scholarly journals ProteinFishing: a protein complex generator within the ModelX toolsuite

2020 ◽  
Vol 36 (14) ◽  
pp. 4208-4210
Author(s):  
Damiano Cianferoni ◽  
Leandro G Radusky ◽  
Sarah A Head ◽  
Luis Serrano ◽  
Javier Delgado
Keyword(s):  
Force Field ◽  
Protein Interactions ◽  
Protein Complex ◽  
Interface Design ◽  
Protein Complexes ◽  
Interleukin 10 ◽  
Supplementary Information ◽  
For Profit ◽  
Knowledge Based ◽  
Command Line Tool

Abstract Summary Accurate 3D modelling of protein–protein interactions (PPI) is essential to compensate for the absence of experimentally determined complex structures. Here, we present a new set of commands within the ModelX toolsuite capable of generating atomic-level protein complexes suitable for interface design. Among these commands, the new tool ProteinFishing proposes known and/or putative alternative 3D PPI for a given protein complex. The algorithm exploits backbone compatibility of protein fragments to generate mutually exclusive protein interfaces that are quickly evaluated with a knowledge-based statistical force field. Using interleukin-10-R2 co-crystalized with interferon-lambda-3, and a database of X-ray structures containing interleukin-10, this algorithm was able to generate interleukin-10-R2/interleukin-10 structural models in agreement with experimental data. Availability and implementation ProteinFishing is a portable command-line tool included in the ModelX toolsuite, written in C++, that makes use of an SQL (tested for MySQL and MariaDB) relational database delivered with a template SQL dump called FishXDB. FishXDB contains the empty tables of ModelX fragments and the data used by the embedded statistical force field. ProteinFishing is compiled for Linux-64bit, MacOS-64bit and Windows-32bit operating systems. This software is a proprietary license and is distributed as an executable with its correspondent database dumps. It can be downloaded publicly at http://modelx.crg.es/. Licenses are freely available for academic users after registration on the website and are available under commercial license for for-profit organizations or companies. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

2020 ◽  
Author(s):  
Isak Johansson-Åkhe ◽  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Protein Interactions ◽  
Protein Complex ◽  
Protein Complexes ◽  
Conformational Space ◽  
Supplementary Information ◽  
Large Set ◽  
Protein Protein Interactions ◽  
Validation Data ◽  
Pairwise Interactions ◽  
Peptide Conformations

AbstractMotivationPeptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modelling such interactions is to exhaustively sample the conformational space by fast-fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection in short enough time for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical pairwise potentials.ResultsWe present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine-learning based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph-network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD<4Å. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC of circa 0.69. When included as selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of Medium and High quality models produced by 80% and 40%, respectively.AvailabilityThe program is available from: http://wallnerlab.org/InterPepRankContactBjörn Wallner [email protected] informationSupplementary data are available at BioRxiv online.

scholarly journals Identification and Molecular Characterization of a Gene Encoding a Protective Leishmania amazonensis Trp-Asp (WD) Protein

2004 ◽  
Vol 72 (4) ◽  
pp. 2194-2202 ◽  
Author(s):  
Kimberly Campbell ◽  
Vsevolod Popov ◽  
Lynn Soong
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Single Copy ◽  
Interleukin 10 ◽  
Leishmania Amazonensis ◽  
Cdna Expression Library ◽  
Protein Protein Interactions ◽  
Expression Library ◽  
Gene Encoding ◽  
Mucocutaneous Leishmaniasis

ABSTRACT Several Leishmania proteins have been identified and characterized in pursuit of understanding pathogenesis and protection in cutaneous leishmaniasis. In the present study, we utilized sera from infected BALB/c mice to screen a Leishmania amazonensis amastigote cDNA expression library and obtained the full-length gene that encodes a novel Trp-Asp (WD) protein designated LAWD (for Leishmania antigenic WD protein). The WD family of proteins mediates protein-protein interactions and coordinates the formation of protein complexes. The single-copy LAWD gene is transcribed as a ∼3.1-kb mRNA in both promastigotes and amastigotes, with homologues being detected in several other Leishmania species. Immunoelectron microscopy revealed a predominant localization of the LAWD protein in the flagellar pocket. Analyses of sera from human patients with cutaneous and mucocutaneous leishmaniasis indicated that these individuals mounted significant humoral responses against LAWD. Given that recombinant LAWD protein elicited the production of high levels of gamma interferon, but no detectable levels of interleukin-10 (IL-10), in CD4+ cells of L. amazonensis-infected mice, we further examined whether LAWD could elicit protective immunity. DNA vaccination with the LAWD and IL-12 genes significantly delayed lesion development, which correlated with a dramatic reduction in parasite burdens. Thus, we have successfully identified a promising vaccine candidate and antigenic vehicle to aid in the dissection of the complicated pathogenic immune response of L. amazonensis.

scholarly journals InterPep2: global peptide–protein docking using interaction surface templates

2020 ◽  
Vol 36 (8) ◽  
pp. 2458-2465 ◽  
Author(s):  
Isak Johansson-Åkhe ◽  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Structural Features ◽  
Protein Docking ◽  
Supplementary Information ◽  
Peptide Ligand ◽  
Protein Protein Interactions ◽  
Intrinsically Disordered ◽  
Intrinsically Disordered Regions ◽  
Improved Performance

Abstract Motivation Interactions between proteins and peptides or peptide-like intrinsically disordered regions are involved in many important biological processes, such as gene expression and cell life-cycle regulation. Experimentally determining the structure of such interactions is time-consuming and difficult because of the inherent flexibility of the peptide ligand. Although several prediction-methods exist, most are limited in performance or availability. Results InterPep2 is a freely available method for predicting the structure of peptide–protein interactions. Improved performance is obtained by using templates from both peptide–protein and regular protein–protein interactions, and by a random forest trained to predict the DockQ-score for a given template using sequence and structural features. When tested on 252 bound peptide–protein complexes from structures deposited after the complexes used in the construction of the training and templates sets of InterPep2, InterPep2-Refined correctly positioned 67 peptides within 4.0 Å LRMSD among top10, similar to another state-of-the-art template-based method which positioned 54 peptides correctly. However, InterPep2 displays a superior ability to evaluate the quality of its own predictions. On a previously established set of 27 non-redundant unbound-to-bound peptide–protein complexes, InterPep2 performs on-par with leading methods. The extended InterPep2-Refined protocol managed to correctly model 15 of these complexes within 4.0 Å LRMSD among top10, without using templates from homologs. In addition, combining the template-based predictions from InterPep2 with ab initio predictions from PIPER-FlexPepDock resulted in 22% more near-native predictions compared to the best single method (22 versus 18). Availability and implementation The program is available from: http://wallnerlab.org/InterPep2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Cryo-EM structure of an activated GPCR-G protein complex in lipid nanodiscs

2020 ◽  
Author(s):  
Gerhard Wagner ◽  
Meng Zhang ◽  
Miao Gui ◽  
Zi-Fu Wang ◽  
Christoph Gorgulla ◽  
...  
Keyword(s):  
Complex Formation ◽  
G Protein ◽  
Lipid Bilayer ◽  
Lipid Bilayers ◽  
Protein Interactions ◽  
Protein Complex ◽  
Lipid Membrane ◽  
Protein Complexes ◽  
Structural Basis ◽  
Downstream Signaling

Abstract G protein coupled receptors (GPCRs) are the largest superfamily of transmembrane proteins and the targets of over 30% of currently marketed pharmaceuticals. Although several structures have been solved for GPCR-G protein complexes, structural studies of the complex in a physiological lipid membrane environment are lacking. Here, we report cryo-EM structures of lipid bilayer-bound complexes of neurotensin, neurotensin receptor 1, and Gai1b1g1 protein in two conformational states, resolved to 4.1 and 4.2 Å resolution. The structures were determined in lipid bilayer without any stabilizing antibodies/nanobodies, and thus provide a native-like platform for understanding the structural basis of GPCR-G protein complex formation. Our structures reveal an extended network of protein-protein interactions at the GPCR-G protein interface compared to in detergent micelles, defining roles for the lipid membrane in modulating the structure and dynamics of complex formation, and providing a molecular explanation for the stronger interaction between GPCR and G protein in lipid bilayers. We propose a detailed allosteric mechanism for GDP release, providing new insights into the activation of G proteins for downstream signaling.

scholarly journals Nuclear membrane-tethered FRAP method for measuring protein complex off-rates in live cells

2021 ◽  
Author(s):  
Lindsey R. Pack ◽  
Leighton H. Daigh ◽  
Mingyu Chung ◽  
Tobias Meyer
Keyword(s):  
Protein Interactions ◽  
Nuclear Membrane ◽  
Protein Complex ◽  
Protein Complexes ◽  
Live Cells ◽  
Cdk Inhibitors ◽  
Cyclin Dependent Kinase ◽  
Protein Protein Interactions ◽  
The Stability

Abstract Understanding the stability or binding affinity of protein complex members is important for understanding their regulation and roles in cells. While there are many biochemical methods to measure protein-protein interactions in vitro, these methods often rely on the ability to robustly purify components individually. Moreover, few methods have been developed to study protein complexes within live cells. Binding parameters for cyclin-dependent kinase (CDK) complexes have been challenging to measure due to difficulty expressing and purifying CDKs separately from activating cyclins. Here, we develop a method to measure off-rates of protein complex components in live-cells. Our method relies on the stable tethering of CDK to the inner nuclear membrane (Figure 1), and the utilization of FRAP to measure the off-rate of soluble, fluorescently-tagged CDK binding proteins. We use this method to study dimeric CDK complexes, measuring the off-rates of cyclins or INK4 CDK inhibitor p16 from CDKs, and trimeric CDK complexes, measuring the off-rate of cyclins and CIP/KIP CDK inhibitors p21 and p27 when bound together.

RoDiCE: robust differential protein co-expression analysis for cancer complexome

2021 ◽  
Author(s):  
Yusuke Matsui ◽  
Yuichi Abe ◽  
Kohei Uno ◽  
Satoru Miyano
Keyword(s):  
Protein Complex ◽  
Drug Targets ◽  
Large Scale ◽  
Protein Complexes ◽  
Specific Protein ◽  
Identification Accuracy ◽  
Supplementary Information ◽  
Full Spectrum ◽  
Differential Protein ◽  
Proteomic Data

Abstract Motivation The full spectrum of abnormalities in cancer-associated protein complexes remains largely unknown. Comparing the co-expression structure of each protein complex between tumor and healthy cells may provide insights regarding cancer-specific protein dysfunction. However, the technical limitations of mass spectrometry-based proteomics, including contamination with biological protein variants, causes noise that leads to non-negligible over- (or under-) estimating co-expression. Results We propose a robust algorithm for identifying protein complex aberrations in cancer based on differential protein co-expression testing. Our method based on a copula is sufficient for improving identification accuracy with noisy data compared to conventional linear correlation-based approaches. As an application, we use large-scale proteomic data from renal cancer to show that important protein complexes, regulatory signaling pathways and drug targets can be identified. The proposed approach surpasses traditional linear correlations to provide insights into higher-order differential co-expression structures. Availability and implementation https://github.com/ymatts/RoDiCE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Electron-transfer studies involving flavodoxin and a natural redox partner, the iron protein of nitrogenase. Conformational constraints on protein-protein interactions and the kinetics of electron transfer within the protein complex

1988 ◽  
Vol 253 (2) ◽  
pp. 587-595 ◽  
Author(s):  
R N Thorneley ◽  
J Deistung
Keyword(s):  
Electron Transfer ◽  
Protein Interactions ◽  
Protein Complex ◽  
Protein Complexes ◽  
Linear Free Energy Relationship ◽  
Midpoint Potential ◽  
Linear Free Energy ◽  
Redox Partner ◽  
Fe Protein ◽  
Kinetics Of

The kinetics of electron-transfer reactions involving flavodoxins from Klebsiella pneumoniae (KpFld), Azotobacter chroococcum (AcFld), Anacystis nidulans (AnFld) and Megasphaera elsdenii (MeFld), the free, MgADP-bound and MgATP-bound forms of the Fe protein component of nitrogenase from K. pneumoniae [Kp2, Kp2(MgADP)2 and Kp2(MgATP)2] and Na2S2O4 were studied by stopped-flow spectrophotometry. Kinetic evidence was obtained for the formation of binary protein complexes involving KpFldSQ (semiquinone) with either Kp2(MgADP)2 (KD = 49 microM) or Kp2(MgATP)2 (KD = 13 microM) but not with Kp2 (KD greater than 730 microM). The binding of 2MgATP or 2MgADP to Kp2 therefore not only shifts the midpoint potential (Em) of the [4Fe-4S] centre from -200 mV to -320 mV or -350 mV respectively but also changes the affinity of Kp2 for KpFldSQ. Thermodynamically unfavourable electron from Kp2(MgADP)2 and Kp2(MgATP)2 to KpFldSQ occurs within the protein complexes with k = 1.2 s-1 (delta E = -72 mV) and 0.5 s-1 (delta E = -120 mV) respectively. Although AcFldSQ is reduced by Kp2, Kp2(MgADP)2 and Kp2(MgATP)2 (k = 8 x 10(3), 2.4 x 10(3) and 9 x 10(2) M-1.s-1 respectively), protein-complex formation is weak in each case (KD greater than 700 microM). Electron transfer in the physiologically important and thermodynamically favourable direction from Kp2FldHQ (hydroquinone) and AcFldHQ to Kp2ox.(MgADP)2 (the state of Kp2 that accepts electrons from FldHQ in the catalytic cycle of nitrogenase) is rapid (k greater than 10(6) M-1.s-1). The second-order rate constants for the reduction of KpFldSQ, AcFldSQ, AnFldSQ and MeFldSQ by SO2.- (active reductant formed by the predissociation of S2O4(2-) ion) exhibited the linear free-energy relationship predicted by the Marcus theory of electron transfer.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein–protein complexes

2020 ◽  
Author(s):  
Pep Amengual-Rigo ◽  
Juan Fernández-Recio ◽  
Victor Guallar
Keyword(s):  
Open Source ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Selection Procedure ◽  
Supplementary Information ◽  
Computational Time ◽  
Contact Potential ◽  
Three Body ◽  
The Impact

Abstract Motivation Single protein residue mutations may reshape the binding affinity of protein–protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein–protein complexes trained on interactome data. Results Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein–protein mutations using UEP, a fast and reliable open-source code. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Integrating ab initio and template-based algorithms for protein–protein complex structure prediction

2019 ◽  
Vol 36 (3) ◽  
pp. 751-757 ◽  
Author(s):  
Sweta Vangaveti ◽  
Thom Vreven ◽  
Yang Zhang ◽  
Zhiping Weng
Keyword(s):  
Protein Complex ◽  
Structure Prediction ◽  
Protein Complexes ◽  
Complex Structure ◽  
Protein Docking ◽  
Supplementary Information ◽  
Test Case ◽  
Binding Modes ◽  
Success Rates ◽  
Template Free

Abstract Motivation Template-based and template-free methods have both been widely used in predicting the structures of protein–protein complexes. Template-based modeling is effective when a reliable template is available, while template-free methods are required for predicting the binding modes or interfaces that have not been previously observed. Our goal is to combine the two methods to improve computational protein–protein complex structure prediction. Results Here, we present a method to identify and combine high-confidence predictions of a template-based method (SPRING) with a template-free method (ZDOCK). Cross-validated using the protein–protein docking benchmark version 5.0, our method (ZING) achieved a success rate of 68.2%, outperforming SPRING and ZDOCK, with success rates of 52.1% and 35.9% respectively, when the top 10 predictions were considered per test case. In conclusion, a statistics-based method that evaluates and integrates predictions from template-based and template-free methods is more successful than either method independently. Availability and implementation ZING is available for download as a Github repository (https://github.com/weng-lab/ZING.git). Supplementary information Supplementary data are available at Bioinformatics online.

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