International Journal of Molecular Sciences

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18 pages, 25553 KiB

Open AccessArticle

Biochemical and Computational Studies of the Interaction between a Glucosamine Derivative, NAPA, and the IKKα Kinase

by Mariangela Lopreiato, Samuele Di Cristofano, Rossana Cocchiola, Alessia Mariano, Libera Guerrizio, Roberto Scandurra, Luciana Mosca, Domenico Raimondo and Anna Scotto d’Abusco

Int. J. Mol. Sci. 2021, 22(4), 1643; https://doi.org/10.3390/ijms22041643 - 6 Feb 2021

Cited by 3 | Viewed by 2338

Abstract

The glucosamine derivative 2-(N-Acetyl)-L-phenylalanylamido-2-deoxy-

β

-D-glucose (NAPA), was shown to inhibit the kinase activity of IKK

α

, one of the two catalytic subunits of IKK complex, decreasing the inflammatory status in osteoarthritis chondrocytes. In the present work we have investigated the inhibition [...] Read more.

The glucosamine derivative 2-(N-Acetyl)-L-phenylalanylamido-2-deoxy-

β

-D-glucose (NAPA), was shown to inhibit the kinase activity of IKK

α

, one of the two catalytic subunits of IKK complex, decreasing the inflammatory status in osteoarthritis chondrocytes. In the present work we have investigated the inhibition mechanism of IKK

α

by NAPA by combining computational simulations, in vitro assays and Mass Spectrometry (MS) technique. The kinase in vitro assay was conducted using a recombinant IKK

α

and IKKtide, a 20 amino acid peptide substrate derived from IkB

α

kinase protein and containing the serine residues Ser32 and Ser36. Phosphorylated peptide production was measured by Ultra Performance Liquid Chromatography coupled with Mass Spectrometry (UPLC-MS), and the atomic interaction between IKK

α

and NAPA has been studied by molecular docking and Molecular Dynamics (MD) approaches. Here we report that NAPA was able to inhibit the IKK

α

kinase activity with an IC₅₀ of 0.5 mM, to decrease the K_m value from 0.337 mM to 0.402 mM and the V_max from 0.0257 mM·min

^{- 1}

to 0.0076 mM·min

^{- 1}

. The computational analyses indicate the region between the KD, ULD and SDD domains of IKK

α

as the optimal binding site explored by NAPA. Biochemical data indicate that there is a non-significant difference between K_m and K_i whereas there is a statistically significant difference between the two V_max values. This evidence, combined with computational results, consistently indicates that the inhibition is non-competitive, and that the NAPA binding site is different than that of ATP or IKKtide. Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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17 pages, 12389 KiB

Open AccessArticle

Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking

by Nischal Karki, Niraj Verma, Francesco Trozzi, Peng Tao, Elfi Kraka and Brian Zoltowski

Int. J. Mol. Sci. 2021, 22(4), 1573; https://doi.org/10.3390/ijms22041573 - 4 Feb 2021

Cited by 26 | Viewed by 4064

Abstract

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple [...] Read more.

Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy. Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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27 pages, 3802 KiB

Open AccessArticle

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

by Niraj Verma, Xingming Qu, Francesco Trozzi, Mohamed Elsaied, Nischal Karki, Yunwen Tao, Brian Zoltowski, Eric C. Larson and Elfi Kraka

Int. J. Mol. Sci. 2021, 22(3), 1392; https://doi.org/10.3390/ijms22031392 - 30 Jan 2021

Cited by 24 | Viewed by 6455

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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16 pages, 3438 KiB

Open AccessArticle

Investigation of Receptor Heteromers Using NanoBRET Ligand Binding

by Elizabeth K. M. Johnstone, Heng B. See, Rekhati S. Abhayawardana, Angela Song, K. Johan Rosengren, Stephen J. Hill and Kevin D. G. Pfleger

Int. J. Mol. Sci. 2021, 22(3), 1082; https://doi.org/10.3390/ijms22031082 - 22 Jan 2021

Cited by 9 | Viewed by 3995

Abstract

Receptor heteromerization is the formation of a complex involving at least two different receptors with pharmacology that is distinct from that exhibited by its constituent receptor units. Detection of these complexes and monitoring their pharmacology is crucial for understanding how receptors function. The [...] Read more.

Receptor heteromerization is the formation of a complex involving at least two different receptors with pharmacology that is distinct from that exhibited by its constituent receptor units. Detection of these complexes and monitoring their pharmacology is crucial for understanding how receptors function. The Receptor-Heteromer Investigation Technology (Receptor-HIT) utilizes ligand-dependent modulation of interactions between receptors and specific biomolecules for the detection and profiling of heteromer complexes. Previously, the interacting biomolecules used in Receptor-HIT assays have been intracellular proteins, however in this study we have for the first time used bioluminescence resonance energy transfer (BRET) with fluorescently-labeled ligands to investigate heteromerization of receptors on the cell surface. Using the Receptor-HIT ligand binding assay with NanoBRET, we have successfully investigated heteromers between the angiotensin II type 1 (AT₁) receptor and the β₂ adrenergic receptor (AT₁-β₂AR heteromer), as well as between the AT₁ and angiotensin II type 2 receptor (AT₁-AT₂ heteromer). Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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16 pages, 26061 KiB

Open AccessArticle

Low Basicity as a Characteristic for Atypical Ligands of Serotonin Receptor 5-HT2

by Sabina Podlewska, Ryszard Bugno, Enza Lacivita, Marcello Leopoldo, Andrzej J. Bojarski and Jadwiga Handzlik

Int. J. Mol. Sci. 2021, 22(3), 1035; https://doi.org/10.3390/ijms22031035 - 21 Jan 2021

Cited by 3 | Viewed by 2120

Abstract

Serotonin receptors are extensively examined by academic and industrial researchers, due to their vital roles, which they play in the organism and constituting therefore important drug targets. Up to very recently, it was assumed that the basic nitrogen in compound structure is a [...] Read more.

Serotonin receptors are extensively examined by academic and industrial researchers, due to their vital roles, which they play in the organism and constituting therefore important drug targets. Up to very recently, it was assumed that the basic nitrogen in compound structure is a necessary component to make it active within this receptor system. Such nitrogen interacts in its protonated form with the aspartic acid from the third transmembrane helix (D3x32) forming a hydrogen bond tightly fitting the ligand in the protein binding site. However, there are several recent studies that report strong serotonin receptor affinity also for compounds without a basic moiety in their structures. In the study, we carried out a comprehensive in silico analysis of the low-basicity phenomenon of the selected serotonin receptor ligands. We focused on the crystallized representatives of the proteins of 5-HT_1B, 5-HT_2A, 5-HT_2B, and 5-HT_2C receptors, and examined the problem both from the ligand- and structure-based perspectives. The study was performed for the native proteins, and for D3x32A mutants. The investigation resulted in the determination of nonstandard structural requirements for activity towards serotonin receptors, which can be used in the design of new nonbasic ligands. Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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18 pages, 12644 KiB

Open AccessArticle

In Silico Finding of Key Interaction Mediated α3β4 and α7 Nicotinic Acetylcholine Receptor Ligand Selectivity of Quinuclidine-Triazole Chemotype

by Kuntarat Arunrungvichian, Sumet Chongruchiroj, Jiradanai Sarasamkan, Gerrit Schüürmann, Peter Brust and Opa Vajragupta

Int. J. Mol. Sci. 2020, 21(17), 6189; https://doi.org/10.3390/ijms21176189 - 27 Aug 2020

Cited by 5 | Viewed by 2385

Abstract

The selective binding of six (S)-quinuclidine-triazoles and their (R)-enantiomers to nicotinic acetylcholine receptor (nAChR) subtypes α3β4 and α7, respectively, were analyzed by in silico docking to provide the insight into the molecular basis for the observed stereospecific subtype discrimination. [...] Read more.

The selective binding of six (S)-quinuclidine-triazoles and their (R)-enantiomers to nicotinic acetylcholine receptor (nAChR) subtypes α3β4 and α7, respectively, were analyzed by in silico docking to provide the insight into the molecular basis for the observed stereospecific subtype discrimination. Homology modeling followed by molecular docking and molecular dynamics (MD) simulations revealed that unique amino acid residues in the complementary subunits of the nAChR subtypes are involved in subtype-specific selectivity profiles. In the complementary β4-subunit of the α3β4 nAChR binding pocket, non-conserved AspB173 through a salt bridge was found to be the key determinant for the α3β4 selectivity of the quinuclidine-triazole chemotype, explaining the 47–327-fold affinity of the (S)-enantiomers as compared to their (R)-enantiomer counterparts. Regarding the α7 nAChR subtype, the amino acids promoting a however significantly lower preference for the (R)-enantiomers were the conserved TyrA93, TrpA149 and TrpB55 residues. The non-conserved amino acid residue in the complementary subunit of nAChR subtypes appeared to play a significant role for the nAChR subtype-selective binding, particularly at the heteropentameric subtype, whereas the conserved amino acid residues in both principal and complementary subunits are essential for ligand potency and efficacy. Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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Review

Jump to: Research

21 pages, 2870 KiB

Open AccessReview

Paroxetine—Overview of the Molecular Mechanisms of Action

by Magdalena Kowalska, Jacek Nowaczyk, Łukasz Fijałkowski and Alicja Nowaczyk

Int. J. Mol. Sci. 2021, 22(4), 1662; https://doi.org/10.3390/ijms22041662 - 7 Feb 2021

Cited by 24 | Viewed by 10981

Abstract

In the 21st century and especially during a pandemic, the diagnosis and treatment of depression is an essential part of the daily practice of many family doctors. It mainly affects patients in the age category 15–44 years, regardless of gender. Anxiety disorders are [...] Read more.

In the 21st century and especially during a pandemic, the diagnosis and treatment of depression is an essential part of the daily practice of many family doctors. It mainly affects patients in the age category 15–44 years, regardless of gender. Anxiety disorders are often diagnosed in children and adolescents. Social phobias can account for up to 13% of these diagnoses. Social anxiety manifests itself in fear of negative social assessment and humiliation, which disrupts the quality of social functioning. Treatment of the above-mentioned disorders is based on psychotherapy and pharmacotherapy. Serious side effects or mortality from antidepressant drug overdose are currently rare. Recent studies indicate that paroxetine (ATC code: N06AB), belonging to the selective serotonin reuptake inhibitors, has promising therapeutic effects and is used off-label in children and adolescents. The purpose of this review is to describe the interaction of paroxetine with several molecular targets in various points of view including the basic chemical and pharmaceutical properties. The central point of the review is focused on the pharmacodynamic analysis based on the molecular mechanism of binding paroxetine to various therapeutic targets. Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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24 pages, 2892 KiB

Open AccessReview

Nucleophosmin in Its Interaction with Ligands

by Ilaria Cela, Adele Di Matteo and Luca Federici

Int. J. Mol. Sci. 2020, 21(14), 4885; https://doi.org/10.3390/ijms21144885 - 10 Jul 2020

Cited by 24 | Viewed by 5058

Abstract

Nucleophosmin (NPM1) is a mainly nucleolar protein that shuttles between nucleoli, nucleoplasm and cytoplasm to fulfill its many functions. It is a chaperone of both nucleic acids and proteins and plays a role in cell cycle control, centrosome duplication, ribosome maturation and export, [...] Read more.

Nucleophosmin (NPM1) is a mainly nucleolar protein that shuttles between nucleoli, nucleoplasm and cytoplasm to fulfill its many functions. It is a chaperone of both nucleic acids and proteins and plays a role in cell cycle control, centrosome duplication, ribosome maturation and export, as well as the cellular response to a variety of stress stimuli. NPM1 is a hub protein in nucleoli where it contributes to nucleolar organization through heterotypic and homotypic interactions. Furthermore, several alterations, including overexpression, chromosomal translocations and mutations are present in solid and hematological cancers. Recently, novel germline mutations that cause dyskeratosis congenita have also been described. This review focuses on NPM1 interactions and inhibition. Indeed, the list of NPM1 binding partners is ever-growing and, in recent years, many studies contributed to clarifying the structural basis for NPM1 recognition of both nucleic acids and several proteins. Intriguingly, a number of natural and synthetic ligands that interfere with NPM1 interactions have also been reported. The possible role of NPM1 inhibitors in the treatment of multiple cancers and other pathologies is emerging as a new therapeutic strategy. Full article

(This article belongs to the Special Issue Protein–Ligand Interactions: Deciphering the Molecular Targets and the Mechanisms of Action of Drugs and Natural Compounds 2.0)

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