scholarly journals Predicting virus-receptor mutant binding by molecular dynamics simulation

2013 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Protein Interactions ◽  
Tight Binding ◽  
Dynamics Simulation ◽  
Virus Protein ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Protein Protein Interaction ◽  
Human Transferrin Receptor ◽  
Machupo Virus ◽  
Receptor Mutant

Existing computational methods to predict protein–protein interaction affinity often perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein–protein interface. Our method is based on extensive non-equilibrium all atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the max imum applied force during a pulling simulation and the area under the force-versus-distance curve. We find that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our method provides an elegant framework to compare the effects of multi ple mutations, individually and jointly, on protein–protein interactions.

scholarly journals Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Machupo Virus

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

scholarly journals Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Protein Protein Interactions ◽  
Distance Curve ◽  
Machupo Virus

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

scholarly journals Analyzing Machupo virus-receptor binding by molecular dynamics simulations

2014 ◽  
Author(s):  
Austin G Meyer ◽  
Sara L Sawyer ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Interactions ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Virus Protein ◽  
Test Case ◽  
Distance Curve ◽  
Machupo Virus ◽  
Dynamics Simulations

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-­protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non­alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-­virus protein-­protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-­versus-­distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-­crystal structure shows two large hydrogen-­bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-­resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein­ protein interactions.

scholarly journals Prediction of antiviral drugs against African swine fever viruses based on protein–protein interaction analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8855
Author(s):  
Zhaozhong Zhu ◽  
Yunshi Fan ◽  
Yang Liu ◽  
Taijiao Jiang ◽  
Yang Cao ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Broad Spectrum ◽  
Antiviral Drug ◽  
African Swine Fever ◽  
Antiviral Drugs ◽  
Dynamics Simulation ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Spectrum Effect

The African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is currently no effective antiviral drug or vaccine against the virus. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein–protein interactions between ASFV and swine were curated by integration of protein–protein interactions from multiple sources. Thirty-eight swine proteins were observed to interact with ASFVs and were defined as ASFV-interacting swine proteins. The ASFV-interacting swine proteins were found to play a central role in the swine protein–protein interaction network, with significant larger degree, betweenness and smaller shortest path length than other swine proteins. Some of ASFV-interacting swine proteins also interacted with several other viruses and could be taken as potential targets of drugs for broad-spectrum effect, such as HSP90AB1. Finally, the antiviral drugs which targeted ASFV-interacting swine proteins and ASFV proteins were predicted. Several drugs with either broad-spectrum effect or high specificity on ASFV-interacting swine proteins were identified, such as Polaprezinc and Geldanamycin. Structural modeling and molecular dynamics simulation showed that Geldanamycin could bind with swine HSP90AB1 stably. This work could not only deepen our understanding towards the ASFV-swine interactions, but also help for the development of effective antiviral drugs against the ASFVs.

An efficient transient assays system using Agrobacterium-mediated transformation of onion (Allium cepa) epidermal cells

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Yu-Miao Zhang ◽  
Jun Wang ◽  
Tao Wu
Keyword(s):  
Subcellular Localization ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Epidermal Cells ◽  
Cyclin Dependent Kinase ◽  
Protein Subcellular Localization ◽  
Protein Protein Interactions ◽  
Efficient System ◽  
Protein Protein Interaction ◽  
Onion Epidermal Cells

In this study, the Agrobacterium infection medium, infection duration, detergent, and cell density were optimized. The sorghum-based infection medium (SbIM), 10-20 min infection time, addition of 0.01% Silwet L-77, and Agrobacterium optical density at 600 nm (OD600), improved the competence of onion epidermal cells to support Agrobacterium infection at >90% efficiency. Cyclin-dependent kinase D-2 (CDKD-2) and cytochrome c-type biogenesis protein (CYCH), protein-protein interactions were localized. The optimized procedure is a quick and efficient system for examining protein subcellular localization and protein-protein interaction.

Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Universal Screening Methods and Applications of ThermoFluor®

2006 ◽  
Vol 11 (7) ◽  
pp. 854-863 ◽  
Author(s):  
Maxwell D. Cummings ◽  
Michael A. Farnum ◽  
Marina I. Nelen
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Unfolding ◽  
Direct Detection ◽  
Functional Characterization ◽  
Screening Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Bacterial Enzyme ◽  
Research Problems

The genomics revolution has unveiled a wealth of poorly characterized proteins. Scientists are often able to produce milligram quantities of proteins for which function is unknown or hypothetical, based only on very distant sequence homology. Broadly applicable tools for functional characterization are essential to the illumination of these orphan proteins. An additional challenge is the direct detection of inhibitors of protein-protein interactions (and allosteric effectors). Both of these research problems are relevant to, among other things, the challenge of finding and validating new protein targets for drug action. Screening collections of small molecules has long been used in the pharmaceutical industry as 1 method of discovering drug leads. Screening in this context typically involves a function-based assay. Given a sufficient quantity of a protein of interest, significant effort may still be required for functional characterization, assay development, and assay configuration for screening. Increasingly, techniques are being reported that facilitate screening for specific ligands for a protein of unknown function. Such techniques also allow for function-independent screening with better characterized proteins. ThermoFluor®, a screening instrument based on monitoring ligand effects on temperature-dependent protein unfolding, can be applied when protein function is unknown. This technology has proven useful in the decryption of an essential bacterial enzyme and in the discovery of a series of inhibitors of a cancer-related, protein-protein interaction. The authors review some of the tools relevant to these research problems in drug discovery, and describe our experiences with 2 different proteins.

scholarly journals Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Dan Tan ◽  
Qiang Li ◽  
Mei-Jun Zhang ◽  
Chao Liu ◽  
Chengying Ma ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Structural Information ◽  
Affinity Purification ◽  
Protein Protein Interactions ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Lysine Residues ◽  
Spacer Arm ◽  
Cross Linker ◽  
Protein Nucleic Acid

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

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