Analyzing Distinct Binding Modes of Diaminotriazole-Based Spastin Inhibitors Through Biochemical Resistance

2019 ◽  
Author(s):  
Rudolf Pisa ◽  
Tommaso Cupido ◽  
Jonathan B. Steinman ◽  
Natalie H. Jones ◽  
Tarun M. Kapoor
Keyword(s):  
Binding Modes ◽  
Biochemical Resistance

On the Adsorption of Aspartate Derivatives to Calcite Surfaces in Aqueous Environment

2020 ◽  
Author(s):  
Robert Stepic ◽  
Lara Jurković ◽  
Ksenia Klementyeva ◽  
Marko Ukrainczyk ◽  
Matija Gredičak ◽  
...  
Keyword(s):  
Active Sites ◽  
Realistic Model ◽  
Binding Energies ◽  
Inhibition Mechanism ◽  
Aqueous Environment ◽  
Binding Modes ◽  
Carboxylate Groups ◽  
Dissolved Ions ◽  
Living Organisms ◽  
Dynamics Simulations

In many living organisms, biomolecules interact favorably with various surfaces of calcium carbonate. In this work, we have considered the interactions of aspartate (Asp) derivatives, as models of complex biomolecules, with calcite. Using kinetic growth experiments, we have investigated the inhibition of calcite growth by Asp, Asp2 and Asp3.This entailed the determination of a step-pinning growth regime as well as the evaluation of the adsorption constants and binding free energies for the three species to calcite crystals. These latter values are compared to free energy profiles obtained from fully atomistic molecular dynamics simulations. When using a flat (104) calcite surface in the models, the measured trend of binding energies is poorly reproduced. However, a more realistic model comprised of a surface with an island containing edges and corners, yields binding energies that compare very well with experiments. Surprisingly, we find that most binding modes involve the positively charged, ammonium group. Moreover, while attachment of the negatively charged carboxylate groups is also frequently observed, it is always balanced by the aqueous solvation of an equal or greater number of carboxylates. These effects are observed on all calcite features including edges and corners, the latter being associated with dominant affinities to Asp derivatives. As these features are also precisely the active sites for crystal growth, the experimental and theoretical results point strongly to a growth inhibition mechanism whereby these sites become blocked, preventing further attachment of dissolved ions and halting further growth.

Reversibly Sampling Conformations and Binding Modes Using Molecular Darting

2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Sites ◽  
Degrees Of Freedom ◽  
Dynamics Simulation ◽  
Hiv Integrase ◽  
Binding Modes ◽  
System A ◽  
Multiple Binding ◽  
Internal Degrees Of Freedom

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>

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>

Biological and In silico Studies on Synthetic Analogues of Tyrosine Betaine as Inhibitors of Neprilysin - A Drug Target for the Treatment of Heart Failure

2018 ◽  
Vol 24 (17) ◽  
pp. 1899-1904
Author(s):  
Daniel Fabio Kawano ◽  
Marcelo Rodrigues de Carvalho ◽  
Mauricio Ferreira Marcondes Machado ◽  
Adriana Karaoglanovic Carmona ◽  
Gilberto Ubida Leite Braga ◽  
...  
Keyword(s):  
Heart Failure ◽  
Secondary Metabolites ◽  
Structural Similarity ◽  
Structural Features ◽  
Major Constituent ◽  
Binding Modes ◽  
Starting Point ◽  
In Silico Studies ◽  
Nep Inhibition

Background: Fungal secondary metabolites are important sources for the discovery of new pharmaceuticals, as exemplified by penicillin, lovastatin and cyclosporine. Searching for secondary metabolites of the fungi Metarhizium spp., we previously identified tyrosine betaine as a major constituent. Methods: Because of the structural similarity with other inhibitors of neprilysin (NEP), an enzyme explored for the treatment of heart failure, we devised the synthesis of tyrosine betaine and three analogues to be subjected to in vitro NEP inhibition assays and to molecular modeling studies. Results: In spite of the similar binding modes with other NEP inhibitors, these compounds only displayed moderate inhibitory activities (IC50 ranging from 170.0 to 52.9 µM). However, they enclose structural features required to hinder passive blood brain barrier permeation (BBB). Conclusions: Tyrosine betaine remains as a starting point for the development of NEP inhibitors because of the low probability of BBB permeation and, consequently, of NEP inhibition at the Central Nervous System, which is associated to an increment in the Aβ levels and, accordingly, with a higher risk for the onset of Alzheimer's disease.

Recent Advances of In-Silico Modeling of Potent Antagonists for the Adenosine Receptors

2019 ◽  
Vol 25 (7) ◽  
pp. 750-773 ◽  
Author(s):  
Pabitra Narayan Samanta ◽  
Supratik Kar ◽  
Jerzy Leszczynski
Keyword(s):  
Drug Targets ◽  
Biological Activities ◽  
Scoring Function ◽  
Md Simulations ◽  
Energy Calculation ◽  
Binding Modes ◽  
Macromolecular Systems ◽  
In Silico Modeling ◽  
Mathematical Algorithms ◽  
The Impact

The rapid advancement of computer architectures and development of mathematical algorithms offer a unique opportunity to leverage the simulation of macromolecular systems at physiologically relevant timescales. Herein, we discuss the impact of diverse structure-based and ligand-based molecular modeling techniques in designing potent and selective antagonists against each adenosine receptor (AR) subtype that constitutes multitude of drug targets. The efficiency and robustness of high-throughput empirical scoring function-based approaches for hit discovery and lead optimization in the AR family are assessed with the help of illustrative examples that have led to nanomolar to sub-micromolar inhibition activities. Recent progress in computer-aided drug discovery through homology modeling, quantitative structure-activity relation, pharmacophore models, and molecular docking coupled with more accurate free energy calculation methods are reported and critically analyzed within the framework of structure-based virtual screening of AR antagonists. Later, the potency and applicability of integrated molecular dynamics (MD) methods are addressed in the context of diligent inspection of intricated AR-antagonist binding processes. MD simulations are exposed to be competent for studying the role of the membrane as well as the receptor flexibility toward the precise evaluation of the biological activities of antagonistbound AR complexes such as ligand binding modes, inhibition affinity, and associated thermodynamic and kinetic parameters.

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