scholarly journals Computational Analysis of Dynamic Allostery and Control in the three SARS-CoV-2 non-structural proteins

2020 ◽  
Author(s):  
Igors Dubanevics ◽  
Charles Heaton ◽  
Carlos Riechmann ◽  
Tom C.B. McLeish
Keyword(s):  
Active Site ◽  
Drug Targets ◽  
Competitive Inhibition ◽  
Network Models ◽  
Vital Role ◽  
Research Field ◽  
Structural Proteins ◽  
Dynamical Structure ◽  
Double Mutation ◽  
Candidate Regions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the COVID-19 pandemic, has no vaccine or antiviral drugs available to the public, at the time of writing. The virus’ non-structural proteins are promising drug targets because of their vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which covalently and competitively bind the active site of the non-structural proteins, but little has been done to address regions other than the active site, i.e. for non-competitive inhibition. Here we extend previous work on the SARS-CoV-2 Mpro (nsp5) to three other SARS-CoV-2 proteins: host shutoff factor (nsp1), papain-like protease (nsp3, also known as PLpro) and RNA-dependent RNA-polymerase (nsp12, also known as RdRp) in complex with nsp7 and nsp8 cofactors. Using open-source software (DDPT) to construct Elastic Network Models (ENM) of the chosen proteins we analyse their fluctuation dynamics and thermodynamics, as well as using this protein family to study convergence and robustness of the ENM. Exhaustive 2-point mutational scans of the ENM and their effect on fluctuation free energies suggest several new candidate regions, distant from the active site, for control of the proteins’ function, which may assist the drug development based on the current small molecule binding screens. The results also provide new insights, including non-additive effects of double-mutation or inhibition, into the active biophysical research field of protein fluctuation allostery and its underpinning dynamical structure.

scholarly journals Computational analysis of dynamic allostery and control in the SARS-CoV-2 main protease

2021 ◽  
Vol 18 (174) ◽  
pp. 20200591
Author(s):  
Igors Dubanevics ◽  
Tom C. B. McLeish
Keyword(s):  
Active Site ◽  
Active Sites ◽  
Competitive Inhibition ◽  
Vital Role ◽  
Research Field ◽  
Dynamical Structure ◽  
Elastic Network Model ◽  
Main Protease ◽  
Candidate Regions ◽  
Novel Coronavirus

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has no publicly available vaccine or antiviral drugs at the time of writing. An attractive coronavirus drug target is the main protease (M pro , also known as 3CL pro ) because of its vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which bind and block the active site of the main protease, but little has been done to address potential non-competitive inhibition, targeting regions other than the active site, partly because the fundamental biophysics of such allosteric control is still poorly understood. In this work, we construct an elastic network model (ENM) of the SARS-CoV-2 M pro homodimer protein and analyse its dynamics and thermodynamics. We found a rich and heterogeneous dynamical structure, including allosterically correlated motions between the homodimeric protease's active sites. Exhaustive 1-point and 2-point mutation scans of the ENM and their effect on fluctuation free energies confirm previously experimentally identified bioactive residues, but also suggest several new candidate regions that are distant from the active site, yet control the protease function. Our results suggest new dynamically driven control regions as possible candidates for non-competitive inhibiting binding sites in the protease, which may assist the development of current fragment-based binding screens. The results also provide new insights into the active biophysical research field of protein fluctuation allostery and its underpinning dynamical structure.

scholarly journals Computational Analysis of Dynamic Allostery and Control in the SARS-CoV-2 Main Protease

2020 ◽  
Author(s):  
Igors Dubanevics ◽  
Tom C.B. McLeish
Keyword(s):  
Active Site ◽  
Active Sites ◽  
Competitive Inhibition ◽  
Vital Role ◽  
Dynamical Structure ◽  
Elastic Network Model ◽  
Correlated Motions ◽  
Main Protease ◽  
Candidate Regions ◽  
Novel Coronavirus

AbstractThe COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 has generated a global pandemic and no vaccine or antiviral drugs exist at the moment of writing. An attractive coronavirus drug target is the main protease (Mpro, also known as 3CLpro) because of its vital role in the viral cycle. A significant body of work has been focused on finding inhibitors which bind and block the active site of the main protease, but little has been done to address potential non-competitive inhibition which targets regions beyond the active site, partly because the fundamental biophysics of such allosteric control is still poorly understood. In this work, we construct an Elastic Network Model (ENM) of the SARS-CoV-2 Mpro homodimer protein and analyse the dynamics and thermodynamics of the main protease’s ENM. We found a rich and heterogeneous dynamical structure in the correlated motions, including allosterically correlated motions between the homodimeric protease’s active sites. Exhaustive 1-point and 2-point mutation scans of the ENM and their effect on fluctuation free energies confirm previously experimentally identified bioactive residues, but also suggest several new candidate regions that are distant from the active site for control of the protease function. Our results suggest new dynamically-driven control regions as possible candidates for non-competitive inhibiting binding sites in the protease, which may assist the development of current fragmentbased binding screens. The results also provide new insight into the protein physics of fluctuation allostery and its underpinning dynamical structure.

Active site‐based analysis of structural proteins for drug targets in different human Coronaviruses

2021 ◽  
Author(s):  
Khadijeh Ahmadi ◽  
Farnaz Zahedifard ◽  
Ladan Mafakher ◽  
Mohammad Ali Einakian ◽  
Tayebeh Ghaedi ◽  
...  
Keyword(s):  
Active Site ◽  
Drug Targets ◽  
Structural Proteins ◽  
Human Coronaviruses

Structure-Based Design, Synthesis, Biological Evaluation and Molecular Docking Study of 4-Hydroxy-N'-methylenebenzohydrazide Derivatives Acting as Tyrosinase Inhibitors with Potentiate Anti-Melanogenesis Activities

2020 ◽  
Vol 16 (7) ◽  
pp. 892-902 ◽  
Author(s):  
Aida Iraji ◽  
Mahsima Khoshneviszadeh ◽  
Pegah Bakhshizadeh ◽  
Najmeh Edraki ◽  
Mehdi Khoshneviszadeh
Keyword(s):  
Molecular Docking ◽  
Active Site ◽  
Competitive Inhibition ◽  
Docking Study ◽  
Biological Evaluation ◽  
Primary Response ◽  
Ratio Method ◽  
Molecular Docking Study ◽  
Metal Chelating

Background: Melanogenesis is a process of melanin synthesis, which is a primary response for the pigmentation of human skin. Tyrosinase is a key enzyme, which catalyzes a ratelimiting step of the melanin formation. Natural products have shown potent inhibitors, but some of these possess toxicity. Numerous synthetic inhibitors have been developed in recent years may lead to the potent anti– tyrosinase agents. Objective: A number of 4-hydroxy-N'-methylenebenzohydrazide analogues with related structure to chalcone and tyrosine were constructed with various substituents at the benzyl ring of the molecule and evaluate as a tyrosinase inhibitor. In addition, computational analysis and metal chelating potential have been evaluated. Methods: Design and synthesized compounds were evaluated for activity against mushroom tyrosinase. The metal chelating capacity of the potent compound was examined using the mole ratio method. Molecular docking of the synthesized compounds was carried out into the tyrosine active site. Results: Novel 4-hydroxy-N'-methylenebenzohydrazide derivatives were synthesized. The two compounds 4c and 4g showed an IC50 near the positive control, led to a drastic inhibition of tyrosinase. Confirming in vitro results were performed via the molecular docking analysis demonstrating hydrogen bound interactions of potent compounds with histatidine-Cu+2 residues with in the active site. Kinetic study of compound 4g showed competitive inhibition towards tyrosinase. Metal chelating assay indicates the mole fraction of 1:2 stoichiometry of the 4g-Cu2+ complex. Conclusion: The findings in the present study demonstrate that 4-Hydroxy-N'- methylenebenzohydrazide scaffold could be regarded as a bioactive core inhibitor of tyrosinase and can be used as an inspiration for further studies in this area.

Covid 19 – Structural proteins and prospective drug candidates (Preprint)

2020 ◽  
Author(s):  
A.N Anoopkumar ◽  
Sharrel Rebello ◽  
Embalil Mathachan Aneesh
Keyword(s):  
Causative Agent ◽  
Protein Level ◽  
Drug Targets ◽  
Current Paper ◽  
Structural Proteins ◽  
Drug Candidates ◽  
Propagation Properties

UNSTRUCTURED Covid 19 the causative agent of the current devastating pandemic has turned out to be a notorious virus to all men-irrespective of either common to scientific calibre. Attempts to combat this deadly virus are the need of the hour and quite often the best way to defeat an opponent is to keenly study about its structural and propagation properties. The current paper describes briefly Covid 19 at the genomic, structural and protein level to the best of our knowledge. Furthermore, the prospects of possible drug targets that could aid in the control of this virus are also discussed.

scholarly journals Conformation-Specific Inhibitory Anti-MMP-7 Monoclonal Antibody Sensitizes Pancreatic Ductal Adenocarcinoma Cells to Chemotherapeutic Cell Kill

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1679
Author(s):  
Vishnu Mohan ◽  
Jean P. Gaffney ◽  
Inna Solomonov ◽  
Maxim Levin ◽  
Mordehay Klepfish ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Active Site ◽  
Drug Targets ◽  
Fas Ligand ◽  
Specific Activity ◽  
Matrix Metalloproteases ◽  
Ductal Adenocarcinoma ◽  
Enzyme Active Site ◽  
Induced Apoptosis

Matrix metalloproteases (MMPs) undergo post-translational modifications including pro-domain shedding. The activated forms of these enzymes are effective drug targets, but generating potent biological inhibitors against them remains challenging. We report the generation of anti-MMP-7 inhibitory monoclonal antibody (GSM-192), using an alternating immunization strategy with an active site mimicry antigen and the activated enzyme. Our protocol yielded highly selective anti-MMP-7 monoclonal antibody, which specifically inhibits MMP-7′s enzyme activity with high affinity (IC50 = 132 ± 10 nM). The atomic model of the MMP-7-GSM-192 Fab complex exhibited antibody binding to unique epitopes at the rim of the enzyme active site, sterically preventing entry of substrates into the catalytic cleft. In human PDAC biopsies, tissue staining with GSM-192 showed characteristic spatial distribution of activated MMP-7. Treatment with GSM-192 in vitro induced apoptosis via stabilization of cell surface Fas ligand and retarded cell migration. Co-treatment with GSM-192 and chemotherapeutics, gemcitabine and oxaliplatin elicited a synergistic effect. Our data illustrate the advantage of precisely targeting catalytic MMP-7 mediated disease specific activity.

Dengue Structural Proteins as Antiviral Drug Targets: Current Status in the Drug Discovery & Development

2021 ◽  
pp. 113527
Author(s):  
Akshatha H. S ◽  
Gurubasavaraj V. Pujar ◽  
Arun Kumar Sethu ◽  
Meduri Bhagyalalitha ◽  
Manisha Singh
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Antiviral Drug ◽  
Structural Proteins ◽  
Current Status

scholarly journals Transfer Learning for Prognostics and Health Management (PHM) of Marine Air Compressors

2021 ◽  
Vol 9 (1) ◽  
pp. 47
Author(s):  
Magnus Gribbestad ◽  
Muhammad Umair Hassan ◽  
Ibrahim A. Hameed
Keyword(s):  
Transfer Learning ◽  
Data Transfer ◽  
Health Management ◽  
Correct Diagnosis ◽  
Vital Role ◽  
Research Field ◽  
Remaining Useful Life ◽  
Prognostic Models ◽  
Classification Problems ◽  
Air Compressors

Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. Due to the requirements of system safety and reliability, the correct diagnosis or prognosis of abnormal condition plays a vital role in the maintenance of industrial systems. It is expected that new requirements in regard to autonomous ships will push suppliers of maritime equipment to provide more insight into the conditions of their systems. One of the stated challenges with these systems is having enough run-to-failure examples to build accurate-enough prognostic models. Due to the scarcity of enough reliable data, transfer learning is established as a successful approach to improve and reduce the need to labelled examples. Transfer learning has shown excellent capabilities in image classification problems. Little work has been done to explore and exploit the use of transfer learning in prognostics. In this paper, various deep learning models are used to predict the remaining useful life (RUL) of air compressors. Here, transfer learning is applied by building a separate prognostics model trained on turbofan engines. It has been found that several of the explored transfer learning architectures were able to improve the predictions on air compressors. The research results suggest transfer learning as a promising research field towards more accurate and reliable prognostics.

scholarly journals Functional Stratification of Cancer Drugs through Integrated Network Similarity

2022 ◽  
Author(s):  
Nurcan Tuncbag ◽  
Seyma Unsal Beyge
Keyword(s):  
Cell Lines ◽  
Drug Targets ◽  
Hdac Inhibitors ◽  
Treatment Strategies ◽  
Network Models ◽  
Drug Combinations ◽  
Multiple Cancer ◽  
Integrated Network ◽  
Drug Molecules ◽  
The Difference

Abstract Heterogeneity across tumors is the main obstacle in developing treatment strategies. Drug molecules not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored the networks modulated by several drug molecules across multiple cancer cell lines by integrating the drug targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing the exact mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation, found literature evidence for a set of drug pairs. Overall, the network-level exploration of the drug perturbations may potentially help optimize treatment strategies and suggest new drug combinations.

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