scholarly journals Genomic analysis of human polymorphisms affecting drug-protein interactions

2017 ◽  
Author(s):  
Oriol Pich i Rosello ◽  
Anna V. Vlasova ◽  
Polina A. Shichkova ◽  
Yuri Markov ◽  
Peter K. Vlasov ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Low Frequency ◽  
Genomic Analysis ◽  
Binding Affinities ◽  
Using Data ◽  
Individual Drug Response ◽  
Approved Drugs ◽  
The Impact ◽  
Fda Approved Drugs

Human genetic variability is thought to account for a substantial fraction of individual biochemical characteristics – in biomedical sense, of individual drug response. However, only a handful of human genetic variants have been linked to medication outcomes. Here, we combine data on drug-protein interactions and human genome sequences to assess the impact of human variation on their binding affinity. Using data from the complexes of FDA-drugs and drug-like compounds, we predict SNPs substantially affecting the protein-ligand binding affinities. We estimate that an average individual carries ~6 SNPs affecting ~5 different FDA-approved drugs from among all of the approved compounds. SNPs affecting drug-protein binding affinity have low frequency in the population indicating that the genetic component for many ADEs may be highly personalized with each individual carrying a unique set of relevant SNPs. The reduction of ADEs, therefore, may primarily rely on the application of computational genome analysis in the clinic rather than the experimental study of common SNPs.

scholarly journals Climbing up and down binding landscapes: a high-throughput study of mutational effects in homologous protein-protein complexes

2020 ◽  
Author(s):  
Michael Heyne ◽  
Jason Shirian ◽  
Itay Cohen ◽  
Yoav Peleg ◽  
Evette S. Radisky ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Enzyme Inhibitor ◽  
Binding Free Energy ◽  
Hot Spot ◽  
Cold Spot ◽  
Binding Affinities ◽  
High Affinity ◽  
Binding Interface ◽  
Biological Complexes

AbstractEach protein-protein interaction (PPI) has evolved to possess binding affinity that is compatible with its cellular function. As such, cognate enzyme/inhibitor interactions frequently exhibit very high binding affinities, while structurally similar non-cognate PPIs possess substantially weaker binding affinities. To understand how slight differences in sequence and structure could lead to drastic changes in PPI binding free energy (ΔΔGbind), we study three homologous PPIs that span nine orders of magnitude in binding affinity and involve a serine protease interacting with an inhibitor BPTI. Using state-of-the-art methodology that combines protein randomization and affinity sorting coupled to next-generation sequencing and data normalization, we report quantitative binding landscapes consisting of ΔΔGbind values for the three PPIs, gleaned from tens of thousands of single and double mutations in the BPTI binding interface. We demonstrate that the three homologous PPIs possess drastically different binding landscapes and lie at different points in respect to the landscape maximum. Furthermore, the three PPIs demonstrate distinct patterns of coupling energies between two simultaneous mutations that depend not only on positions involved but also on the nature of the mutation. Interestingly, we find that in all three PPIs positive epistasis is frequently observed at hot-spot positions where mutations lead to loss of high affinity, while conversely negative epistasis is observed at cold-spot positions, where mutations lead to affinity enhancement. The new insights on PPI evolution revealed in this study will be invaluable in understanding evolution of other biological complexes and can greatly facilitate design of novel high-affinity protein inhibitors.SignificanceProtein-protein interactions (PPIs) have evolved to display binding affinities that can support their function. As such, cognate and non-cognate PPIs could be highly similar structurally but exhibit huge differences in binding affinities. To understand this phenomenon, we studied the effect of tens of thousands of single and double mutations on binding affinity of three homologous protease-inhibitor complexes. We show that binding landscapes of the three complexes are strikingly different and depend on the PPI evolutionary optimality. We observe different patterns of couplings between mutations for the three PPIs with negative and positive epistasis appearing most frequently at hot-spot and cold-spot positions, respectively. The evolutionary trends observed here are likely to be universal to all biological complexes in the cell.

scholarly journals Quantitative yeast-yeast two hybrid for discovery and binding affinity estimation of protein-protein interactions

2020 ◽  
Author(s):  
Kaitlyn Bacon ◽  
Abigail Blain ◽  
John Bowen ◽  
Matthew Burroughs ◽  
Nikki McArthur ◽  
...  
Keyword(s):  
Binding Affinity ◽  
Protein Interactions ◽  
Quantitative Estimation ◽  
Surface Display ◽  
Combinatorial Libraries ◽  
Yeast Surface Display ◽  
Binding Affinities ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Two Hybrid

AbstractQuantifying the binding affinity of protein-protein interactions is important for elucidating connections within biochemical signaling pathways, as well as characterization of binding proteins isolated from combinatorial libraries. We describe a quantitative yeast-yeast two hybrid (qYY2H) system that not only enables discovery of specific protein-protein interactions, but also efficient, quantitative estimation of their binding affinities (KD). In qYY2H, the bait and prey proteins are expressed as yeast cell surface fusions using yeast surface display. We developed a semi-empirical framework for estimating the KD of monovalent bait-prey interactions, using measurements of the apparent KD of yeast-yeast binding, which is mediated by multivalent interactions between yeast-displayed bait and prey. Using qYY2H, we identified interaction partners of SMAD3 and the tandem WW domains of YAP from a cDNA library and characterized their binding affinities. Finally, we showed that qYY2H could also quantitatively evaluate binding interactions mediated by post-translational modifications on the bait protein.

scholarly journals In Silico Screening of Potential Phytocompounds from Several Herbs against SARS-CoV-2 Indian Delta Variant B.1.617.2 to Inhibit the Spike Glycoprotein Trimer

2022 ◽  
Vol 12 (2) ◽  
pp. 665
Author(s):  
Muruganantham Bharathi ◽  
Bhagavathi Sundaram Sivamaruthi ◽  
Periyanaina Kesika ◽  
Subramanian Thangaleela ◽  
Chaiyavat Chaiyasut
Keyword(s):  
The United States ◽  
Inhibitory Effect ◽  
Binding Affinities ◽  
Standard Drug ◽  
Houttuynia Cordata ◽  
Future Drug ◽  
Strong Inhibitory Effect ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Shed Light

In October 2020, the SARS-CoV-2 B.1.617 lineage was discovered in India. It has since become a prominent variant in several Indian regions and 156 countries, including the United States of America. The lineage B.1.617.2 is termed the delta variant, harboring diverse spike mutations in the N-terminal domain (NTD) and the receptor-binding domain (RBD), which may heighten its immune evasion potentiality and cause it to be more transmissible than other variants. As a result, it has sparked substantial scientific investigation into the development of effective vaccinations and anti-viral drugs. Several efforts have been made to examine ancient medicinal herbs known for their health benefits and immune-boosting action against SARS-CoV-2, including repurposing existing FDA-approved anti-viral drugs. No efficient anti-viral drugs are available against the SARS-CoV-2 Indian delta variant B.1.617.2. In this study, efforts were made to shed light on the potential of 603 phytocompounds from 22 plant species to inhibit the Indian delta variant B.1.617.2. We also compared these compounds with the standard drug ceftriaxone, which was already suggested as a beneficial drug in COVID-19 treatment; these compounds were compared with other FDA-approved drugs: remdesivir, chloroquine, hydroxy-chloroquine, lopinavir, and ritonavir. From the analysis, the identified phytocompounds acteoside (−7.3 kcal/mol) and verbascoside (−7.1 kcal/mol), from the plants Clerodendrum serratum and Houttuynia cordata, evidenced a strong inhibitory effect against the mutated NTD (MT-NTD). In addition, the phytocompounds kanzonol V (−6.8 kcal/mol), progeldanamycin (−6.4 kcal/mol), and rhodoxanthin (−7.5 kcal/mol), from the plant Houttuynia cordata, manifested significant prohibition against RBD. Nevertheless, the standard drug, ceftriaxone, signals less inhibitory effect against MT-NTD and RBD with binding affinities of −6.3 kcal/mol and −6.5 kcal/mol, respectively. In this study, we also emphasized the pharmacological properties of the plants, which contain the screened phytocompounds. Our research could be used as a lead for future drug design to develop anti-viral drugs, as well as for preening the Siddha formulation to control the Indian delta variant B.1.617.2 and other future SARS-CoV-2 variants.

Solar Wind induced waves in the skies of Mars: Ionospheric compression, energization, and escape resulting from the impact of ultra-low frequency magnetosonic waves generated upstream of the Martian bow shock

2020 ◽  
Author(s):  
Glyn Collinson ◽  
Lynn Wilson III ◽  
Nick Omidi ◽  
David Sibeck ◽  
Jared Espley ◽  
...  
Keyword(s):  
Solar Wind ◽  
Low Frequency ◽  
Ulf Waves ◽  
Mars Atmosphere ◽  
Transient Phenomena ◽  
Mars Express ◽  
Linearly Polarized ◽  
Using Data ◽  
Atmospheric Loss ◽  
The Impact

<p>Using data from the NASA Mars Atmosphere and Voltatile EvolutioN (MAVEN) and ESA Mars Express spacecraft, we show that transient phenomena in the foreshock and solar wind can directly inject energy into the ionosphere of Mars. We demonstrate that the impact of compressive Ultra-Low Frequency (ULF) waves in the solar wind on the induced magnetospheres drive compressional, linearly polarized, magnetosonic ULF waves in the ionosphere, and a localized electromagnetic "ringing" at the local proton gyrofrequency. The pulsations heat and energize ionospheric plasmas. A preliminary survey of events shows that no special upstream conditions are required in the interplanetary magnetic field or solar wind. Elevated ion densities and temperatures in the solar wind near to Mars are consistent with the presence of an additional population of Martian ions, leading to ion-ion instablities, associated wave-particle interactions, and heating of the solar wind. The phenomenon was found to be seasonal, occurring when Mars is near perihelion. Finally, we present simultaneous multipoint observations of the phenomenon, with the Mars Express observing the waves upstream, and MAVEN observing the response in the ionosphere. When these new observations are combined with decades of previous studies, they collectively provide strong evidence for a previously undemonstrated atmospheric loss process at unmagnetized planets: ionospheric escape driven by the direct impact of transient phenomena from the foreshock and solar wind.</p>

scholarly journals Multi-Targeted Approaches and Drug Repurposing Reveal Possible SARS-CoV-2 Inhibitors

Vaccines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 24
Author(s):  
Khalid Mashay Alanazi ◽  
Mohammad Abul Farah ◽  
Yan-Yan Hor
Keyword(s):  
Experimental Validation ◽  
Computational Analysis ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Binding Affinities ◽  
Normal Model ◽  
Inhibitory Potential ◽  
Us Fda ◽  
Approved Drugs ◽  
Fda Approved Drugs

The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.

In Silico Identification of Active Phytochemicals against COVID-19 by Targeting the SARS-CoV-2 Spike Glycoprotein Through Molecular Docking: A Drug Repurposing Approach

2021 ◽  
Vol 8 (2) ◽  
pp. 72-87
Author(s):  
Rathan Kumar
Keyword(s):  
Molecular Docking ◽  
Antiviral Treatment ◽  
Drug Repurposing ◽  
Modern Human ◽  
Binding Affinities ◽  
In Silico Identification ◽  
Adme Properties ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Natural Inhibitors

The spread of coronavirus disease (COVID-19) has become one of the most significant pandemics in modern human history, affecting more than 70 million people worldwide. Currently, only a few fda-approved drugs have suggested fighting the infection, in the absence of a specific antiviral treatment. Thus, repurposing the presently available drugs or using plant-based bioactive compounds can be the fastest possible solution. In this study, the computational methodology of molecular docking techniques was performed to screen and identify the viable potent inhibitors against the SARS-CoV-2 spike protein from a library of 200 active phytochemicals, based on their highest binding affinity towards the target protein. Later, the binding affinities of these phytochemicals were compared with that of the fda-approved drug fluvoxamine, which is currently in use against the mild COVID-19 patients. Out of these, 86 phytochemicals that exhibited better binding energy of value ≤-7.00kcal/mol, is selected for adme (absorption, distribution, metabolism, and excretion) analysis and drug likeliness studies to check the feasibility of these compounds. Wherein, 79 out of 86 phytochemicals showed a better theoretical affinity with sufficiently bearable adme properties. Thus, they can be the lead molecule for further investigation and validation processes towards developing natural inhibitors against the SARS-CoV-2 virus.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein–protein complexes

2020 ◽  
Author(s):  
Pep Amengual-Rigo ◽  
Juan Fernández-Recio ◽  
Victor Guallar
Keyword(s):  
Open Source ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Selection Procedure ◽  
Supplementary Information ◽  
Computational Time ◽  
Contact Potential ◽  
Three Body ◽  
The Impact

Abstract Motivation Single protein residue mutations may reshape the binding affinity of protein–protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein–protein complexes trained on interactome data. Results Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein–protein mutations using UEP, a fast and reliable open-source code. Availability and implementation UEP algorithm can be found at: https://github.com/pepamengual/UEP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Simeprevir and Eltrombopag as Potential Inhibitors of SARS-CoV2 Proteases: A Molecular Docking and Virtual Screening Approach to Combat COVID-19

2020 ◽  
Author(s):  
Ananta Swargiary ◽  
AKALESH Verma ◽  
Manita Daimari ◽  
Mritunjoy Kumar Roy
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Binding Affinities ◽  
Screening Approach ◽  
Approved Drugs ◽  
Potential Inhibitors ◽  
Fda Approved Drugs ◽  
Virtual Screening Approach

The present study investigates the binding affinities of 61 FDA approved drugs against two key proteases of SARS-COV2, 3-chymotrypsin-like protease and papain-like protease. We also investigates the ADMET properties of the top 10 besting binding drugs to understand the drug likeness property.

scholarly journals MONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins

2019 ◽  
Author(s):  
Shuya Li ◽  
Fangping Wan ◽  
Hantao Shu ◽  
Tao Jiang ◽  
Dan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Prediction Models ◽  
Benchmark Dataset ◽  
Binding Affinities ◽  
Multi Objective ◽  
Interaction Sites ◽  
Non Covalent Interactions ◽  
Covalent Interactions

AbstractComputational approaches for inferring the mechanisms of compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, although a number of deep learning based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins through neural attentions, they still lack a systematic evaluation on the interpretability of the identified local features. In addition, in these previous approaches, the exact matchings between interaction sites from compounds and proteins, which are generally important for understanding drug mechanisms of action, still remain unknown. Here, we compiled the first benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs, and used it to systematically evaluate the interpretability of neural attentions in existing prediction models. We developed a multi-objective neural network, called MONN, to predict both non-covalent interactions and binding affinity for a given compound-protein pair. MONN uses convolution neural networks on molecular graphs of compounds and primary sequences of proteins to effectively capture the intrinsic features from both inputs, and also takes advantage of the predicted non-covalent interactions to further boost the accuracy of binding affinity prediction. Comprehensive evaluation demonstrated that while the previous neural attention based approaches fail to exhibit satisfactory interpretability results without extra supervision, MONN can successfully predict non-covalent interactions on our benchmark dataset as well as another independent dataset derived from the Protein Data Bank (PDB). Moreover, MONN can outperform other state-of-the-art methods in predicting compound-protein binding affinities. In addition, the pairwise interactions predicted by MONN displayed compatible and accordant patterns in chemical properties, which provided another evidence to support the strong predictive power of MONN. These results suggested that MONN can offer a powerful tool in predicting binding affinities of compound-protein pairs and also provide useful insights into understanding the molecular mechanisms of compound-protein interactions, which thus can greatly advance the drug discovery process. The source code of the MONN model and the dataset creation process can be downloaded from https://github.com/lishuya17/MONN.

scholarly journals Characterizing RNA-binding Ligands on Structures, Chemical Information, Binding Affinity and Drug-likeness

2022 ◽  
Author(s):  
Cong Fan ◽  
Xin Wang ◽  
Tianze Wang ◽  
Huiying Zhao
Keyword(s):  
Binding Affinity ◽  
Drug Targets ◽  
Rna Binding ◽  
Chemical Properties ◽  
P Value ◽  
Chemical Information ◽  
Ligand Interactions ◽  
Approved Drugs ◽  
2D And 3D ◽  
Fda Approved Drugs

Recent studies suggest RNAs playing essential roles in many cell activities and act as promising drug targets. However, limited development has been achieved in detecting RNA-ligand interactions. To guide the discovery of RNA-binding ligands, it is necessary to characterize them comprehensively. We established a database, RNALID that collects RNA-ligand interactions validated by low-throughput experiment. RNALID contains 358 RNA-ligand interactions. Comparing to other databases, 94.5% of ligands in RNALID are completely or partially novel collections, and 51.78% have novel two-dimensional (2D) structures. The ligand structure analysis indicated that multivalent ligands (MV), ligands binding with cellular mRNA (mRNA), ligands binding with RNA from virus (vRNA) and ligands binding with RNA containing repetitive sequence (rep RNA) are more structurally conserved in both 2D and 3D structures than other ligand types. Binding affinity analysis revealed that interactions between ligands and rep RNA were significantly stronger (two-tailed MW-U test P-value = 0.012) than the interactions between ligands and non-rep RNAs; the interactions between ligands and vRNA were significantly stronger (two-tailed MW-U test P-value = 0.012) than those between ligands and mRNA. Drug-likeness analysis indicated that small molecule (SM) ligands binding with non-rep RNA or vRNA may have higher probability to be drugs than other types of ligands. Comparing ligands in RNALID to FDA-approved drugs and ligands without bioactivity indicated that RNA-binding ligands are different from them in chemical properties, structural properties and drug-likeness. Thus, characterizing the RNA-ligand interactions in RNALID in multiple respects provides new insights into discovering and designing druggable ligands binding with RNA.

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