Assessment of scoring functions and in silico parameters for AChE-ligand interactions as a tool for predicting inhibition potency

2019 ◽  
Vol 308 ◽  
pp. 216-223 ◽  
Author(s):  
Goran Šinko
Keyword(s):  
In Silico ◽  
Scoring Functions ◽  
Ligand Interactions ◽  
Inhibition Potency

scholarly journals Elucidating Binding Sites and Affinities of ERα Agonists and Antagonists to Human Alpha-Fetoprotein by In Silico Modeling and Point Mutagenesis

2020 ◽  
Vol 21 (3) ◽  
pp. 893 ◽  
Author(s):  
Nurbubu T. Moldogazieva ◽  
Daria S. Ostroverkhova ◽  
Nikolai N. Kuzmich ◽  
Vladimir V. Kadochnikov ◽  
Alexander A. Terentiev ◽  
...  
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
Electrostatic Interactions ◽  
Molecular Mechanisms ◽  
3D Structure ◽  
Alpha Fetoprotein ◽  
Affinity Binding ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Ligand Interactions

Alpha-fetoprotein (AFP) is a major embryo- and tumor-associated protein capable of binding and transporting a variety of hydrophobic ligands, including estrogens. AFP has been shown to inhibit estrogen receptor (ER)-positive tumor growth, which can be attributed to its estrogen-binding ability. Despite AFP having long been investigated, its three-dimensional (3D) structure has not been experimentally resolved and molecular mechanisms underlying AFP–ligand interaction remains obscure. In our study, we constructed a homology-based 3D model of human AFP (HAFP) with the purpose of molecular docking of ERα ligands, three agonists (17β-estradiol, estrone and diethylstilbestrol), and three antagonists (tamoxifen, afimoxifene and endoxifen) into the obtained structure. Based on the ligand-docked scoring functions, we identified three putative estrogen- and antiestrogen-binding sites with different ligand binding affinities. Two high-affinity binding sites were located (i) in a tunnel formed within HAFP subdomains IB and IIA and (ii) on the opposite side of the molecule in a groove originating from a cavity formed between domains I and III, while (iii) the third low-affinity binding site was found at the bottom of the cavity. Here, 100 ns molecular dynamics (MD) simulation allowed us to study their geometries and showed that HAFP–estrogen interactions were caused by van der Waals forces, while both hydrophobic and electrostatic interactions were almost equally involved in HAFP–antiestrogen binding. Molecular mechanics/Generalized Born surface area (MM/GBSA) rescoring method exploited for estimation of binding free energies (ΔGbind) showed that antiestrogens have higher affinities to HAFP as compared to estrogens. We performed in silico point substitutions of amino acid residues to confirm their roles in HAFP–ligand interactions and showed that Thr132, Leu138, His170, Phe172, Ser217, Gln221, His266, His316, Lys453, and Asp478 residues, along with two disulfide bonds (Cys224–Cys270 and Cys269–Cys277), have key roles in both HAFP–estrogen and HAFP–antiestrogen binding. Data obtained in our study contribute to understanding mechanisms underlying protein–ligand interactions and anticancer therapy strategies based on ERα-binding ligands.

scholarly journals ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xujun Zhang ◽  
Chao Shen ◽  
Xueying Guo ◽  
Zhe Wang ◽  
Gaoqi Weng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Web Server ◽  
Scoring Functions ◽  
Protein Ligand Interactions ◽  
Prediction Module ◽  
Ligand Interactions ◽  
Benchmark Datasets

AbstractVirtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

scholarly journals Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2600
Author(s):  
Fábio G. Martins ◽  
André Melo ◽  
Sérgio F. Sousa
Keyword(s):  
Virtual Screening ◽  
Quorum Sensing ◽  
In Silico ◽  
Bacterial Infections ◽  
Extracellular Polymeric Substances ◽  
Free Energy Calculations ◽  
Ligand Docking ◽  
Screening Tests ◽  
Scoring Functions ◽  
Potential Inhibitors

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition.

scholarly journals Elucidating Binding Sites and Affinities of ERα Agonist and Antagonist to Human Alpha-Fetoprotein by in silico Modeling and Point Mutagenesis

2019 ◽  
Author(s):  
Nurbubu T. Moldogazieva ◽  
Daria S. Ostroverkhova ◽  
Nikolai N. Kuzmich ◽  
Vladimir V. Kadochnikov ◽  
Alexander A. Terentiev ◽  
...  
Keyword(s):  
Binding Sites ◽  
In Silico ◽  
Disulfide Bonds ◽  
Electrostatic Interactions ◽  
Molecular Mechanisms ◽  
3D Structure ◽  
Scoring Function ◽  
Alpha Fetoprotein ◽  
Ligand Interaction ◽  
Ligand Interactions

Alpha-fetoprotein (AFP) is a major embryo- and tumor-associated protein capable of binding and transporting variety of hydrophobic ligands including estrogens. AFP has been shown to inhibit estrogen receptor (ER)-positive tumor growth and this can be attributed to its estrogen-binding ability. Despite AFP has long been investigated, its three-dimensional (3D) structure has not been experimentally resolved and molecular mechanisms underlying AFP-ligand interaction remain obscure. In our study we constructed homology-based 3D model of human AFP (HAFP) with the purpose to perform docking of ERα ligands, three agonists (17β-estradiol, estrone and diethylstilbestrol) and three antagonists (tamoxifen, afimoxifene and endoxifen) into the obtained structure. Based on ligand docked scoring function, we identified three putative estrogen- and antiestrogen-binding sites with different ligand binding affinities. Two high-affinity sites were located in (i) a tunnel formed within HAFP subdomains IB and IIA and (ii) opposite side of the molecule in a groove originating from cavity formed between domains I and III, while (iii) the third low-affinity site was found at the bottom of the cavity. 100 ns MD simulation allowed studying their geometries and showed that HAFP-estrogen interactions occur due to van der Waals forces, while both hydrophobic and electrostatic interactions were almost equally involved in HAFP-antiestrogen binding. MM/GBSA rescoring method estimated binding free energies (ΔGbind) and showed that antiestrogens have higher affinities to HAFP as compared to estrogens. We performed in silico point substitutions of amino acid residues to confirm their roles in HAFP-ligand interactions and showed that Thr132, Leu138, His170, Phe172, Ser217, Gln221, His266, His316, Lys453, and Asp478 residues along two disulfide bonds, Cys224-Cys270 and Cys269-Cys277 have key roles in both HAFP-estrogen and HAFP-antiestrogen binding. Data obtained in our study contribute to understanding mechanisms underlying protein-ligand interactions and anti-cancer therapy strategies based on ER-binding ligands.

scholarly journals Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors

2020 ◽  
Vol 21 (15) ◽  
pp. 5183 ◽  
Author(s):  
Eric D. Boittier ◽  
Yat Yin Tang ◽  
McKenna E. Buckley ◽  
Zachariah P. Schuurs ◽  
Derek J. Richard ◽  
...  
Keyword(s):  
Scoring Function ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Molecular Fingerprints ◽  
Biologically Relevant ◽  
Protein Ligand Interactions ◽  
Molecular Features ◽  
Ligand Interactions ◽  
Model Protein ◽  
Scoring Accuracy

A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca2+ concentrations. Recently, a small-molecule inhibitor of CD38 was shown to slow down pathways relating to aging and DNA damage. We examined the performance of seven docking programs for their ability to model protein-ligand interactions with CD38. A test set of twelve CD38 crystal structures, containing crystallized biologically relevant substrates, were used to assess pose prediction. The rankings for each program based on the median RMSD between the native and predicted were Vina, AD4 > PLANTS, Gold, Glide, Molegro > rDock. Forty-two compounds with known affinities were docked to assess the accuracy of the programs at affinity/ranking predictions. The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock. Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size. Factors that affect the reliability of pose prediction and scoring are discussed. General limitations and known biases of scoring functions are examined, aided in part by using molecular fingerprints and Random Forest classifiers. This machine learning approach may be used to systematically diagnose molecular features that are correlated with poor scoring accuracy.

C(X)CR in silico: Computer-aided prediction of chemokine receptor–ligand interactions

2012 ◽  
Vol 9 (4) ◽  
pp. e281-e291 ◽  
Author(s):  
L. Roumen ◽  
D.J. Scholten ◽  
P. de Kruijf ◽  
I.J.P. de Esch ◽  
R. Leurs ◽  
...  
Keyword(s):  
Chemokine Receptor ◽  
In Silico ◽  
Receptor Ligand ◽  
Computer Aided ◽  
Ligand Interactions

Assessing Scoring Functions for Protein−Ligand Interactions

2004 ◽  
Vol 47 (12) ◽  
pp. 3032-3047 ◽  
Author(s):  
Philippe Ferrara ◽  
Holger Gohlke ◽  
Daniel J. Price ◽  
Gerhard Klebe ◽  
Charles L. Brooks
Keyword(s):  
Scoring Functions ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

scholarly journals Combining Molecular Dynamic Information and an Aspherical-Atom Data Bank in the Evaluation of the Electrostatic Interaction Energy in Multimeric Protein-Ligand Complex: A Case Study for HIV-1 Protease

2021 ◽  
Author(s):  
Prashant Kumar ◽  
Paulina Dominiak
Keyword(s):  
Interaction Energy ◽  
Molecular Dynamic ◽  
Binding Affinity ◽  
Electrostatic Interaction ◽  
Electrostatic Interactions ◽  
Data Bank ◽  
Scoring Functions ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Hiv 1

<div> <div> <div> <p>Computational analysis of protein-ligand interactions is of crucial importance for drug discovery. Assessment of ligand binding energy allows us to have a glimpse on the potential of a small organic molecule to be a ligand to the binding site of a protein target. Available scoring functions such as in docking programs, we could say that they all rely on equations that sum each type of protein-ligand interactions to model the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. Electrostatic interactions contribute one of the most important part of total interaction energies between macromolecules, unlike dispersion forces they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In this paper, complexes of HIV-1 protease with inhibitor molecules (JE-2147 and Darunavir) have been analysed using charge densities from a transferable aspherical-atom data bank. Moreover, we analyse the electrostatic interaction energy for an ensemble of structures using molecular dynamic simulation to highlight the main features related to the importance of this interaction for binding affinity. </p> </div> </div> </div>

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