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

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 214 ◽  
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/.

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

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 214 ◽  
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 calculated by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide a ranked list of residues to the user in order to obtain loss-of-function mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

Correlating structure and ligand affinity in drug discovery: a cautionary tale involving second shell residues

2014 ◽  
Vol 395 (7-8) ◽  
pp. 891-903 ◽  
Author(s):  
Anastasia Tziridis ◽  
Daniel Rauh ◽  
Piotr Neumann ◽  
Petr Kolenko ◽  
Anja Menzel ◽  
...  
Keyword(s):  
Binding Site ◽  
Proteinase Inhibitors ◽  
Factor Xa ◽  
Serine Proteinase ◽  
Crystallographic Structure ◽  
Binding Modes ◽  
Ligand Complex ◽  
Structure Based Drug Design ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Abstract A high-resolution crystallographic structure determination of a protein–ligand complex is generally accepted as the ‘gold standard’ for structure-based drug design, yet the relationship between structure and affinity is neither obvious nor straightforward. Here we analyze the interactions of a series of serine proteinase inhibitors with trypsin variants onto which the ligand-binding site of factor Xa has been grafted. Despite conservative mutations of only two residues not immediately in contact with ligands (second shell residues), significant differences in the affinity profiles of the variants are observed. Structural analyses demonstrate that these are due to multiple effects, including differences in the structure of the binding site, differences in target flexibility and differences in inhibitor binding modes. The data presented here highlight the myriad competing microscopic processes that contribute to protein–ligand interactions and emphasize the difficulties in predicting affinity from structure.

Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes

2020 ◽  
Vol 17 (2) ◽  
pp. 233-247
Author(s):  
Krishna A. Gajjar ◽  
Anuradha K. Gajjar
Keyword(s):  
Molecular Docking ◽  
Structural Information ◽  
Docking Studies ◽  
Structural Motif ◽  
Roc Curve Analysis ◽  
Ligand Complex ◽  
Discovery Studio ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Pharmacophore Models

Background: Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators. Methods: DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software. Results and Conclusion: Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.

scholarly journals Mapping of an NH-terminal Ligand Binding Site of the Insulin Receptor by Alanine Scanning Mutagenesis

1995 ◽  
Vol 270 (7) ◽  
pp. 3012-3016 ◽  
Author(s):  
Paul F. Williams ◽  
Dennis C. Mynarcik ◽  
Gui Qin Yu ◽  
Jonathan Whittaker
Keyword(s):  
Ligand Binding ◽  
Insulin Receptor ◽  
Binding Site ◽  
Ligand Binding Site ◽  
Alanine Scanning Mutagenesis ◽  
Alanine Scanning ◽  
Terminal Ligand

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.

Using molecular docking and molecular dynamics to investigate protein-ligand interactions

2021 ◽  
Vol 35 (08) ◽  
pp. 2130002
Author(s):  
Connor J. Morris ◽  
Dennis Della Corte
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Drug Discovery ◽  
State Of The Art ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Art Methods ◽  
Ligand Interactions ◽  
Docking Calculations ◽  
Ligand Bond

Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular docking and MD methods.

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

2019 ◽  
Vol 21 (1) ◽  
pp. 24 ◽  
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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