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

Data Bank ◽

Scoring Functions ◽

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>

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

Molecules ◽

10.3390/molecules26133872 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3872

Author(s):

Prashant Kumar ◽

Paulina Maria Dominiak

Keyword(s):

Interaction Energy ◽

Molecular Dynamic ◽

Binding Affinity ◽

Electrostatic Interaction ◽

Scoring Functions ◽

Ligand Complex ◽

Ligand Interactions ◽

Hiv 1

Computational analysis of protein–ligand interactions is of crucial importance for drug discovery. Assessment of ligand binding energy allows us to have a glimpse of 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, all rely on equations that sum each type of protein–ligand interactions in order to predict the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. Electrostatic interactions constitute one of the most important part of total interactions 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 study, complexes of HIV-1 protease with inhibitor molecules (JE-2147 and darunavir) were analyzed by using charge densities from the transferable aspherical-atom University at Buffalo Databank (UBDB). Moreover, we analyzed the electrostatic interaction energy for an ensemble of structures, using molecular dynamic simulations to highlight the main features of electrostatic interactions important for binding affinity.

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

Journal of Cheminformatics ◽

10.1186/s13321-021-00486-3 ◽

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 ◽

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.

Protein-ligand interactions in the lysine-binding site of plasminogen kringle 4 are different in crystal and solution. Electrostatic interactions studied by site-directed mutagenesis exclude Lys35 as an important acceptor in solution

Biochemistry ◽

10.1021/bi00211a010 ◽

1993 ◽

Vol 32 (48) ◽

pp. 13019-13025 ◽

Cited By ~ 6

Author(s):

Peter Reinholt Nielsen ◽

Katja Einer-Jensen ◽

Thor L. Holtet ◽

Bente Damm Andersen ◽

Flemming M. Poulsen ◽

...

Keyword(s):

Binding Site ◽

Site Directed Mutagenesis ◽

Directed Mutagenesis ◽

Ligand Interactions

Molecular tapes in the structure of isoguaninium chloride

Acta Crystallographica Section C Structural Chemistry ◽

10.1107/s2053229617017685 ◽

2017 ◽

Vol 74 (1) ◽

pp. 108-112 ◽

Cited By ~ 1

Author(s):

Urszula Anna Budniak ◽

Paulina Maria Dominiak

Keyword(s):

Crystal Structure ◽

Single Crystal ◽

Charge Density ◽

Interaction Energy ◽

Electrostatic Interaction ◽

The Other ◽

X Ray Diffraction ◽

X Ray ◽

Diffraction Structure

Isoguanine, an analogue of guanine, is of intrinsic interest as a noncanonical nucleobase. The crystal structure of isoguaninium chloride (systematic name: 6-amino-2-oxo-1H,7H-purin-3-ium chloride), C5H6N5O+·Cl−, has been determined by single-crystal X-ray diffraction. Structure analysis was supported by electrostatic interaction energy (E es) calculations based on charge density reconstructed with the UBDB databank. In the structure, two kinds of molecular tapes are observed, one parallel to (010) and the other parallel to (50\overline{4}). The tapes are formed by dimers of isoguaninium cations interacting with chloride anions. E es analysis indicates that cations in one kind of tape are oriented so as to minimize repulsive electrostatic interactions.

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

International Journal of Molecular Sciences ◽

10.3390/ijms21155183 ◽

2020 ◽

Vol 21 (15) ◽

pp. 5183 ◽

Cited By ~ 1

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 ◽

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.

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

International Journal of Molecular Sciences ◽

10.3390/ijms21030893 ◽

2020 ◽

Vol 21 (3) ◽

pp. 893 ◽

Cited By ~ 3

Author(s):

Nurbubu T. Moldogazieva ◽

Daria S. Ostroverkhova ◽

Nikolai N. Kuzmich ◽

Vladimir V. Kadochnikov ◽

Alexander A. Terentiev ◽

...

Keyword(s):

Binding Sites ◽

In Silico ◽

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.

Preorganization in biological systems: Are conformational constraints worth the energy?

Pure and Applied Chemistry ◽

10.1351/pac200779020193 ◽

2007 ◽

Vol 79 (2) ◽

pp. 193-200 ◽

Cited By ~ 27

Author(s):

Stephen F. Martin

Keyword(s):

Experimental Evidence ◽

Binding Affinity ◽

Three Dimensional ◽

Common Belief ◽

Flexible Control ◽

Conformational Constraints ◽

Ligand Interactions ◽

Dimensional Shape ◽

Three Dimensional Shape

It is generally assumed that preorganizing a flexible ligand in the three-dimensional shape it adopts when bound to a macromolecular receptor will provide a derivative having an increased binding affinity, primarily because the rigidified molecule is expected to benefit from a lesser entropic penalty during complexation. We now provide the first experimental evidence that demonstrates this common belief is not universally true. Indeed, we find that ligand preorganization may be accompanied by an unfavorable entropy of binding, even when the constrained ligand exhibits a higher binding affinity than its flexible control. Thus, the effects that ligand preorganization have upon energetics and structure in protein-ligand interactions must be reevaluated.