scholarly journals A Molecular Mechanics Approach to Modeling Protein–Ligand Interactions: Relative Binding Affinities in Congeneric Series

2011 ◽  
Vol 51 (9) ◽  
pp. 2082-2089 ◽  
Author(s):  
Chaya Rapp ◽  
Chakrapani Kalyanaraman ◽  
Aviva Schiffmiller ◽  
Esther Leah Schoenbrun ◽  
Matthew P. Jacobson
2019 ◽  
Vol 26 (26) ◽  
pp. 4964-4983 ◽  
Author(s):  
CongBao Kang

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.


2021 ◽  
Author(s):  
Yunhui Ge ◽  
Vincent Voelz

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories, but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ε parameter was tuned, and the system placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 μs of unbiased simulation, and an 18-state Markov state model used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional MSMs, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal, and that in most cases only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.


1982 ◽  
Vol 104 (23) ◽  
pp. 6424-6434 ◽  
Author(s):  
Jeffrey M. Blaney ◽  
Paul K. Weiner ◽  
Andrew Dearing ◽  
Peter A. Kollman ◽  
Eugene C. Jorgensen ◽  
...  

2019 ◽  
Vol 14 (5) ◽  
pp. 1934578X1984929 ◽  
Author(s):  
Sanhita Maity ◽  
Ravi Kumar Gundampati ◽  
Thallapuranam Krishnaswamy Suresh Kumar

Structural information pertaining to the interactions between biological macromolecules and ligands is of potential significance for understanding of molecular mechanisms in key biological processes. Recently, nuclear magnetic resonance (NMR) spectroscopic techniques has come of age and has widened its scope to characterize binding interactions of small molecules with biological macromolecules especially, proteins. NMR spectroscopy-based techniques are versatile due to their ability to examine weak binding interactions and for rapid screening the binding affinities of ligands with proteins at atomic resolution. In this review, we provide a broad overview of some of the important NMR approaches to investigate interactions of small organic molecules with proteins.


2020 ◽  
Author(s):  
Nitai Sylvetsky

Contemporary efforts for modeling protein-ligand interactions entail a painful tradeoff – as reliable information on both noncovalent binding factors and the dynamic behavior of a protein-ligand complex is often beyond practical limits. In the following paper, we demonstrate that information drawn exclusively from static molecular structures can be used for the semi-quantitative prediction of experimentally-measured binding affinities for protein-ligand complexes. In the particular case considered here, inhibition constants (Ki) were calculated for eight different ligands of torpedo californica acetylcholinesterase using a simple, multiple-linearregression-based model. The latter, incorporating five informative and independent variables – drawn from QM cluster, DLPNO-CCSD(T) calculations and LED analyses on the eight complexes – is shown to recover no-lessthan 96% of the sum of squares for measured Ki values, and used to predict the inhibition potential for yet another ligand (E20, for which no Ki values are available in the literature). This thus challenges the widespread assumption that “static pictures” are inadequate for predicting reactivity properties of flexible and dynamic protein-ligand systems.


2014 ◽  
Vol 169 ◽  
pp. 477-499 ◽  
Author(s):  
Christopher J. Woods ◽  
Maturos Malaisree ◽  
Julien Michel ◽  
Ben Long ◽  
Simon McIntosh-Smith ◽  
...  

Recent advances in computational hardware, software and algorithms enable simulations of protein–ligand complexes to achieve timescales during which complete ligand binding and unbinding pathways can be observed. While observation of such events can promote understanding of binding and unbinding pathways, it does not alone provide information about the molecular drivers for protein–ligand association, nor guidance on how a ligand could be optimised to better bind to the protein. We have developed the waterswap (C. J. Woods et al., J. Chem. Phys., 2011, 134, 054114) absolute binding free energy method that calculates binding affinities by exchanging the ligand with an equivalent volume of water. A significant advantage of this method is that the binding free energy is calculated using a single reaction coordinate from a single simulation. This has enabled the development of new visualisations of binding affinities based on free energy decompositions to per-residue and per-water molecule components. These provide a clear picture of which protein–ligand interactions are strong, and which active site water molecules are stabilised or destabilised upon binding. Optimisation of the algorithms underlying the decomposition enables near-real-time visualisation, allowing these calculations to be used either to provide interactive feedback to a ligand designer, or to provide run-time analysis of protein–ligand molecular dynamics simulations.


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