scholarly journals Development of a Novel Bioinformatics Tool for In Silico Validation of Protein Interactions

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Nicola Barbarini ◽  
Luca Simonelli ◽  
Alberto Azzalin ◽  
Sergio Comincini ◽  
Riccardo Bellazzi
Keyword(s):  
Protein Interactions ◽  
In Silico ◽  
Structural Information ◽  
Sequence Similarity ◽  
Experimental Studies ◽  
Target Protein ◽  
Biological Processes ◽  
Score Threshold ◽  
Validation Strategy

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions. Given a target protein, the approach selects the most likely interactors among the candidates revealed by experimental techniques, but not yet in vivo validated. The sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors. Finally, a score is calculated to suggest the most likely interactors of the target protein. As an example, we searched for GRB2 interactors. We ranked a set of 46 candidate interactors by the presented method. These candidates were then reduced to 21, through a score threshold chosen by means of a cross-validation strategy. Among them, the isoform 1 of MAPK14 was in silico confirmed as a GRB2 interactor. Finally, given a set of already confirmed interactors of GRB2, the accuracy and the precision of the approach were 75% and 86%, respectively. In conclusion, the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors.

scholarly journals Molecular Docking of Olea europaea and Curcuma Longa Compounds as Potential Drug Agents for Targeting Main-Protease of SARS-nCoV2

2020 ◽  
Author(s):  
Rashid Saif ◽  
Muhammad Hassan Raza ◽  
Talha Rehman ◽  
Muhammad Osama Zafar ◽  
Saeeda Zia ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Structural Information ◽  
Curcuma Longa ◽  
Experimental Studies ◽  
Md Simulations ◽  
Binding Energies ◽  
Target Protein ◽  
Target Proteins ◽  
Main Protease

<p>One of the main reasons of rapidly growing cases of COVID-19 pandemic is the unavailability of approved therapeutic agents. Therefore, it is urgently required to find out the best drug/vaccine by all means. Aim of the current study is to test the anti-viral drug potential of many of the available olive and turmeric compounds that can be used as potential inhibitors against one of the target proteins of SARS-nCoV2 named Main protease (Mpro/3clpro). Molecular docking of thirty olive and turmeric compounds with target protein was performed using Molecular Operating Environment (MOE) software to determine the best ligand-protein interaction energies. The structural information of the viral target protein M pro/3CL pro and ligands were taken from PDB and PubChem database respectively. Out of the thirty drug agents, 6 ligands do not follow the Lipinski rule of drug likeliness by violating two or more rules while remaining 24 obey the rules and included for the downstream analysis. Ten ligands from olive and four from turmeric gave the best lowest binding energies, which are Neuzhenide, Rutin, Demethyloleoeuropein, Oleuropein, Luteolin-7-rutinoside, Ligstroside, Verbascoside, Luteolin-7-glucoside, Cosmosin, Curcumin, Tetrehydrocurcumin, Luteolin-4'-o-glucoside, Demethoxycurcumin and Bidemethoxycurcumin with docking scores of -10.91, -9.49, -9.48, -9.21, -9.18, -8.72, -8.51, -7.68, -7.67, -7.65, -7.42, -7.25, -7.02 and - 6.77 kcal/mol respectively. Our predictions suggest that these ligands have the potential inhibitory effects of M pro of SARS-nCoV2, so, these herbal plants would be helpful in harnessing COVID-19 infection as home remedy with no serious known side effects. Further, in-silico MD simulations and in-vivo experimental studies are needed to validate the inhibitory properties of these compounds against the current and other target proteins in SARS-nCoV2.<br></p>

scholarly journals Molecular Docking of Olea europaea and Curcuma Longa Compounds as Potential Drug Agents for Targeting Main-Protease of SARS-nCoV2

2020 ◽  
Author(s):  
Rashid Saif ◽  
Muhammad Hassan Raza ◽  
Talha Rehman ◽  
Muhammad Osama Zafar ◽  
Saeeda Zia ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Structural Information ◽  
Curcuma Longa ◽  
Experimental Studies ◽  
Md Simulations ◽  
Binding Energies ◽  
Target Protein ◽  
Target Proteins ◽  
Main Protease

<p>One of the main reasons of rapidly growing cases of COVID-19 pandemic is the unavailability of approved therapeutic agents. Therefore, it is urgently required to find out the best drug/vaccine by all means. Aim of the current study is to test the anti-viral drug potential of many of the available olive and turmeric compounds that can be used as potential inhibitors against one of the target proteins of SARS-nCoV2 named Main protease (Mpro/3clpro). Molecular docking of thirty olive and turmeric compounds with target protein was performed using Molecular Operating Environment (MOE) software to determine the best ligand-protein interaction energies. The structural information of the viral target protein M pro/3CL pro and ligands were taken from PDB and PubChem database respectively. Out of the thirty drug agents, 6 ligands do not follow the Lipinski rule of drug likeliness by violating two or more rules while remaining 24 obey the rules and included for the downstream analysis. Ten ligands from olive and four from turmeric gave the best lowest binding energies, which are Neuzhenide, Rutin, Demethyloleoeuropein, Oleuropein, Luteolin-7-rutinoside, Ligstroside, Verbascoside, Luteolin-7-glucoside, Cosmosin, Curcumin, Tetrehydrocurcumin, Luteolin-4'-o-glucoside, Demethoxycurcumin and Bidemethoxycurcumin with docking scores of -10.91, -9.49, -9.48, -9.21, -9.18, -8.72, -8.51, -7.68, -7.67, -7.65, -7.42, -7.25, -7.02 and - 6.77 kcal/mol respectively. Our predictions suggest that these ligands have the potential inhibitory effects of M pro of SARS-nCoV2, so, these herbal plants would be helpful in harnessing COVID-19 infection as home remedy with no serious known side effects. Further, in-silico MD simulations and in-vivo experimental studies are needed to validate the inhibitory properties of these compounds against the current and other target proteins in SARS-nCoV2.<br></p>

scholarly journals In Silico Investigation of Mitragynine and 7-Hydroxymitragynine Metabolism

2019 ◽  
Author(s):  
Taweetham Limpanuparb ◽  
Rattha Noorat ◽  
Yuthana Tantirungrotechai
Keyword(s):  
Gas Phase ◽  
In Silico ◽  
Metabolic Pathways ◽  
Experimental Studies ◽  
Medicinal Uses ◽  
Detection Techniques ◽  
Mass Spectrom ◽  
Gibbs Energies ◽  
In Silico Study

Abstract Objective: Mitragynine is the main active compound of Mitragyna speciose (Kratom in Thai). The understanding of mitragynine derivative metabolism in human body is required to develop effective detection techniques in case of drug abuse or establish an appropriate dosage in case of medicinal uses. This in silico study is based upon in vivo results in rat and human by Philipp et al. (J. Mass Spectrom., 2009, 44, 1249.) Results: The gas-phase structures of mitragynine, 7-hydroxymitragynine and their metabolites were obtained by quantum chemical method at B3LYP/6-311++G(d,p) level. Results in terms of standard Gibbs energies of reaction for all metabolic pathways are reported with solvation energy from SMD model. We found that 7-hydroxy substitution leads to changes in reactivity in comparison to mitragynine: position 17 is more reactive towards demethylation and conjugation to a glucuronide and position 9 is less reactive towards conjugation to a glucuronide. Despite the changes, position 9 is the most reactive for demethylation and position 17 is the most reactive for conjugation to a glucuronide for both mitragynine and 7-hydroxymitragynine. Our results suggest that 7-hydroxy substitution could lead to different metabolic pathways and raise an important question for further experimental studies of this more potent derivative.

scholarly journals In Silico Drug Repositioning Against Human NRP1 to Block SARS-CoV-2 Host Entry

2020 ◽  
Author(s):  
Seref Gul
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Structural Information ◽  
Drug Repositioning ◽  
Experimental Studies ◽  
Md Simulations ◽  
Drug Binding ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
S Protein

Despite COVID-19 turned into a pandemic, no approved drug for the treatment or globally available vaccine is out yet. In such a global emergency, drug repurposing approach that bypasses a costly and long-time demanding drug discovery process is an effective way in search of finding drugs for the COVID-19 treatment. Recent studies showed that SARS-CoV-2 uses neuropilin-1 (NRP1) for host entry. Here I took advantage of structural information of the NRP1 in complex with C-terminal of spike (S) protein of SARS-CoV-2 to identify drugs that may inhibit NRP1 and S protein interaction. U.S. Food and Drug Administration (FDA) approved drugs were screened using docking simulations. Among top drugs, well-tolerated drugs were selected for further analysis. Molecular dynamics (MD) simulations of drugs-NRP1 complexes were run for 100 ns to assess the persistency of binding. MM/GBSA calculations from MD simulations showed that eltrombopag, glimepiride, sitagliptin, dutasteride, and ergotamine stably and strongly bind to NRP1. In silico Alanine scanning analysis revealed that Tyr<sup>297</sup>, Trp<sup>301</sup>, and Tyr<sup>353</sup> amino acids of NRP1 are critical for drug binding. Validating the effect of drugs analyzed in this paper by experimental studies and clinical trials will expedite the drug discovery process for COVID-19.

scholarly journals An In-Silico Investigation of Menthol Metabolism

2019 ◽  
Author(s):  
Taweetham Limpanuparb ◽  
Wanutcha Lorpaiboon ◽  
Kridtin Chinsukserm
Keyword(s):  
In Silico ◽  
Metabolic Pathways ◽  
Glucuronic Acid ◽  
Experimental Studies ◽  
Gibbs Energies ◽  
Therapeutic Benefits ◽  
Solvation Energies ◽  
Safety Design ◽  
Combining Information

ABSTRACTPrevalence of mentholated products for consumption has brought great importance to studies on menthol’s metabolic pathways to ensure safety, design more potent derivatives, and identify therapeutic benefits. Proposed pathways of (-)-menthol metabolism based on metabolites found experimentally in previous works by Yamaguchi, Caldwell & Farmer, Madyastha & Srivatsan and Hiki et al. were not in agreement. This in silico approach is based on the three in vivo studies and aims to resolve the discrepancies. Reactions in the pathways are conjugation with glucuronic acid/sulfate, oxidation to alcohol, aldehyde & carboxylic acid, and formation of a four-membered/five-membered ring. Gas-phase structures, standard Gibbs energies and SMD solvation energies at B3LYP/6-311++G(d,p) level were obtained for 102 compounds in the pathways. This study provides a more complete picture of menthol metabolism by combining information from three experimental studies and filling missing links in previously published pathways.

Fluorescence resonance energy transfer in revealing protein–protein interactions in living cells

2021 ◽  
Author(s):  
Sukesh R. Bhaumik
Keyword(s):  
Single Cell ◽  
Protein Interactions ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Living Cells ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Functional Mechanisms

Genes are expressed to proteins for a wide variety of fundamental biological processes at the cellular and organismal levels. However, a protein rarely functions alone, but rather acts through interactions with other proteins to maintain normal cellular and organismal functions. Therefore, it is important to analyze the protein–protein interactions to determine functional mechanisms of proteins, which can also guide to develop therapeutic targets for treatment of diseases caused by altered protein–protein interactions leading to cellular/organismal dysfunctions. There is a large number of methodologies to study protein interactions in vitro, in vivo and in silico, which led to the development of many protein interaction databases, and thus, have enriched our knowledge about protein–protein interactions and functions. However, many of these interactions were identified in vitro, but need to be verified/validated in living cells. Furthermore, it is unclear whether these interactions are direct or mediated via other proteins. Moreover, these interactions are representative of cell- and time-average, but not a single cell in real time. Therefore, it is crucial to detect direct protein–protein interactions in a single cell during biological processes in vivo, towards understanding the functional mechanisms of proteins in living cells. Importantly, a fluorescence resonance energy transfer (FRET)-based methodology has emerged as a powerful technique to decipher direct protein–protein interactions at a single cell resolution in living cells, which is briefly described in a limited available space in this mini-review.

scholarly journals A Comprehensive In Silico Analysis of the Functional and Structural Impact of Nonsynonymous SNPs in the ABCA1 Transporter Gene

Cholesterol ◽  
2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Francisco R. Marín-Martín ◽  
Cristina Soler-Rivas ◽  
Roberto Martín-Hernández ◽  
Arantxa Rodriguez-Casado
Keyword(s):  
In Silico ◽  
Rare Variants ◽  
Experimental Studies ◽  
In Silico Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Experimental Approaches ◽  
And Function

Disease phenotypes and defects in function can be traced to nonsynonymous single nucleotide polymorphisms (nsSNPs), which are important indicators of action sites and effective potential therapeutic approaches. Identification of deleterious nsSNPs is crucial to characterize the genetic basis of diseases, assess individual susceptibility to disease, determinate molecular and therapeutic targets, and predict clinical phenotypes. In this study using PolyPhen2 and MutPred in silico algorithms, we analyzed the genetic variations that can alter the expression and function of the ABCA1 gene that causes the allelic disorders familial hypoalphalipoproteinemia and Tangier disease. Predictions were validated with published results from in vitro, in vivo, and human studies. Out of a total of 233 nsSNPs, 80 (34.33%) were found deleterious by both methods. Among these 80 deleterious nsSNPs found, 29 (12.44%) rare variants resulted highly deleterious with a probability >0.8. We have observed that mostly variants with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, the controversial results of experimental approaches correspond to nsSNPs predicted as neutral by both methods, or contradictory predictions are obtained for them. A total of seventeen nsSNPs were predicted as deleterious by PolyPhen2, which resulted neutral by MutPred. Otherwise, forty two nsSNPs were predicted as deleterious by MutPred, which resulted neutral by PolyPhen2.

scholarly journals Flexibility and mobility of SARS-CoV-2-related protein structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rudolf A. Römer ◽  
Navodya S. Römer ◽  
A. Katrine Wallis
Keyword(s):  
In Silico ◽  
Drug Targets ◽  
Large Scale ◽  
Structural Information ◽  
Protein Structures ◽  
Antiviral Drug ◽  
Docking Studies ◽  
Related Protein

AbstractThe worldwide CoVid-19 pandemic has led to an unprecedented push across the whole of the scientific community to develop a potent antiviral drug and vaccine as soon as possible. Existing academic, governmental and industrial institutions and companies have engaged in large-scale screening of existing drugs, in vitro, in vivo and in silico. Here, we are using in silico modelling of possible SARS-CoV-2 drug targets, as deposited on the Protein Databank (PDB), and ascertain their dynamics, flexibility and rigidity. For example, for the SARS-CoV-2 spike protein—using its complete homo-trimer configuration with 2905 residues—our method identifies a large-scale opening and closing of the S1 subunit through movement of the S$${}^\text{B}$$ B domain. We compute the full structural information of this process, allowing for docking studies with possible drug structures. In a dedicated database, we present similarly detailed results for the further, nearly 300, thus far resolved SARS-CoV-2-related protein structures in the PDB.

scholarly journals Target-templated de novo design of macrocyclic d-/l-peptides: discovery of drug-like inhibitors of PD-1

2021 ◽  
Vol 12 (14) ◽  
pp. 5164-5170
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Macarena Sánchez-Navarro ◽  
Jesús García ◽  
...  
Keyword(s):  
Specific Surface ◽  
Protein Interactions ◽  
In Silico ◽  
Cyclic Peptides ◽  
De Novo ◽  
De Novo Design ◽  
Target Protein ◽  
Surface Patch ◽  
Protein Protein Interactions

In silico design of heterochiral cyclic peptides that bind to a specific surface patch on the target protein (PD-1, in this case) and disrupt protein–protein interactions.

scholarly journals In Silico Predictions of Endocrine Disruptors Properties

Endocrinology ◽  
2019 ◽  
Vol 160 (11) ◽  
pp. 2709-2716 ◽  
Author(s):  
Melanie Schneider ◽  
Jean-Luc Pons ◽  
Gilles Labesse ◽  
William Bourguet
Keyword(s):  
Biological Activity ◽  
Endocrine Disruptors ◽  
In Silico ◽  
Chemical Structure ◽  
Structural Information ◽  
Broad Class ◽  
Computer Assisted ◽  
Screening Methods

Abstract Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure–activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs’ targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

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