scholarly journals Multiscale simulations examining glycan shield effects on drug binding to influenza neuraminidase

2020 ◽  
Author(s):  
Christian Seitz ◽  
Lorenzo Casalino ◽  
Robert Konecny ◽  
Gary Huber ◽  
Rommie E. Amaro ◽  
...  
Keyword(s):  
Drug Development ◽  
Ligand Binding ◽  
Binding Site ◽  
Brownian Dynamics ◽  
Drug Binding ◽  
Binding Mechanism ◽  
Drug Effectiveness ◽  
Future Drug ◽  
Dynamics Simulations ◽  
Inhibit Antibody

AbstractInfluenza neuraminidase is an important drug target. Glycans are present on neuraminidase, and are generally considered to inhibit antibody binding via their glycan shield. In this work we studied the effect of glycans on the binding kinetics of antiviral drugs to the influenza neuraminidase. We created all-atom in silico systems of influenza neuraminidase with experimentally-derived glycoprofiles consisting of four systems with different glycan conformations and one system without glycans. Using Brownian dynamics simulations, we observe a two- to eight-fold decrease in the rate of ligand binding to the primary binding site of neuraminidase due to the presence of glycans. These glycans are capable of covering much of the surface area of neuraminidase, and the ligand binding inhibition is derived from glycans sterically occluding the primary binding site on a neighboring monomer. Our work also indicates that drugs preferentially bind to the primary binding site (i.e. the active site) over the secondary binding site, and we propose a binding mechanism illustrating this. These results help illuminate the complex interplay between glycans and ligand binding on the influenza membrane protein neuraminidase.Statement of SignificanceThe influenza glycoprotein neuraminidase is the target for three FDA-approved influenza drugs in the US. However, drug resistance and low drug effectiveness merits further drug development towards neuraminidase, which is hindered by our limited understanding of glycan effects on ligand binding. Generally, drug developers do not include glycans in their development pipelines. Here, we show that even though glycans can reduce drug binding towards neuraminidase, we recommend future drug development work to focus on strong binders with a long lifetime. Furthermore, we examine the binding competition between the primary and secondary binding sites on neuraminidase, leading us to propose a new, to the best of our knowledge, multivalent binding mechanism.

scholarly journals The Interplay of Cholesterol and Ligand Binding in hTSPO from Classical Molecular Dynamics Simulations

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1250
Author(s):  
Hien T. T. Lai ◽  
Alejandro Giorgetti ◽  
Giulia Rossetti ◽  
Toan T. Nguyen ◽  
Paolo Carloni ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Molecular Dynamics Simulations ◽  
Binding Site ◽  
Transmembrane Protein ◽  
Free Form ◽  
Experimental Investigations ◽  
Terminal Part ◽  
Dynamics Simulations ◽  
Cholesterol Binding

The translocator protein (TSPO) is a 18kDa transmembrane protein, ubiquitously present in human mitochondria. It is overexpressed in tumor cells and at the sites of neuroinflammation, thus representing an important biomarker, as well as a promising drug target. In mammalian TSPO, there are cholesterol–binding motifs, as well as a binding cavity able to accommodate different chemical compounds. Given the lack of structural information for the human protein, we built a model of human (h) TSPO in the apo state and in complex with PK11195, a molecule routinely used in positron emission tomography (PET) for imaging of neuroinflammatory sites. To better understand the interactions of PK11195 and cholesterol with this pharmacologically relevant protein, we ran molecular dynamics simulations of the apo and holo proteins embedded in a model membrane. We found that: (i) PK11195 stabilizes hTSPO structural fold; (ii) PK11195 might enter in the binding site through transmembrane helices I and II of hTSPO; (iii) PK11195 reduces the frequency of cholesterol binding to the lower, N–terminal part of hTSPO in the inner membrane leaflet, while this impact is less pronounced for the upper, C–terminal part in the outer membrane leaflet, where the ligand binding site is located; (iv) very interestingly, cholesterol most frequently binds simultaneously to the so-called CRAC and CARC regions in TM V in the free form (residues L150–X–Y152–X(3)–R156 and R135–X(2)–Y138–X(2)–L141, respectively). However, when the protein is in complex with PK11195, cholesterol binds equally frequently to the CRAC–resembling motif that we observed in TM I (residues L17–X(2)–F20–X(3)–R24) and to CRAC in TM V. We expect that the CRAC–like motif in TM I will be of interest in future experimental investigations. Thus, our MD simulations provide insight into the structural features of hTSPO and the previously unknown interplay between PK11195 and cholesterol interactions with this pharmacologically relevant protein.

scholarly journals A structural basis for how ligand binding site changes can allosterically regulate GPCR signaling and engender functional selectivity

2020 ◽  
Vol 13 (617) ◽  
pp. eaaw5885 ◽  
Author(s):  
Marta Sanchez-Soto ◽  
Ravi Kumar Verma ◽  
Blair K. A. Willette ◽  
Elizabeth C. Gonye ◽  
Annah M. Moore ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
G Protein ◽  
Structural Basis ◽  
Protein Signaling ◽  
Ligand Binding Site ◽  
Intracellular Loop ◽  
Gpcr Signaling ◽  
Biased Signaling ◽  
Dynamics Simulations

Signaling bias is the propensity for some agonists to preferentially stimulate G protein–coupled receptor (GPCR) signaling through one intracellular pathway versus another. We previously identified a G protein–biased agonist of the D2 dopamine receptor (D2R) that results in impaired β-arrestin recruitment. This signaling bias was predicted to arise from unique interactions of the ligand with a hydrophobic pocket at the interface of the second extracellular loop and fifth transmembrane segment of the D2R. Here, we showed that residue Phe189 within this pocket (position 5.38 using Ballesteros-Weinstein numbering) functions as a microswitch for regulating receptor interactions with β-arrestin. This residue is relatively conserved among class A GPCRs, and analogous mutations within other GPCRs similarly impaired β-arrestin recruitment while maintaining G protein signaling. To investigate the mechanism of this signaling bias, we used an active-state structure of the β2-adrenergic receptor (β2R) to build β2R-WT and β2R-Y1995.38A models in complex with the full β2R agonist BI-167107 for molecular dynamics simulations. These analyses identified conformational rearrangements in β2R-Y1995.38A that propagated from the extracellular ligand binding site to the intracellular surface, resulting in a modified orientation of the second intracellular loop in β2R-Y1995.38A, which is predicted to affect its interactions with β-arrestin. Our findings provide a structural basis for how ligand binding site alterations can allosterically affect GPCR-transducer interactions and result in biased signaling.

scholarly journals Simulating Protein-Ligand Binding with Neural Network Potentials

2019 ◽  
Author(s):  
Shae-Lynn Lahey ◽  
Christopher Rowley
Keyword(s):  
Neural Network ◽  
Ligand Binding ◽  
Computational Cost ◽  
Bound State ◽  
Drug Binding ◽  
Cancer Drug ◽  
Molecular Conformations ◽  
Drug Molecules ◽  
The Stability ◽  
Dynamics Simulations

Drug molecules adopt a range of conformations both in solution and in their protein-bound state. The strain and reduced flexibility of bound drugs can partially counter the intermolecular interactions that drive protein–ligand binding. To make accurate computational predictions of drug binding affinities, computational chemists have attempted to develop efficient empirical models of these interactions, although these methods are not always reliable. Machine learning has allowed the development of highly-accurate neural-network potentials (NNPs), which are capable of predicting the stability of molecular conformations with accuracy comparable to state-of-the-art quantum chemical calculations but at a billionth of the computational cost. Here, we demonstrate that these methods can be used to represent the intramolecular forces of protein-bound drugs within molecular dynamics simulations. These simulations are shown to be capable of predicting the protein–ligand binding pose and conformational component of the absolute Gibbs energy of binding for a set of drug molecules. Notably, the conformational energy for anti-cancer drug erlotinib binding to its target was found to considerably overestimated by a molecular mechanical model, while the NNP predicts a more moderate value. Although the ANI-1ccX NNP was not trained to describe ionic molecules, reasonable binding poses are predicted for charged ligands, although this method is not suitable for modeling the ligands in solution.

scholarly journals Simulating Protein-Ligand Binding with Neural Network Potentials

2019 ◽  
Author(s):  
Shae-Lynn Lahey ◽  
Christopher Rowley
Keyword(s):  
Neural Network ◽  
Ligand Binding ◽  
Computational Cost ◽  
Bound State ◽  
Drug Binding ◽  
Cancer Drug ◽  
Molecular Conformations ◽  
Drug Molecules ◽  
The Stability ◽  
Dynamics Simulations

Drug molecules adopt a range of conformations both in solution and in their protein-bound state. The strain and reduced flexibility of bound drugs can partially counter the intermolecular interactions that drive protein–ligand binding. To make accurate computational predictions of drug binding affinities, computational chemists have attempted to develop efficient empirical models of these interactions, although these methods are not always reliable. Machine learning has allowed the development of highly-accurate neural-network potentials (NNPs), which are capable of predicting the stability of molecular conformations with accuracy comparable to state-of-the-art quantum chemical calculations but at a billionth of the computational cost. Here, we demonstrate that these methods can be used to represent the intramolecular forces of protein-bound drugs within molecular dynamics simulations. These simulations are shown to be capable of predicting the protein–ligand binding pose and conformational component of the absolute Gibbs energy of binding for a set of drug molecules. Notably, the conformational energy for anti-cancer drug erlotinib binding to its target was found to considerably overestimated by a molecular mechanical model, while the NNP predicts a more moderate value. Although the ANI-1ccX NNP was not trained to describe ionic molecules, reasonable binding poses are predicted for charged ligands, although this method is not suitable for modeling the ligands in solution.

scholarly journals How does Glycosylation Affect Drug Binding on Influenza? The Roles of Electrostatics and Sterics Examined through Brownian Dynamics Simulations

2019 ◽  
Vol 116 (3) ◽  
pp. 483a
Author(s):  
Christian Seitz ◽  
Lorenzo Casalino ◽  
Gary Huber ◽  
Robert Konecny ◽  
Yu-Ming Huang ◽  
...  
Keyword(s):  
Brownian Dynamics ◽  
Drug Binding ◽  
Dynamics Simulations ◽  
Brownian Dynamics Simulations

Protein-Ligand Binding Enthalpies from Near-Millisecond Simulations: Analysis of a Preorganization Paradox

2018 ◽  
Author(s):  
Amanda Li ◽  
Michael Gilson
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Configurational Entropy ◽  
Sh2 Domain ◽  
Relative Binding ◽  
Large Numbers ◽  
Explicit Solvent ◽  
Calorimetric Studies ◽  
Dynamics Simulations ◽  
Further Development

Calorimetric studies of protein-ligand binding often yield thermodynamic data that are difficult to explain in physical terms. Today, explicit solvent molecular dynamics simulations are fast enough that we can begin to use them to look for explanations of such thermodynamic puzzles. Additionally, when such simulations generate results that do not agree well with experiment, this may motivate further development of computational methods and force fields. Here, we apply near-millisecond duration simulations to compute and analyze the binding of four peptidic ligands with the Grb2 SH2 domain, focusing on relative binding enthalpies. The ligands fall into matched pairs, which differ only in the presence or absence of a conformationally constraining bond, which preorganizes two of the ligands for binding. Prior experimental work had revealed, unexpectedly, that binding of the constrained ligands is favored enthalpically, rather than entropically, relative to their flexible<br>analogs. However, the present calculations yield the opposite trend. On further analysis, the computed relative binding enthalpies are found to be small balances of much larger underlying differences in the mean energies of structural components, such as the ligand and the binding site residues. As a consequence, the deviations from experiment in the relative binding enthalpies represent small differences between these large numbers. We also computed first order estimates of changes in configurational entropy on binding. These suggest that the more rigid constrained ligands reduce the entropy of binding site residues more than their flexible analogs do. The implications of these calculations for the use of simulations to understand the thermodynamics of molecular recognition, and for the computational analysis of binding thermodynamics, are discussed.

The Interaction of the Microtubule Targeting Anticancer Drug Colchicine with Human Glutathione Transferases

2020 ◽  
Vol 26 (40) ◽  
pp. 5205-5212
Author(s):  
Georgios Premetis ◽  
Panagiotis Marugas ◽  
Georgios Fanos ◽  
Dimitrios Vlachakis ◽  
Evangelia G. Chronopoulou ◽  
...  
Keyword(s):  
Binding Site ◽  
Anticancer Drug ◽  
Molecular Modelling ◽  
Chemotherapeutic Agents ◽  
Detoxification Enzymes ◽  
Glutathione Transferases ◽  
Multi Drug Resistance ◽  
Future Drug ◽  
Dynamics Simulations ◽  
Gst Family

Background: Glutathione transferases (GSTs) are a family of Phase II detoxification enzymes that have been shown to be involved in the development of multi-drug resistance (MDR) mechanism toward chemotherapeutic agents. GST inhibitors have, therefore, emerged as promising chemosensitizers to manage and reverse MDR. Colchicine (COL) is a classical antimitotic, tubulin-binding agent (TBA) which is being explored as anticancer drug. Methods: In the present work, the interaction of COL and its derivative 2,3-didemethylcolchicine (2,3-DDCOL) with human glutathione transferases (hGSTA1-1, hGSTP1-1, hGSTM1-1) was investigated by inhibition analysis, molecular modelling and molecular dynamics simulations. Results: The results showed that both compounds bind reversibly to human GSTs and behave as potent inhibitors. hGSTA1-1 was the most sensitive enzyme to inhibition by COL with IC50 22 μΜ. Molecular modelling predicted that COL overlaps with both the hydrophobic (H-site) and glutathione binding site (G-site) and polar interactions appear to be the driving force for its positioning and recognition at the binding site. The interaction of COL with other members of GST family (hGSTA2-2, hGSTM3-3, hGSTM3-2) was also investigated with similar results. Conclusion: The results of the present study might be useful in future drug design and development efforts towards human GSTs.

scholarly journals Allosteric binding site in a Cys-loop receptor ligand-binding domain unveiled in the crystal structure of ELIC in complex with chlorpromazine

2016 ◽  
Vol 113 (43) ◽  
pp. E6696-E6703 ◽  
Author(s):  
Mieke Nys ◽  
Eveline Wijckmans ◽  
Ana Farinha ◽  
Özge Yoluk ◽  
Magnus Andersson ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Ion Channel ◽  
Binding Site ◽  
Binding Domain ◽  
Ligand Binding Domain ◽  
Channel Pore ◽  
X Ray ◽  
Electrophysiological Recordings ◽  
Channel Opening ◽  
Dynamics Simulations

Pentameric ligand-gated ion channels or Cys-loop receptors are responsible for fast inhibitory or excitatory synaptic transmission. The antipsychotic compound chlorpromazine is a widely used tool to probe the ion channel pore of the nicotinic acetylcholine receptor, which is a prototypical Cys-loop receptor. In this study, we determine the molecular determinants of chlorpromazine binding in the Erwinia ligand-gated ion channel (ELIC). We report the X-ray crystal structures of ELIC in complex with chlorpromazine or its brominated derivative bromopromazine. Unexpectedly, we do not find a chlorpromazine molecule in the channel pore of ELIC, but behind the β8–β9 loop in the extracellular ligand-binding domain. The β8–β9 loop is localized downstream from the neurotransmitter binding site and plays an important role in coupling of ligand binding to channel opening. In combination with electrophysiological recordings from ELIC cysteine mutants and a thiol-reactive derivative of chlorpromazine, we demonstrate that chlorpromazine binding at the β8–β9 loop is responsible for receptor inhibition. We further use molecular-dynamics simulations to support the X-ray data and mutagenesis experiments. Together, these data unveil an allosteric binding site in the extracellular ligand-binding domain of ELIC. Our results extend on previous observations and further substantiate our understanding of a multisite model for allosteric modulation of Cys-loop receptors.

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