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

2018 ◽  
Vol 9 (4) ◽  
pp. 1014-1021 ◽  
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.

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.

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.

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.

Structures of apo and GTP-bound molybdenum cofactor biosynthesis protein MoaC fromThermus thermophilusHB8

2010 ◽  
Vol 66 (7) ◽  
pp. 821-833 ◽  
Author(s):  
Shankar Prasad Kanaujia ◽  
Jeyaraman Jeyakanthan ◽  
Noriko Nakagawa ◽  
Sathyaramya Balasubramaniam ◽  
Akeo Shinkai ◽  
...  
Keyword(s):  
Thermus Thermophilus ◽  
Md Simulations ◽  
Molybdenum Cofactor ◽  
Titration Calorimetry ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Enzyme Superfamily ◽  
Ligand Interactions ◽  
Cofactor Biosynthesis ◽  
Molybdenum Cofactor Biosynthesis

The first step in the molybdenum cofactor (Moco) biosynthesis pathway involves the conversion of guanosine triphosphate (GTP) to precursor Z by two proteins (MoaA and MoaC). MoaA belongs to theS-adenosylmethionine-dependent radical enzyme superfamily and is believed to generate protein and/or substrate radicals by reductive cleavage ofS-adenosylmethionine using an Fe–S cluster. MoaC has been suggested to catalyze the release of pyrophosphate and the formation of the cyclic phosphate of precursor Z. However, structural evidence showing the binding of a substrate-like molecule to MoaC is not available. Here, apo and GTP-bound crystal structures of MoaC fromThermus thermophilusHB8 are reported. Furthermore, isothermal titration calorimetry experiments have been carried out in order to obtain thermodynamic parameters for the protein–ligand interactions. In addition, molecular-dynamics (MD) simulations have been carried out on the protein–ligand complex of known structure and on models of relevant complexes for which X-ray structures are not available. The biophysical, structural and MD results reveal the residues that are involved in substrate binding and help in speculating upon a possible mechanism.

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/.

NMR Approaches To Understanding Protein Specificity

1997 ◽  
Author(s):  
G.C. K. Roberts ◽  
L.-Y. Lian
Keyword(s):  
Nmr Spectroscopy ◽  
Ligand Binding ◽  
Pathogenic Bacteria ◽  
Structural Information ◽  
Nuclear Overhauser Effect ◽  
Protein Protein Interactions ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Nuclear Overhauser

The biological functions of proteins all depend on their highly specific interactions with other molecules, and the understanding of the molecular basis of the specificity of these interactions is an important part of the effort to understand protein structure-function relationships. NMR spectroscopy can provide information on many different aspects of protein-ligand interactions, ranging from the determination of the complete structure of a protein-ligand complex to focussing on selected features of the interactions between the ligand and protein by using “reporter groups” on the ligand or the protein. It has two particular advantages: the ability to study the complex in solution, and the ability to provide not only structural, but also dynamic, kinetic and thermodynamic information on ligand binding. Early analyses of ligand binding (Jardetzky and Roberts, 1981) focused on measurements of relaxation times, chemical shifts and coupling constants, which gave relatively limited, although valuable, structural information. More recently, it has become possible to obtain much more detailed information, due to the extensive use of nuclear Overhauser effect measurements and isotope-labeled proteins and ligands; a number of reviews of this area are available (Feeney and Birdsall, 1993; Lian et al, 1994; Wand and Short, 1994; Petros and Fesik, 1994; Wemmer and Williams, 1994). In this article, we describe some recent work from our laboratory which illustrates the use of NMR spectroscopy to obtain structural and mechanistic information on relatively large enzyme-substrate and proteinprotein complexes. A number of species of pathogenic bacteria, notably Streptococci and Staphylococci, have proteins on their surface that bind immurioglobulins (reviewed in Boyle (1990)). Protein A from S. aureus and protein G from species of Streptococci are widely used as imrnunological tools and are the most extensively studied of these antibody-binding proteins. A detailed understanding of the binding mechanisms of these proteins is important, not only for providing us with the structural basis for their functions, but also as a contribution toward understanding the general rules of protein-protein interactions.

A microfluidic linear node array for the study of protein–ligand interactions

Lab on a Chip ◽  
2014 ◽  
Vol 14 (20) ◽  
pp. 3993-3999 ◽  
Author(s):  
Cheuk-Wing Li ◽  
Guodong Yu ◽  
Jingyun Jiang ◽  
Simon Ming-Yuen Lee ◽  
Changqing Yi ◽  
...  
Keyword(s):  
Microfluidic Device ◽  
Ligand Complex ◽  
Continuous Separation ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Through utilizing streamline reversibility, we developed a microfluidic device for the continuous separation of free ligands from a protein–ligand complex for off-chip detection.

scholarly journals Layer-wise relevance propagation of InteractionNet explains protein–ligand interactions at the atom level

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hyeoncheol Cho ◽  
Eok Kyun Lee ◽  
Insung S. Choi
Keyword(s):  
Dissociation Constants ◽  
Structural Information ◽  
Three Dimensional ◽  
Graph Representation ◽  
Ligand Complex ◽  
Covalent Bonds ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Atom Level ◽  
Graph Neural Networks

AbstractDevelopment of deep-learning models for intermolecular noncovalent (NC) interactions between proteins and ligands has great potential in the chemical and pharmaceutical tasks, including structure–activity relationship and drug design. It still remains an open question how to convert the three-dimensional, structural information of a protein–ligand complex into a graph representation in the graph neural networks (GNNs). It is also difficult to know whether a trained GNN model learns the NC interactions properly. Herein, we propose a GNN architecture that learns two distinct graphs—one for the intramolecular covalent bonds in a protein and a ligand, and the other for the intermolecular NC interactions between the protein and the ligand—separately by the corresponding covalent and NC convolutional layers. The graph separation has some advantages, such as independent evaluation on the contribution of each convolutional step to the prediction of dissociation constants, and facile analysis of graph-building strategies for the NC interactions. In addition to its prediction performance that is comparable to that of a state-of-the art model, the analysis with an explainability strategy of layer-wise relevance propagation shows that our model successfully predicts the important characteristics of the NC interactions, especially in the aspect of hydrogen bonding, in the chemical interpretation of protein–ligand binding.

scholarly journals Toward Simple, Predictive Understanding of Protein-Ligand Interactions: Electronic Structure Calculations Join Forces with the Chemist’s Intuition

2020 ◽  
Author(s):  
Nitai Sylvetsky
Keyword(s):  
Molecular Structures ◽  
Quantitative Prediction ◽  
Binding Affinities ◽  
Ligand Complex ◽  
Independent Variables ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Static Pictures ◽  
Predictive Understanding ◽  
Structure Calculations

Contemporary efforts for modeling protein-ligand interactions entail a painful tradeoff – as reliable information on both noncovalent binding factors and the dynamic behavior of a protein-ligand complex is often beyond practical limits. In the following paper, we demonstrate that information drawn exclusively from static molecular structures can be used for the semi-quantitative prediction of experimentally-measured binding affinities for protein-ligand complexes. In the particular case considered here, inhibition constants (Ki) were calculated for eight different ligands of torpedo californica acetylcholinesterase using a simple, multiple-linearregression-based model. The latter, incorporating five informative and independent variables – drawn from QM cluster, DLPNO-CCSD(T) calculations and LED analyses on the eight complexes – is shown to recover no-lessthan 96% of the sum of squares for measured Ki values, and used to predict the inhibition potential for yet another ligand (E20, for which no Ki values are available in the literature). This thus challenges the widespread assumption that “static pictures” are inadequate for predicting reactivity properties of flexible and dynamic protein-ligand systems.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yi Fu ◽  
Ji Zhao ◽  
Zhiguo Chen
Keyword(s):  
Structural Changes ◽  
Molecular Mechanisms ◽  
Search Algorithm ◽  
Dynamic Properties ◽  
Dynamics Simulation ◽  
Salt Bridges ◽  
Ligand Interaction ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Autodock 4.2

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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