A quantum mechanical approach to ligand binding — Calculation of ligand–protein binding affinities for stromelysin-1 (MMP-3) inhibitors

2009 ◽  
Vol 87 (10) ◽  
pp. 1480-1484 ◽  
Author(s):  
Jian Li ◽  
Charles. H. Reynolds
Keyword(s):  
Ligand Binding ◽  
Metal Ion ◽  
Protein Complexes ◽  
Field Method ◽  
Binding Energies ◽  
Quantum Mechanical ◽  
Binding Affinities ◽  
Bond Forming ◽  
Proton Charge ◽  
Quantum Mechanical Approach

Linear-scaling quantum mechanical method was applied to calculate binding affinities of six stromelysin-1 (MMP-3) inhibitors with two different zinc binding groups (ZBGs). The entire protein and ligand–protein complexes were calculated using PM5 Hamiltonian, which enables the treatment of metal ion coordination, bond forming/breaking, and proton/charge transfers associated with the ligand binding process by the self-consistent field method. The calculated binding energies reproduce the binding-affinity trend observed experimentally.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zbigniew Dutkiewicz
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Development Project ◽  
Quantum Mechanical ◽  
Prediction Methods ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Quantum Mechanical Description ◽  
Optimization Stage

AbstractDrug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

Estimates of ligand-binding affinities supported by quantum mechanical methods

2010 ◽  
Vol 2 (1) ◽  
pp. 21-37 ◽  
Author(s):  
Pär Söderhjelm ◽  
Jacob Kongsted ◽  
Samuel Genheden ◽  
Ulf Ryde
Keyword(s):  
Ligand Binding ◽  
Quantum Mechanical ◽  
Binding Affinities ◽  
Mechanical Methods

Formation and Binding Energies of Vacancy Clusters in Silicon

1997 ◽  
Vol 469 ◽  
Author(s):  
L. Colombo ◽  
A. Bongiorno ◽  
T. Diaz De La Rubia
Keyword(s):  
Molecular Dynamics ◽  
Large Scale ◽  
Tight Binding ◽  
Model Potential ◽  
Binding Energies ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Mechanical Approach ◽  
Dynamics Simulations ◽  
Quantum Mechanical Approach

ABSTRACTWe critically readdress the problem of vacancy clustering in silicon by perform large-scale tight-binding molecular dynamics simulations. We also compare the results of this quantum-mechanical approach to the widely used model-potential molecular dynamics scheme based on the Tersoff and Stillinger-Weber interatomic potentials.

Calculation of protein–ligand binding affinities based on a fragment quantum mechanical method

RSC Advances ◽  
2015 ◽  
Vol 5 (129) ◽  
pp. 107020-107030 ◽  
Author(s):  
Jinfeng Liu ◽  
Xianwei Wang ◽  
John Z. H. Zhang ◽  
Xiao He
Keyword(s):  
Ligand Binding ◽  
Quantum Mechanical ◽  
Binding Affinities ◽  
Mechanical Method ◽  
Reliable Prediction ◽  
Quantum Mechanical Method

An efficient fragment-based quantum mechanical method has been successfully applied for reliable prediction of protein–ligand binding affinities.

Distinct Modulation of Wild-Type and Selective Gene Mutated Vitamin D Receptor by Essential Poly Unsaturated Fatty Acids

2021 ◽  
Vol 21 ◽  
Author(s):  
Hari Balaji ◽  
Selvaraj Ayyamperuma ◽  
Niladri Saha ◽  
Shyam Sundar Pottabathula ◽  
Jubie Selvaraj ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Vitamin D ◽  
Ligand Binding ◽  
Vitamin D Deficiency ◽  
Binding Affinity ◽  
Unsaturated Fatty Acids ◽  
Binding Domain ◽  
Ligand Binding Domain ◽  
Binding Energies ◽  
Wild Type

: Vitamin-D deficiency is a global concern. Gene mutations in the vitamin D receptor’s (VDR) ligand binding domain (LBD) variously alter the ligand binding affinity, heterodimerization with retinoid X receptor (RXR) and inhibit coactivator interactions. These LBD mutations may result in partial or total hormone unresponsiveness. A plethora of evidence report that selective long chain polyunsaturated fatty acids (PUFAs) including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and arachidonic acid (AA) bind to the ligand-binding domain of VDR and lead to transcriptional activation. We therefore hypothesize that selective PUFAs would modulate the dynamics and kinetics of VDRs, irrespective bioactive of vitamin-D binding. The spatial arrangements of the selected PUFAs in VDR active site were examined by in-silico docking studies. The docking results revealed that PUFAs have fatty acid structure-specific binding affinity towards VDR. The calculated EPA, DHA & AA binding energies (Cdocker energy) were lesser compared to vitamin-D in wild type of VDR (PDB id: 2ZLC). Of note, the DHA has higher binding interactions to the mutated VDR (PDB id: 3VT7) when compared to the standard Vitamin-D. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding of DHA with mutated VDR complex. These findings suggest the unique roles of PUFAs in VDR activation and may offer alternate strategy to circumvent vitamin-D deficiency.

scholarly journals SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Surendra Kumar ◽  
Mi-hyun Kim
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Ligand Interaction ◽  
Fingerprint Pattern ◽  
Comparable Performance ◽  
Direct Interpretation ◽  
Benchmark Datasets ◽  
Complex Features

AbstractIn drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore, researchers hope for a high correlation between docking score and pose with key interactive residues, although scoring functions as free energy surrogates of protein–ligand complexes have failed to provide collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawbacks of scoring functions. Despite being highly accurate, their featurization process is complex and the meaning of the embedded features cannot directly be interpreted by human recognition without an additional feature analysis. Here, we propose SMPLIP-Score (Substructural Molecular and Protein–Ligand Interaction Pattern Score), a direct interpretable predictor of absolute binding affinity. Our simple featurization embeds the interaction fingerprint pattern on the ligand-binding site environment and molecular fragments of ligands into an input vectorized matrix for learning layers (random forest or deep neural network). Despite their less complex features than other state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson’s correlation coefficient up to 0.80, and a root mean square error up to 1.18 in pK units with several benchmark datasets (PDBbind v.2015, Astex Diverse Set, CSAR NRC HiQ, FEP, PDBbind NMR, and CASF-2016). For this model, generality, predictive power, ranking power, and robustness were examined using direct interpretation of feature matrices for specific targets.

scholarly journals Novel Method for Quantifying AhR-Ligand Binding Affinities Using Microscale Thermophoresis

Biosensors ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 60
Author(s):  
Anne Stinn ◽  
Jens Furkert ◽  
Stefan H. E. Kaufmann ◽  
Pedro Moura-Alves ◽  
Michael Kolbe
Keyword(s):  
Ligand Binding ◽  
Environmental Pollutants ◽  
Screening Strategy ◽  
Pharmacological Intervention ◽  
Binding Affinities ◽  
Free System ◽  
Microscale Thermophoresis ◽  
Aryl Hydrocarbon ◽  
Promising Target ◽  
A Cell

The aryl hydrocarbon receptor (AhR) is a highly conserved cellular sensor of a variety of environmental pollutants and dietary-, cell- and microbiota-derived metabolites with important roles in fundamental biological processes. Deregulation of the AhR pathway is implicated in several diseases, including autoimmune diseases and cancer, rendering AhR a promising target for drug development and host-directed therapy. The pharmacological intervention of AhR processes requires detailed information about the ligand binding properties to allow specific targeting of a particular signaling process without affecting the remaining. Here, we present a novel microscale thermophoresis-based approach to monitoring the binding of purified recombinant human AhR to its natural ligands in a cell-free system. This approach facilitates a precise identification and characterization of unknown AhR ligands and represents a screening strategy for the discovery of potential selective AhR modulators.

scholarly journals Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

scholarly journals Comprehensive Virtual Screening of the Antiviral Potentialities of Marine Polycyclic Guanidine Alkaloids against SARS-CoV-2 (COVID-19)

Biomolecules ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 460
Author(s):  
Amr El-Demerdash ◽  
Ahmed M. Metwaly ◽  
Afnan Hassan ◽  
Tarek Mohamed Abd El-Aziz ◽  
Eslam B. Elkaeed ◽  
...  
Keyword(s):  
In Silico ◽  
Active Sites ◽  
Specific Treatment ◽  
Aqueous Solubility ◽  
Maximum Tolerated Dose ◽  
Binding Energies ◽  
Log P ◽  
Binding Affinities ◽  
Structural Protein ◽  
Guanidine Alkaloids

The huge global expansion of the COVID-19 pandemic caused by the novel SARS-corona virus-2 is an extraordinary public health emergency. The unavailability of specific treatment against SARS-CoV-2 infection necessitates the focus of all scientists in this direction. The reported antiviral activities of guanidine alkaloids encouraged us to run a comprehensive in silico binding affinity of fifteen guanidine alkaloids against five different proteins of SARS-CoV-2, which we investigated. The investigated proteins are COVID-19 main protease (Mpro) (PDB ID: 6lu7), spike glycoprotein (PDB ID: 6VYB), nucleocapsid phosphoprotein (PDB ID: 6VYO), membrane glycoprotein (PDB ID: 6M17), and a non-structural protein (nsp10) (PDB ID: 6W4H). The binding energies for all tested compounds indicated promising binding affinities. A noticeable superiority for the pentacyclic alkaloids particularly, crambescidin 786 (5) and crambescidin 826 (13) has been observed. Compound 5 exhibited very good binding affinities against Mpro (ΔG = −8.05 kcal/mol), nucleocapsid phosphoprotein (ΔG = −6.49 kcal/mol), and nsp10 (ΔG = −9.06 kcal/mol). Compound 13 showed promising binding affinities against Mpro (ΔG = −7.99 kcal/mol), spike glycoproteins (ΔG = −6.95 kcal/mol), and nucleocapsid phosphoprotein (ΔG = −8.01 kcal/mol). Such promising activities might be attributed to the long ω-fatty acid chain, which may play a vital role in binding within the active sites. The correlation of c Log P with free binding energies has been calculated. Furthermore, the SAR of the active compounds has been clarified. The Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) studies were carried out in silico for the 15 compounds; most examined compounds showed optimal to good range levels of ADMET aqueous solubility, intestinal absorption and being unable to pass blood brain barrier (BBB), non-inhibitors of CYP2D6, non-hepatotoxic, and bind plasma protein with a percentage less than 90%. The toxicity of the tested compounds was screened in silico against five models (FDA rodent carcinogenicity, carcinogenic potency TD50, rat maximum tolerated dose, rat oral LD50, and rat chronic lowest observed adverse effect level (LOAEL)). All compounds showed expected low toxicity against the tested models. Molecular dynamic (MD) simulations were also carried out to confirm the stable binding interactions of the most promising compounds, 5 and 13, with their targets. In conclusion, the examined 15 alkaloids specially 5 and 13 showed promising docking, ADMET, toxicity and MD results which open the door for further investigations for them against SARS-CoV-2.

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