scholarly journals Inverse PI by NMR: Analysis of Ligand 1H-Chemical Shifts in the Protein-Bound State

2021 ◽  
Author(s):  
Gerald Platzer ◽  
Moriz Mayer ◽  
Jark Boettcher ◽  
Leonhard Geist ◽  
Julian E. Fuchs ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Chemical Shifts ◽  
Bound State ◽  
Structure Information ◽  
Π Interactions ◽  
Protein Ligand Interactions ◽  
Protein Deuteration ◽  
Ligand Interactions ◽  
A Site ◽  
Ligand Conformation

The study of protein-ligand interactions via protein-based NMR generally relies on the detection of chemical-shift changes induced by ligand binding. However, the chemical shift of the ligand when bound to the protein is rarely discussed, since it is not readily detectable. In this work we use protein deuteration in combination with [1H-1H]-NOESY NMR to extract 1H chemical shift values of the ligand in the bound state. The chemical shift perturbations (CSPs) experienced by the proton ligand resonances (free vs bound) are an extremely rich source of information on protein-ligand complexes. Besides allowing for the detection of intermolecular CH-π interactions and elucidation of the protein-bound ligand conformation, the CSP information can be used to analyse (de)solvation effects in a site-specific manner. In conjunction with crystal structure information, it is possible to distinguish protons whose desolvation penalty is compensated for upon protein-binding, from those that are not. Combined with the previously reported PI by NMR technique for the protein-based detection of intermolecular CH-π interactions, this method represents another important step towards the ultimate goal of Interaction-Based Drug Discovery.

scholarly journals acACS: Improving the Prediction Accuracy of Protein Subcellular Locations and Protein Classification by Incorporating the Average Chemical Shifts Composition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guo-Liang Fan ◽  
Yan-Ling Liu ◽  
Yong-Chun Zuo ◽  
Han-Xue Mei ◽  
Yi Rang ◽  
...  
Keyword(s):  
Chemical Shift ◽  
Prediction Accuracy ◽  
Structural Changes ◽  
Chemical Shifts ◽  
Protein Classification ◽  
Structure Information ◽  
Local Environments ◽  
Protein Subcellular Locations ◽  
Auto Covariance ◽  
Online Web

The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.

scholarly journals Molecular basis for the specific recognition of the metazoan cyclic GMP-AMP by the innate immune adaptor protein STING

2015 ◽  
Vol 112 (29) ◽  
pp. 8947-8952 ◽  
Author(s):  
Heping Shi ◽  
Jiaxi Wu ◽  
Zhijian J. Chen ◽  
Chuo Chen
Keyword(s):  
Cyclic Gmp ◽  
Free Ligand ◽  
Adaptor Protein ◽  
Endoplasmic Reticulum Membrane ◽  
Type I ◽  
Linkage Isomers ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Preferential Binding ◽  
Ligand Conformation

Cyclic GMP-AMP containing a unique combination of mixed phosphodiester linkages (2′3′-cGAMP) is an endogenous second messenger molecule that activates the type-I IFN pathway upon binding to the homodimer of the adaptor protein STING on the surface of endoplasmic reticulum membrane. However, the preferential binding of the asymmetric ligand 2′3′-cGAMP to the symmetric dimer of STING represents a physicochemical enigma. Here we show that 2′3′-cGAMP, but not its linkage isomers, adopts an organized free-ligand conformation that resembles the STING-bound conformation and pays low entropy and enthalpy costs in converting into the active conformation. Our results demonstrate that analyses of free-ligand conformations can be as important as analyses of protein conformations in understanding protein–ligand interactions.

scholarly journals Interact: Automated analysis of protein-ligand interactions by 1D and 2D NMR

2018 ◽  
Author(s):  
Pierre Millard ◽  
Guy Lippens
Keyword(s):  
Dissociation Constants ◽  
Chemical Shifts ◽  
Automated Analysis ◽  
2D Nmr ◽  
Nmr Titration ◽  
Protein Ligand Interactions ◽  
Fitting Procedures ◽  
Ligand Interactions ◽  
Titration Data ◽  
Automated Tools

AbstractNMR titration experiments contain rich information on the thermodynamic, kinetic and structural aspects of protein-ligand interactions. Automated tools are required to process the large number of signals typically acquired in these experiments and facilitate quantitative interpretations. We present Interact, a Python script accessible within the Bruker BioSpin TopSpin™ software, which allows automated analysis of both 1D and 2D NMR titration experiments. Interact performs peak picking and annotation of the successive spectra and supports quantitative interpretation of changes in chemical shifts and linewidths induced by the ligand (e.g. to estimate dissociation constants) through different fitting procedures. Interact can be applied to all types of 1D and 2D NMR experiments and all nuclei, hence facilitating routine analysis of existing and forthcoming NMR titration data. Interact was implemented in Python and can be used on Windows, Unix and MacOS platforms. The source code is distributed under OpenSource license at http://github.com/MetaSys-LISBP/Interact.

Studies of protein–ligand interactions by NMR

2003 ◽  
Vol 31 (5) ◽  
pp. 1006-1009 ◽  
Author(s):  
J. Clarkson ◽  
I.D. Campbell
Keyword(s):  
Chemical Shift ◽  
Structure Determination ◽  
Dissociation Constants ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Small Proteins ◽  
Ligand Interactions

Solution-state NMR has become an accepted method for studying the structure of small proteins in solution. This has resulted in over 3000 NMR-based co-ordinate sets being deposited in the Protein Databank. It is becoming increasingly apparent, however, that NMR is also a very powerful tool for accessing interactions between macromolecules and various ligands. These interactions can be assessed at a wide variety of levels, e.g. qualitative screening of libraries of pharmaceuticals and ‘chemical shift mapping’. Dissociation constants can sometimes be obtained in such cases. Another example would be the complete three-dimensional structure determination of a protein–ligand complex. Here we briefly describe a few of the principles involved and illustrate the method with recent examples.

scholarly journals SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

2019 ◽  
Author(s):  
Niraj Verma ◽  
Xingming Qu ◽  
Francesco Trozzi ◽  
Mohamed Elsaied ◽  
Nischal Karki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Computational Prediction ◽  
Ligand Interaction ◽  
Structure Information ◽  
Protein Ligand Interactions ◽  
Intermediate Layers ◽  
Ligand Interactions ◽  
Protein Ligand Interaction ◽  
Curvature And Torsion

AbstractComputational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-machine learning models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure based deep learning, which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research.

scholarly journals SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

2021 ◽  
Vol 22 (3) ◽  
pp. 1392
Author(s):  
Niraj Verma ◽  
Xingming Qu ◽  
Francesco Trozzi ◽  
Mohamed Elsaied ◽  
Nischal Karki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
De Novo ◽  
Computational Prediction ◽  
Ligand Interaction ◽  
Structure Information ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Protein Ligand Interaction ◽  
Curvature And Torsion

Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utilizes secondary structure information of proteins extracted as the curvature and torsion of the protein backbone to predict PLI. We demonstrate the performance of SSnet by comparing against a variety of currently popular machine and non-Machine Learning (ML) models using various metrics. We visualize the intermediate layers of SSnet to show a potential latent space for proteins, in particular to extract structural elements in a protein that the model finds influential for ligand binding, which is one of the key features of SSnet. We observed in our study that SSnet learns information about locations in a protein where a ligand can bind, including binding sites, allosteric sites and cryptic sites, regardless of the conformation used. We further observed that SSnet is not biased to any specific molecular interaction and extracts the protein fold information critical for PLI prediction. Our work forms an important gateway to the general exploration of secondary structure-based Deep Learning (DL), which is not just confined to protein-ligand interactions, and as such will have a large impact on protein research, while being readily accessible for de novo drug designers as a standalone package.

19F-NMR in Target-based Drug Discovery

2019 ◽  
Vol 26 (26) ◽  
pp. 4964-4983 ◽  
Author(s):  
CongBao Kang
Keyword(s):  
Drug Discovery ◽  
Conformational Changes ◽  
Protein Structures ◽  
19F Nmr ◽  
Binding Affinities ◽  
Protein Ligand Interactions ◽  
Compound Libraries ◽  
Ligand Interactions ◽  
Reference Ligand ◽  
Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

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.

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