NMR and molecular modeling reveal specificity of the interactions between CXCL14 and glycosaminoglycans

Glycobiology ◽  
2019 ◽  
Vol 29 (10) ◽  
pp. 715-725 ◽  
Author(s):  
Anja Penk ◽  
Lars Baumann ◽  
Daniel Huster ◽  
Sergey A Samsonov
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Dermatan Sulfate ◽  
Driving Forces ◽  
Immune Surveillance ◽  
Receptor Activation ◽  
Free Energy Calculations ◽  
Resonance Spectroscopy ◽  
Binding Modes ◽  
Gag Protein

Abstract CXCL14, chemokine (C-X-C motif) ligand 14, is a novel highly conserved chemokine with unique features. Despite exhibiting the typical chemokine fold, it has a very short N-terminus of just two amino acid residues responsible for chemokine receptor activation. CXCL14 actively participates in homeostatic immune surveillance of skin and mucosae, is linked to metabolic disorders and fibrotic lung diseases and possesses strong anti-angiogenic properties in early tumor development. In this work, we investigated the interaction of CXCL14 with various glycosaminoglycans (GAGs) by nuclear magnetic resonance spectroscopy, microscale thermophoresis, analytical heparin (HE) affinity chromatography and in silico approaches to understand the molecular basis of GAG-binding. We observed different GAG-binding modes specific for the GAG type used in the study. In particular, the CXCL14 epitope for HE suggests a binding pose distinguishable from the ones of the other GAGs investigated (hyaluronic acid, chondroitin sulfate-A/C, −D, dermatan sulfate). This observation is also supported by computational methods that included molecular docking, molecular dynamics and free energy calculations. Based on our results, we suggest that distinct GAG sulfation patterns confer specificity beyond simple electrostatic interactions usually considered to represent the driving forces in protein–GAG interactions. The CXCL14–GAG system represents a promising approach to investigate the specificity of GAG–protein interactions, which represents an important topic for developing the rational approaches to novel strategies in regenerative medicine.

scholarly journals Chondroitin Sulfate/Dermatan Sulfate-Protein Interactions and Their Biological Functions in Human Diseases: Implications and Analytical Tools

2021 ◽  
Vol 9 ◽  
Author(s):  
Bin Zhang ◽  
Lianli Chi
Keyword(s):  
Cell Surface ◽  
Chondroitin Sulfate ◽  
Protein Interactions ◽  
Dermatan Sulfate ◽  
Interaction Analysis ◽  
Structural Complexity ◽  
Protein Characterization ◽  
Biological Functions ◽  
Gag Protein ◽  
Core Proteins

Chondroitin sulfate (CS) and dermatan sulfate (DS) are linear anionic polysaccharides that are widely present on the cell surface and in the cell matrix and connective tissue. CS and DS chains are usually attached to core proteins and are present in the form of proteoglycans (PGs). They not only are important structural substances but also bind to a variety of cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes and fibrillary glycoproteins to execute series of important biological functions. CS and DS exhibit variable sulfation patterns and different sequence arrangements, and their molecular weights also vary within a large range, increasing the structural complexity and diversity of CS/DS. The structure-function relationship of CS/DS PGs directly and indirectly involves them in a variety of physiological and pathological processes. Accumulating evidence suggests that CS/DS serves as an important cofactor for many cell behaviors. Understanding the molecular basis of these interactions helps to elucidate the occurrence and development of various diseases and the development of new therapeutic approaches. The present article reviews the physiological and pathological processes in which CS and DS participate through their interactions with different proteins. Moreover, classic and emerging glycosaminoglycan (GAG)-protein interaction analysis tools and their applications in CS/DS-protein characterization are also discussed.

scholarly journals Glycosaminoglycan–Protein Interactions: The First Draft of the Glycosaminoglycan Interactome

2020 ◽  
pp. 002215542094640 ◽  
Author(s):  
Sylvain D. Vallet ◽  
Olivier Clerc ◽  
Sylvie Ricard-Blum
Keyword(s):  
Extracellular Matrix ◽  
Protein Interactions ◽  
Dermatan Sulfate ◽  
Protein Complexes ◽  
Keratan Sulfate ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Matrix Proteins ◽  
Matrix Assembly ◽  
Gag Protein

The six mammalian glycosaminoglycans (GAGs), chondroitin sulfate, dermatan sulfate, heparin, heparan sulfate, hyaluronan, and keratan sulfate, are linear polysaccharides. Except for hyaluronan, they are sulfated to various extent, and covalently attached to proteins to form proteoglycans. GAGs interact with growth factors, morphogens, chemokines, extracellular matrix proteins and their bioactive fragments, receptors, lipoproteins, and pathogens. These interactions mediate their functions, from embryonic development to extracellular matrix assembly and regulation of cell signaling in various physiological and pathological contexts such as angiogenesis, cancer, neurodegenerative diseases, and infections. We give an overview of GAG–protein interactions (i.e., specificity and chemical features of GAG- and protein-binding sequences), and review the available GAG–protein interaction networks. We also provide the first comprehensive draft of the GAG interactome composed of 832 biomolecules (827 proteins and five GAGs) and 932 protein–GAG interactions. This network is a scaffold, which in the future should integrate structures of GAG–protein complexes, quantitative data of the abundance of GAGs in tissues to build tissue-specific interactomes, and GAG interactions with metal ions such as calcium, which plays a major role in the assembly of the extracellular matrix and its interactions with cells. This contextualized interactome will be useful to identify druggable GAG–protein interactions for therapeutic purpose:

scholarly journals Insights from Free-Energy Calculations: Protein Conformational Equilibrium, Driving Forces, and Ligand-Binding Modes

2012 ◽  
Vol 103 (2) ◽  
pp. 342-351 ◽  
Author(s):  
Yu-ming M. Huang ◽  
Wei Chen ◽  
Michael J. Potter ◽  
Chia-en A. Chang
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Conformational Equilibrium ◽  
Driving Forces ◽  
Free Energy Calculations ◽  
Binding Modes ◽  
Energy Calculations

scholarly journals Structural basis for chemokine recognition and receptor activation of chemokine receptor CCR5

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Hui Zhang ◽  
Kun Chen ◽  
Qiuxiang Tan ◽  
Qiang Shao ◽  
Shuo Han ◽  
...  
Keyword(s):  
Chemokine Receptor ◽  
Immune Surveillance ◽  
Receptor Activation ◽  
Structural Basis ◽  
Tryptophan Residue ◽  
Toggle Switch ◽  
Binding Modes ◽  
Extracellular Region ◽  
Chemokine Receptor Ccr5 ◽  
Receptor Ccr5

AbstractThe chemokine receptor CCR5 plays a vital role in immune surveillance and inflammation. However, molecular details that govern its endogenous chemokine recognition and receptor activation remain elusive. Here we report three cryo-electron microscopy structures of Gi1 protein-coupled CCR5 in a ligand-free state and in complex with the chemokine MIP-1α or RANTES, as well as the crystal structure of MIP-1α-bound CCR5. These structures reveal distinct binding modes of the two chemokines and a specific accommodate pattern of the chemokine for the distal N terminus of CCR5. Together with functional data, the structures demonstrate that chemokine-induced rearrangement of toggle switch and plasticity of the receptor extracellular region are critical for receptor activation, while a conserved tryptophan residue in helix II acts as a trigger of receptor constitutive activation.

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Approach in improving potency and selectivity of kinase inhibitors: Allosteric kinase inhibitors

2020 ◽  
Vol 21 ◽  
Author(s):  
Shangfei Wei ◽  
Tianming Zhao ◽  
Jie Wang ◽  
Xin Zhai
Keyword(s):  
Protein Interactions ◽  
Kinase Inhibitors ◽  
Binding Modes ◽  
Allosteric Inhibitors ◽  
Wide Range ◽  
Allosteric Sites ◽  
Protein Functions ◽  
Regulate Protein

: Allostery is an efficient and particular regulatory mechanism to regulate protein functions. Different from conserved orthosteric sites, allosteric sites have distinctive functional mechanism to form the complex regulatory network. In drug discovery, kinase inhibitors targeting the allosteric pockets have received extensive attention for the advantages of high selectivity and low toxicity. The approval of trametinib as the first allosteric inhibitor validated that allosteric inhibitors could be used as effective therapeutic drugs for treatment of diseases. To date, a wide range of allosteric inhibitors have been identified. In this perspective, we outline different binding modes and potential advantages of allosteric inhibitors. In the meantime, the research processes of typical and novel allosteric inhibitors are described briefly in terms of structureactivity relationships, ligand-protein interactions and in vitro and in vivo activity. Additionally, challenges as well as opportunities are presented.

scholarly journals Computational Perspectives into Plasmepsins Structure—Function Relationship: Implications to Inhibitors Design

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Alejandro Gil L. ◽  
Pedro A. Valiente ◽  
Pedro G. Pascutti ◽  
Tirso Pons
Keyword(s):  
Structure Function ◽  
Free Energy Calculations ◽  
Special Focus ◽  
Binding Modes ◽  
Linear Interaction ◽  
Lead Compounds ◽  
Structure Function Relationship ◽  
Function Relationship ◽  
Dynamics Simulations ◽  
Selection Of

The development of efficient and selective antimalariais remains a challenge for the pharmaceutical industry. The aspartic proteases plasmepsins, whose inhibition leads to parasite death, are classified as targets for the design of potent drugs. Combinatorial synthesis is currently being used to generate inhibitor libraries for these enzymes, and together with computational methodologies have been demonstrated capable for the selection of lead compounds. The high structural flexibility of plasmepsins, revealed by their X-ray structures and molecular dynamics simulations, made even more complicated the prediction of putative binding modes, and therefore, the use of common computational tools, like docking and free-energy calculations. In this review, we revised the computational strategies utilized so far, for the structure-function relationship studies concerning the plasmepsin family, with special focus on the recent advances in the improvement of the linear interaction estimation (LIE) method, which is one of the most successful methodologies in the evaluation of plasmepsin-inhibitor binding affinity.

scholarly journals Sequence analysis of heparan sulphate and heparin oligosaccharides

1999 ◽  
Vol 339 (3) ◽  
pp. 767-773 ◽  
Author(s):  
Romain R. VIVÈS ◽  
David A. PYE ◽  
Markku SALMIVIRTA ◽  
John J. HOPWOOD ◽  
Ulf LINDAHL ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Protein Interactions ◽  
Sequence Data ◽  
Specific Binding ◽  
Heparan Sulphate ◽  
Biologically Active ◽  
Simple Method ◽  
Gag Protein ◽  
Specific Binding Sites ◽  
Strong Anion Exchange

The biological activity of heparan sulphate (HS) and heparin largely depends on internal oligosaccharide sequences that provide specific binding sites for an extensive range of proteins. Identification of such structures is crucial for the complete understanding of glycosaminoglycan (GAG)-protein interactions. We describe here a simple method of sequence analysis relying on the specific tagging of the sugar reducing end by 3H radiolabelling, the combination of chemical scission and specific enzymic digestion to generate intermediate fragments, and the analysis of the generated products by strong-anion-exchange HPLC. We present full sequence data on microgram quantities of four unknown oligosaccharides (three HS-derived hexasaccharides and one heparin-derived octasaccharide) which illustrate the utility and relative simplicity of the technique. The results clearly show that it is also possible to read sequences of inhomogeneous preparations. Application of this technique to biologically active oligosaccharides should accelerate progress in the understanding of HS and heparin structure-function relationships and provide new insights into the primary structure of these polysaccharides.

scholarly journals ATP and Tri-Polyphosphate (TPP) Suppress Protein Aggregate Growth by a Supercharging Mechanism

Biomedicines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1646
Author(s):  
Jordan Bye ◽  
Kiah Murray ◽  
Robin Curtis
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Colloidal Stability ◽  
Conformational Stability ◽  
Kinetic Studies ◽  
Structural Factors ◽  
Protein Protein Interactions ◽  
Protein Aggregate ◽  
Aggregate Growth

A common strategy to increase aggregation resistance is through rational mutagenesis to supercharge proteins, which leads to high colloidal stability, but often has the undesirable effect of lowering conformational stability. We show this trade-off can be overcome by using small multivalent polyphosphate ions, adenosine triphosphate (ATP) and tripolyphosphate (TPP) as excipients. These ions are equally effective at suppressing aggregation of ovalbumin and bovine serum albumin (BSA) upon thermal stress as monitored by dynamic and static light scattering. Monomer loss kinetic studies, combined with measurements of native state protein–protein interactions and ζ-potentials, indicate the ions reduce aggregate growth by increasing the protein colloidal stability through binding and overcharging the protein. Out of three additional proteins studied, ribonuclease A (RNaseA), α-chymotrypsinogen (α-Cgn), and lysozyme, we only observed a reduction in aggregate growth for RNaseA, although overcharging by the poly-phosphate ions still occurs for lysozyme and α-Cgn. Because the salts do not alter protein conformational stability, using them as excipients could be a promising strategy for stabilizing biopharmaceuticals once the protein structural factors that determine whether multivalent ion binding will increase colloidal stability are better elucidated. Our findings also have biological implications. Recently, it has been proposed that ATP also plays an important role in maintaining intracellular biological condensates and preventing protein aggregation in densely packed cellular environments. We expect electrostatic interactions are a significant factor in determining the stabilizing ability of ATP towards maintaining proteins in non-dispersed states in vivo.

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