Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein

Protein-ligand interactions are a necessary prerequisite for signal transduction, immunoreaction, and gene regulation. Protein-ligand interaction studies are important for understanding the mechanisms of biological regulation, and they provide a theoretical basis for the design and discovery of new drug targets. In this study, we analyzed the molecular interactions of protein-ligand which was docked by AutoDock 4.2 software. In AutoDock 4.2 software, we used a new search algorithm, hybrid algorithm of random drift particle swarm optimization and local search (LRDPSO), and the classical Lamarckian genetic algorithm (LGA) as energy optimization algorithms. The best conformations of each docking algorithm were subjected to molecular dynamic (MD) simulations to further analyze the molecular mechanisms of protein-ligand interactions. Here, we analyze the binding energy between protein receptors and ligands, the interactions of salt bridges and hydrogen bonds in the docking region, and the structural changes during complex unfolding. Our comparison of these complexes highlights differences in the protein-ligand interactions between the two docking methods. It also shows that salt bridge and hydrogen bond interactions play a crucial role in protein-ligand stability. The present work focuses on extracting the deterministic characteristics of docking interactions from their dynamic properties, which is important for understanding biological functions and determining which amino acid residues are crucial to docking interactions.

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

10.20944/preprints201912.0374.v1 ◽

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.

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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 ◽

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.

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Prediction of Protein–Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences

International Journal of Molecular Sciences ◽

10.3390/ijms21010024 ◽

2019 ◽

Vol 21 (1) ◽

pp. 24 ◽

Cited By ~ 3

Author(s):

Dmitry Karasev ◽

Boris Sobolev ◽

Alexey Lagunin ◽

Dmitry Filimonov ◽

Vladimir Poroikov

Keyword(s):

Prediction Accuracy ◽

Sequence Similarity ◽

Amino Acid Sequences ◽

Protein Families ◽

Pairwise Sequence Alignment ◽

Ligand Interaction ◽

Protein Ligand Interactions ◽

General Chemical ◽

Local Sequence ◽

Ligand Interactions

The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions.

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Designing interactions by control of protein–ligand complex conformation: tuning arginine–arene interaction geometry for enhanced electrostatic protein–ligand interactions

Chemical Science ◽

10.1039/c7sc04749e ◽

2018 ◽

Vol 9 (4) ◽

pp. 1014-1021 ◽

Cited By ~ 3

Author(s):

A.-L. Noresson ◽

O. Aurelius ◽

C. T. Öberg ◽

O. Engström ◽

A. P. Sundin ◽

...

Keyword(s):

Protein Conformation ◽

Salt Bridges ◽

Ligand Complex ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Galectin 3 ◽

Control Protein

3-Benzamido-2-O-sulfo-galactosides can be designed to control protein conformation into forming entropically favourable galectin-3-arginine salt bridges with ligand sulfates.

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An Investigation of Small GTPases in relation to Liver Tumorigenesis Using Traditional Chinese Medicine

BioMed Research International ◽

10.1155/2014/428210 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Tzu-Chieh Hung ◽

Wen-Yuan Lee ◽

Kuen-Bao Chen ◽

Yueh-Chiu Chan ◽

Calvin Yu-Chian Chen

Keyword(s):

Molecular Dynamics ◽

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Hydrophobic Interactions ◽

Small Gtpases ◽

Ligand Interaction ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Protein Ligand Interaction ◽

Better Than

Recently, an important topic of liver tumorigenesis had been published in 2013. In this report, Ras and Rho had defined the relation of liver tumorigenesis. The traditional Chinese medicine (TCM) database has been screened for molecular compounds by simulating molecular docking and molecular dynamics to regulate Ras and liver tumorigenesis. Saussureamine C, S-allylmercaptocysteine, and Tryptophan are selected based on the highest docking score than other TCM compounds. The molecular dynamics are helpful in the analysis and detection of protein-ligand interactions. Based on the docking poses, hydrophobic interactions, and hydrogen bond variations, this research surmises are the main regions of important amino acids in Ras. In addition to the detection of TCM compound efficacy, we suggest Saussureamine C is better than the others for protein-ligand interaction.

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Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations

Scientific Reports ◽

10.1038/s41598-021-92480-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Balint Dudas ◽

Daniel Toth ◽

David Perahia ◽

Arnaud B. Nicot ◽

Erika Balog ◽

...

Keyword(s):

Molecular Dynamics ◽

Conformational Changes ◽

Molecular Mechanisms ◽

Normal Modes ◽

Md Simulations ◽

Conformational Space ◽

Metabolizing Enzymes ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Excited Normal Modes

AbstractSulfotransferases (SULTs) are phase II drug-metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3′-phosphoadenosine 5′-phosphosulfate (PAPS) to a substrate. It has been previously suggested that a considerable shift of SULT structure caused by PAPS binding could control the capability of SULT to bind large substrates. We employed molecular dynamics (MD) simulations and the recently developed approach of MD with excited normal modes (MDeNM) to elucidate molecular mechanisms guiding the recognition of diverse substrates and inhibitors by SULT1A1. MDeNM allowed exploring an extended conformational space of PAPS-bound SULT1A1, which has not been achieved up to now by using classical MD. The generated ensembles combined with docking of 132 SULT1A1 ligands shed new light on substrate and inhibitor binding mechanisms. Unexpectedly, our simulations and analyses on binding of the substrates estradiol and fulvestrant demonstrated that large conformational changes of the PAPS-bound SULT1A1 could occur independently of the co-factor movements that could be sufficient to accommodate large substrates as fulvestrant. Such structural displacements detected by the MDeNM simulations in the presence of the co-factor suggest that a wider range of drugs could be recognized by PAPS-bound SULT1A1 and highlight the utility of including MDeNM in protein–ligand interactions studies where major rearrangements are expected.

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Graphlet signature-based scoring method to estimate protein–ligand binding affinity

Royal Society Open Science ◽

10.1098/rsos.140306 ◽

2014 ◽

Vol 1 (4) ◽

pp. 140306 ◽

Cited By ~ 5

Author(s):

Omkar Singh ◽

Kunal Sawariya ◽

Polamarasetty Aparoy

Keyword(s):

Hydrogen Bond ◽

Ligand Binding ◽

Interaction Networks ◽

Scoring Method ◽

Ligand Interaction ◽

Ligand Affinity ◽

Protein Ligand Interactions ◽

Residue Interaction ◽

Ligand Interactions ◽

Cox 2

Over the years, various computational methodologies have been developed to understand and quantify receptor–ligand interactions. Protein–ligand interactions can also be explained in the form of a network and its properties. The ligand binding at the protein-active site is stabilized by formation of new interactions like hydrogen bond, hydrophobic and ionic. These non-covalent interactions when considered as links cause non-isomorphic sub-graphs in the residue interaction network. This study aims to investigate the relationship between these induced sub-graphs and ligand activity. Graphlet signature-based analysis of networks has been applied in various biological problems; the focus of this work is to analyse protein–ligand interactions in terms of neighbourhood connectivity and to develop a method in which the information from residue interaction networks, i.e. graphlet signatures, can be applied to quantify ligand affinity. A scoring method was developed, which depicts the variability in signatures adopted by different amino acids during inhibitor binding, and was termed as GSUS (graphlet signature uniqueness score). The score is specific for every individual inhibitor. Two well-known drug targets, COX-2 and CA-II and their inhibitors, were considered to assess the method. Residue interaction networks of COX-2 and CA-II with their respective inhibitors were used. Only hydrogen bond network was considered to calculate GSUS and quantify protein–ligand interaction in terms of graphlet signatures. The correlation of the GSUS with pIC 50 was consistent in both proteins and better in comparison to the Autodock results. The GSUS scoring method was better in activity prediction of molecules with similar structure and diverse activity and vice versa. This study can be a major platform in developing approaches that can be used alone or together with existing methods to predict ligand affinity from protein–ligand complexes.

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ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex

F1000Research ◽

10.12688/f1000research.5165.1 ◽

2014 ◽

Vol 3 ◽

pp. 214 ◽

Cited By ~ 12

Author(s):

Praveen Anand ◽

Deepesh Nagarajan ◽

Sumanta Mukherjee ◽

Nagasuma Chandra

Keyword(s):

Binding Site ◽

In Silico ◽

Ligand Recognition ◽

Loss Of Function ◽

Ligand Complex ◽

Alanine Scanning Mutagenesis ◽

Ligand Interaction ◽

Alanine Scanning ◽

Protein Ligand Interactions ◽

Ligand Interactions

Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are computed by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide clues to researchers about residues to obtain loss-of-function mutations and to understand drug resistant mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

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Prediction of Dissociation Constant in Protein-Ligand Interaction

10.26434/chemrxiv.14167586 ◽

2021 ◽

Author(s):

Masatoshi Kawashima

Keyword(s):

Dissociation Constant ◽

Dissociation Constants ◽

Coefficient Of Determination ◽

Ligand Interaction ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Predicted Values ◽

Equilibrium Dissociation ◽

Antigen Antibody ◽

Antibody Interactions

In protein-ligand interactions, such as antigen-antibody interactions and hormone-receptor interactions, a correlation between the equilibrium dissociation constant KD and the reduced mass of the protein and ligand was found. The correlation of dissociation constants as pKD (-logKD) between literature values and predicted values was confirmed in high coefficient of determination R2 over 0.98.

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