scholarly journals Potent Inhibition of Mandelate Racemase by a Fluorinated Substrate-Product Analogue with a Novel Binding Mode

Biochemistry ◽  
2014 ◽  
Vol 53 (7) ◽  
pp. 1169-1178 ◽  
Author(s):  
Mitesh Nagar ◽  
Adam D. Lietzan ◽  
Martin St. Maurice ◽  
Stephen L. Bearne
Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 5962 ◽  
Author(s):  
Anne Wurzlbauer ◽  
Katharina Rüben ◽  
Ece Gürdal ◽  
Apirat Chaikuad ◽  
Stefan Knapp ◽  
...  

The β-carboline alkaloid harmine is a potent DYRK1A inhibitor, but suffers from undesired potent inhibition of MAO-A, which strongly limits its application. We synthesized more than 60 analogues of harmine, either by direct modification of the alkaloid or by de novo synthesis of β-carboline and related scaffolds aimed at learning about structure–activity relationships for inhibition of both DYRK1A and MAO-A, with the ultimate goal of separating desired DYRK1A inhibition from undesired MAO-A inhibition. Based on evidence from published crystal structures of harmine bound to each of these enzymes, we performed systematic structure modifications of harmine yielding DYRK1A-selective inhibitors characterized by small polar substituents at N-9 (which preserve DYRK1A inhibition and eliminate MAO-A inhibition) and beneficial residues at C-1 (methyl or chlorine). The top compound AnnH75 remains a potent DYRK1A inhibitor, and it is devoid of MAO-A inhibition. Its binding mode to DYRK1A was elucidated by crystal structure analysis, and docking experiments provided additional insights for this attractive series of DYRK1A and MAO-A inhibitors.


2017 ◽  
Author(s):  
David Maussang-Detaille ◽  
Camilla de Nardis ◽  
Linda Hendriks ◽  
Carina Bartelink-Clements ◽  
Eric Rovers ◽  
...  

Biochemistry ◽  
2021 ◽  
Author(s):  
Colin D. Douglas ◽  
Lia Grandinetti ◽  
Nicole M. Easton ◽  
Oliver P. Kuehm ◽  
Joshua A. Hayden ◽  
...  

Biochemistry ◽  
2020 ◽  
Vol 59 (33) ◽  
pp. 3026-3037
Author(s):  
Amar Nath Sharma ◽  
Lia Grandinetti ◽  
Erin R. Johnson ◽  
Martin St. Maurice ◽  
Stephen L. Bearne

2019 ◽  
Vol 116 (14) ◽  
pp. 6760-6765 ◽  
Author(s):  
Claire Debarnot ◽  
Yoan R. Monneau ◽  
Véronique Roig-Zamboni ◽  
Vincent Delauzun ◽  
Christine Le Narvor ◽  
...  

Heparan sulfate (HS) is a linear, complex polysaccharide that modulates the biological activities of proteins through binding sites made by a series of Golgi-localized enzymes. Of these, glucuronyl C5-epimerase (Glce) catalyzes C5-epimerization of the HS component,d-glucuronic acid (GlcA), intol-iduronic acid (IdoA), which provides internal flexibility to the polymer and forges protein-binding sites to ensure polymer function. Here we report crystal structures of human Glce in the unbound state and of an inactive mutant, as assessed by real-time NMR spectroscopy, bound with a (GlcA-GlcNS)nsubstrate or a (IdoA-GlcNS)nproduct. Deep infiltration of the oligosaccharides into the active site cleft imposes a sharp kink within the central GlcNS-GlcA/IdoA-GlcNS trisaccharide motif. An extensive network of specific interactions illustrates the absolute requirement ofN-sulfate groups vicinal to the epimerization site for substrate binding. At the epimerization site, the GlcA/IdoA rings are highly constrained in two closely related boat conformations, highlighting ring-puckering signatures during catalysis. The structure-based mechanism involves the two invariant acid/base residues, Glu499 and Tyr578, poised on each side of the target uronic acid residue, thus allowing reversible abstraction and readdition of a proton at the C5 position through a neutral enol intermediate, reminiscent of mandelate racemase. These structures also shed light on a convergent mechanism of action between HS epimerases and lyases and provide molecular frameworks for the chemoenzymatic synthesis of heparin or HS analogs.


2015 ◽  
Vol 53 (01) ◽  
Author(s):  
L Spomer ◽  
CGW Gertzen ◽  
D Häussinger ◽  
H Gohlke ◽  
V Keitel

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  

<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>


2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  

<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>


2019 ◽  
Author(s):  
Sukanya Sasmal ◽  
Léa El Khoury ◽  
David Mobley

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4, which focused on predicting the binding poses and affinity ranking for compounds targeting the beta-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 A RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and Dock 6 and found that HYBRID performed better here for pose prediction. We also conducted end-point free energy estimates on protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R Grand Challenge 4 suggest that: i) the generation of the macrocycles conformers is a key step for successful pose prediction, ii) the protonation states of the BACE-1 binding site should be treated carefully, iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and iv) the MM-GBSA method does not perform well at predicting protein-ligand binding affinities here.


2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>


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