Wang
Keyword(s):  
Synaptic Plasticity ◽  
Protein Interactions ◽  
Metabotropic Glutamate Receptors ◽  
Adaptor Proteins ◽  
Mammalian Brain ◽  
Amino Acid Residues ◽  
Protein Protein Interactions ◽  
Metabotropic Glutamate ◽  
Group I ◽  
Associated Proteins

Group I metabotropic glutamate (mGlu) receptors (mGlu1/5 subtypes) are G protein-coupled receptors and are broadly expressed in the mammalian brain. These receptors play key roles in the modulation of normal glutamatergic transmission and synaptic plasticity, and abnormal mGlu1/5 signaling is linked to the pathogenesis and symptomatology of various mental and neurological disorders. Group I mGlu receptors are noticeably regulated via a mechanism involving dynamic protein–protein interactions. Several synaptic protein kinases were recently found to directly bind to the intracellular domains of mGlu1/5 receptors and phosphorylate the receptors at distinct amino acid residues. A variety of scaffolding and adaptor proteins also interact with mGlu1/5. Constitutive or activity-dependent interactions between mGlu1/5 and their interacting partners modulate trafficking, anchoring, and expression of the receptors. The mGlu1/5-associated proteins also finetune the efficacy of mGlu1/5 postreceptor signaling and mGlu1/5-mediated synaptic plasticity. This review analyzes the data from recent studies and provides an update on the biochemical and physiological properties of a set of proteins or molecules that interact with and thus regulate mGlu1/5 receptors.

scholarly journals An Integrative Computational Approach for the Prediction of Human-Plasmodium Protein-Protein Interactions

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Kais Ghedira ◽  
Yosr Hamdi ◽  
Abir El Béji ◽  
Houcemeddine Othman
Keyword(s):  
Protein Interactions ◽  
Computational Approach ◽  
Protein Protein Interactions ◽  
Human Red Blood Cells ◽  
Pharmacological Targets ◽  
Host Pathogen ◽  
Human Proteins ◽  
Life Threatening ◽  
Similarity Method ◽  
Human Plasmodium

Host-pathogen molecular cross-talks are critical in determining the pathophysiology of a specific infection. Most of these cross-talks are mediated via protein-protein interactions between the host and the pathogen (HP-PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life-threatening disease caused by an obligate intracellular parasite belonging to the Plasmodium genus, of which P. falciparum is the most prevalent. Several previous studies predicted human-plasmodium protein-protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host-pathogen interaction networks. To predict potential putative HP-PPIs, we use an integrative computational approach based on the combination of multiple OMICS-based methods including human red blood cells (RBC) and Plasmodium falciparum 3D7 strain expressed proteins, domain-domain based PPI, similarity of gene ontology terms, structure similarity method homology identification, and machine learning prediction. Our results reported a set of 716 protein interactions involving 302 human proteins and 130 Plasmodium proteins. This work provides a list of potential human-Plasmodium interacting proteins. These findings will contribute to better understand the mechanisms underlying the molecular determinism of malaria disease and potentially to identify candidate pharmacological targets.

scholarly journals Characterization of COMMD protein–protein interactions in NF-κB signalling

2006 ◽  
Vol 398 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Prim de Bie ◽  
Bart van de Sluis ◽  
Ezra Burstein ◽  
Karen J. Duran ◽  
Ruud Berger ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Copper Metabolism ◽  
Nuclear Factor Κb ◽  
Inhibitory Effect ◽  
Protein Family ◽  
Amino Acid Residues ◽  
Protein Protein Interactions ◽  
Necrosis Factor

COMMD [copper metabolism gene MURR1 (mouse U2af1-rs1 region 1) domain] proteins constitute a recently identified family of NF-κB (nuclear factor κB)-inhibiting proteins, characterized by the presence of the COMM domain. In the present paper, we report detailed investigation of the role of this protein family, and specifically the role of the COMM domain, in NF-κB signalling through characterization of protein–protein interactions involving COMMD proteins. The small ubiquitously expressed COMMD6 consists primarily of the COMM domain. Therefore COMMD1 and COMMD6 were analysed further as prototype members of the COMMD protein family. Using specific antisera, interaction between endogenous COMMD1 and COMMD6 is described. This interaction was verified by independent techniques, appeared to be direct and could be detected throughout the whole cell, including the nucleus. Both proteins inhibit TNF (tumour necrosis factor)-induced NF-κB activation in a non-synergistic manner. Mutation of the amino acid residues Trp24 and Pro41 in the COMM domain of COMMD6 completely abolished the inhibitory effect of COMMD6 on TNF-induced NF-κB activation, but this was not accompanied by loss of interaction with COMMD1, COMMD6 or the NF-κB subunit RelA. In contrast with COMMD1, COMMD6 does not bind to IκBα (inhibitory κBα), indicating that both proteins inhibit NF-κB in an overlapping, but not completely similar, manner. Taken together, these data support the significance of COMMD protein–protein interactions and provide new mechanistic insight into the function of this protein family in NF-κB signalling.

scholarly journals Coevolution-based prediction of protein–protein interactions in polyketide biosynthetic assembly lines

2020 ◽  
Vol 36 (19) ◽  
pp. 4846-4853 ◽  
Author(s):  
Yan Wang ◽  
Miguel Correa Marrero ◽  
Marnix H Medema ◽  
Aalt D J van Dijk
Keyword(s):  
Protein Interactions ◽  
Antitumor Agents ◽  
Acyl Carrier Protein ◽  
Specific Protein ◽  
Combinatorial Biosynthesis ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Assembly Lines ◽  
Specific Order ◽  
Encoding Genes

Abstract Motivation Polyketide synthases (PKSs) are enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by cis-AT modular PKSs, which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein–protein interactions (PPIs). The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Results Here, we introduce PKSpop, which uses a coevolution-based PPI algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (93% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for PPIs than coevolution between ketosynthase and acyl carrier protein domains. Availability and implementation The code is available on http://www.bif.wur.nl/ (under ‘Software’). Supplementary information Supplementary data are available 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 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 A distinct class of plant and animal viral proteins that disrupt mitosis by directly interrupting the mitotic entry switch Wee1-Cdc25-Cdk1

2020 ◽  
Vol 6 (20) ◽  
pp. eaba3418
Author(s):  
Huaibing Jin ◽  
Zhiqiang Du ◽  
Yanjing Zhang ◽  
Judit Antal ◽  
Zongliang Xia ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Viral Proteins ◽  
Plant Viruses ◽  
Amino Acid Residues ◽  
Protein Protein Interactions ◽  
Common Amino Acid ◽  
Mitotic Entry ◽  
Distinct Class ◽  
Barley Yellow Dwarf ◽  
Hiv 1

Many animal viral proteins, e.g., Vpr of HIV-1, disrupt host mitosis by directly interrupting the mitotic entry switch Wee1-Cdc25-Cdk1. However, it is unknown whether plant viruses may use this mechanism in their pathogenesis. Here, we report that the 17K protein, encoded by barley yellow dwarf viruses and related poleroviruses, delays G2/M transition and disrupts mitosis in both host (barley) and nonhost (fission yeast, Arabidopsis thaliana, and tobacco) cells through interrupting the function of Wee1-Cdc25-CDKA/Cdc2 via direct protein-protein interactions and alteration of CDKA/Cdc2 phosphorylation. When ectopically expressed, 17K disrupts the mitosis of cultured human cells, and HIV-1 Vpr inhibits plant cell growth. Furthermore, 17K and Vpr share similar secondary structural feature and common amino acid residues required for interacting with plant CDKA. Thus, our work reveals a distinct class of mitosis regulators that are conserved between plant and animal viruses and play active roles in viral pathogenesis.

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