scholarly journals Computer-Aided Approaches to De Novo Design of drug candidates targeting the SARS-CoV-2 Spike protein bound to angiotensin converting enzyme 2 (ACE2)

2021 ◽  
Author(s):  
Konstantinos Kalamatianos
Keyword(s):  
De Novo ◽  
Md Simulations ◽  
De Novo Design ◽  
Design Tool ◽  
Spike Protein ◽  
De Novo Drug Design ◽  
Angiotensin Converting Enzyme 2 ◽  
Lipinski’S Rule ◽  
Drug Candidates ◽  
Computer Aided

In this study a computer-aided approach to de novo design of chemical entities with drug-like properties against the SARS-CoV-2 Spike protein bound to ACE2 is presented. A structure-based de novo drug design tool LIGANN was used to produce complementary ligand shapes to the SARS-CoV-2 Spike protein (6M0J). The obtained ligand structures - potential drug candidates – were optimized and virtually screened. Hit ligands were considered all that showed initial binding energy scores ≤ -9.0 kcal.mol-1 for the protein. These compounds were tested for drug-likeness (Lipinski’s rule and BOILED Permeation Predictive Model). All satisfying the criteria were re-optimized (geometry & frequencies) at the HF-3c33 level of theory and virtually screened against 6M0J. Molecular dynamics (MD) simulations were used to assess the structural stability of selected 6M0J/novel compound complexes. Synthetic pathways for selected compounds from commercially available starting materials are proposed.

scholarly journals Computer-Aided Approaches to De Novo Design of drug candidates targeting the SARS-CoV-2 Spike protein bound to angiotensin converting enzyme 2 (ACE2)

2021 ◽  
Author(s):  
Konstantinos Kalamatianos
Keyword(s):  
De Novo ◽  
Md Simulations ◽  
De Novo Design ◽  
Design Tool ◽  
Spike Protein ◽  
De Novo Drug Design ◽  
Angiotensin Converting Enzyme 2 ◽  
Lipinski’S Rule ◽  
Drug Candidates ◽  
Computer Aided

In this study a computer-aided approach to de novo design of chemical entities with drug-like properties against the SARS-CoV-2 Spike protein bound to ACE2 is presented. A structure-based de novo drug design tool LIGANN was used to produce complementary ligand shapes to the SARS-CoV-2 Spike protein (6M0J). The obtained ligand structures - potential drug candidates – were optimized and virtually screened. Hit ligands were considered all that showed initial binding energy scores ≤ -9.0 kcal.mol-1 for the protein. These compounds were tested for drug-likeness (Lipinski’s rule and BOILED Permeation Predictive Model). All satisfying the criteria were re-optimized (geometry & frequencies) at the HF-3c33 level of theory and virtually screened against 6M0J. Molecular dynamics (MD) simulations were used to assess the structural stability of selected 6M0J/novel compound complexes. Synthetic pathways for selected compounds from commercially available starting materials are proposed.

scholarly journals De novo design of potent and resilient hACE2 decoys to neutralize SARS-CoV-2

Science ◽  
2020 ◽  
Vol 370 (6521) ◽  
pp. 1208-1214 ◽  
Author(s):  
Thomas W. Linsky ◽  
Renan Vergara ◽  
Nuria Codina ◽  
Jorgen W. Nelson ◽  
Matthew J. Walker ◽  
...  
Keyword(s):  
Protein Design ◽  
De Novo ◽  
High Specificity ◽  
Spike Protein ◽  
Host Proteins ◽  
Angiotensin Converting Enzyme 2 ◽  
Cryo Electron Microscopy ◽  
De Novo Protein Design ◽  
Single Spike

We developed a de novo protein design strategy to swiftly engineer decoys for neutralizing pathogens that exploit extracellular host proteins to infect the cell. Our pipeline allowed the design, validation, and optimization of de novo human angiotensin-converting enzyme 2 (hACE2) decoys to neutralize severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The best monovalent decoy, CTC-445.2, bound with low nanomolar affinity and high specificity to the receptor-binding domain (RBD) of the spike protein. Cryo–electron microscopy (cryo-EM) showed that the design is accurate and can simultaneously bind to all three RBDs of a single spike protein. Because the decoy replicates the spike protein target interface in hACE2, it is intrinsically resilient to viral mutational escape. A bivalent decoy, CTC-445.2d, showed ~10-fold improvement in binding. CTC-445.2d potently neutralized SARS-CoV-2 infection of cells in vitro, and a single intranasal prophylactic dose of decoy protected Syrian hamsters from a subsequent lethal SARS-CoV-2 challenge.

scholarly journals Sulfonanilide Derivatives in Identifying Novel Aromatase Inhibitors by Applying Docking, Virtual Screening, and MD Simulations Studies

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Shailima Rampogu ◽  
Minky Son ◽  
Chanin Park ◽  
Hyong-Ha Kim ◽  
Jung-Keun Suh ◽  
...  
Keyword(s):  
Aromatase Inhibitors ◽  
Causes Of Death ◽  
Binding Free Energy ◽  
Chemical Compounds ◽  
Md Simulations ◽  
Free Energy Calculations ◽  
Lipinski’S Rule ◽  
Discovery Studio ◽  
Drug Candidates ◽  
Lipinski’S Rule Of Five

Breast cancer is one of the leading causes of death noticed in women across the world. Of late the most successful treatments rendered are the use of aromatase inhibitors (AIs). In the current study, a two-way approach for the identification of novel leads has been adapted. 81 chemical compounds were assessed to understand their potentiality against aromatase along with the four known drugs. Docking was performed employing the CDOCKER protocol available on the Discovery Studio (DS v4.5). Exemestane has displayed a higher dock score among the known drug candidates and is labeled as reference. Out of 81 ligands 14 have exhibited higher dock scores than the reference. In the second approach, these 14 compounds were utilized for the generation of the pharmacophore. The validated four-featured pharmacophore was then allowed to screen Chembridge database and the potential Hits were obtained after subjecting them to Lipinski’s rule of five and the ADMET properties. Subsequently, the acquired 3,050 Hits were escalated to molecular docking utilizing GOLD v5.0. Finally, the obtained Hits were consequently represented to be ideal lead candidates that were escalated to the MD simulations and binding free energy calculations. Additionally, the gene-disease association was performed to delineate the associated disease caused by CYP19A1.

scholarly journals Computational simulations reveal the binding dynamics between human ACE2 and the receptor binding domain of SARS-CoV-2 spike protein

2020 ◽  
Author(s):  
Cecylia S. Lupala ◽  
Xuanxuan Li ◽  
Jian Lei ◽  
Hong Chen ◽  
Jianxun Qi ◽  
...  
Keyword(s):  
Receptor Binding ◽  
Genomic Analysis ◽  
Md Simulations ◽  
Binding Domain ◽  
Spike Protein ◽  
Receptor Binding Domain ◽  
Binding Modes ◽  
Angiotensin Converting Enzyme 2 ◽  
Causal Pathogen ◽  
Novel Coronavirus

AbstractA novel coronavirus (the SARS-CoV-2) has been identified in January 2020 as the causal pathogen for COVID-19 pneumonia, an outbreak started near the end of 2019 in Wuhan, China. The SARS-CoV-2 was found to be closely related to the SARS-CoV, based on the genomic analysis. The Angiotensin converting enzyme 2 protein (ACE2) utilized by the SARS-CoV as a receptor was found to facilitate the infection of SARS-CoV-2 as well, initiated by the binding of the spike protein to the human ACE2. Using homology modeling and molecular dynamics (MD) simulation methods, we report here the detailed structure of the ACE2 in complex with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. The predicted model is highly consistent with the experimentally determined complex structures. Plausible binding modes between human ACE2 and the RBD were revealed from all-atom MD simulations. The simulation data further revealed critical residues at the complex interface and provided more details about the interactions between the SARS-CoV-2 RBD and human ACE2. Two mutants mimicking rat ACE2 were modeled to study the mutation effects on RBD binding to ACE2. The simulations showed that the N-terminal helix and the K353 of the human ACE2 alter the binding modes of the CoV2-RBD to the ACE2.

scholarly journals Multi-Targeting Approach in Selection of Potential Molecule for COVID-19 Treatment

2020 ◽  
Author(s):  
Varalakshmi Velagacherla ◽  
Akhil Suresh ◽  
Chetan H Mehta ◽  
Yogendra Nayak ◽  
Usha Y Nayak
Keyword(s):  
Md Simulations ◽  
Viral Proteins ◽  
Spike Protein ◽  
Protein Targets ◽  
Angiotensin Converting Enzyme 2 ◽  
Main Protease ◽  
Multiple Protein ◽  
Approved Drugs ◽  
Viral Target ◽  
Selection Of

Abstract Background: Coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is now a pandemic which began in Wuhan province of China. Drug discovery teams around the globe are in a race to develop a medicine for its management. For a novel molecule to enter into the market it takes time and the ideal way is to exploit the already approved drugs and repurpose them to use therapeutically.Methods: In this work, we have attempted to screen selected molecules that have shown an affinity towards multiple protein targets of COVID-19 using Schrödinger suit. Molecules were selected from approved antiviral, anti-inflammatory or immunomodulatory classes. The viral proteins selected were angiotensin-converting enzyme 2 (ACE2), main protease (Mpro) and spike protein. Computational tools such as molecular docking, prime MM-GBSA, induced-fit docking (IFD) and molecular dynamics (MD) simulations were used to identify the most suitable molecule that forms a stable interaction with the selected viral proteins.Results: The ligand-binding stability for the viral proteins PDB-IDs 1ZV8 (spike protein), 5R82 (Mpro) and 6M1D (ACE2), was in the order of Nintedanib>Quercetin, Nintedanib>Darunavir, Nintedanib> Baricitinib respectively. The MM-GBSA, IFD, and MD simulation studies infer that the drug nintedanib has the highest binding stability among the shortlisted molecules towards the selected viral target proteins. Conclusion: Nintedanib, which is primarily used for idiopathic pulmonary fibrosis, can be considered for repurposing and used in the management of COVID-19.

scholarly journals REINVENT 2.0 – an AI Tool for De Novo Drug Design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
Interaction Point ◽  
De Novo Drug Design ◽  
Generative Methods ◽  
Collaborative Efforts ◽  
Scientific Collaborations

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.

scholarly journals REINVENT 2.0 – an AI Tool for De Novo Drug Design

2020 ◽  
Author(s):  
Thomas Blaschke ◽  
Josep Arús-Pous ◽  
Hongming Chen ◽  
Christian Margreitter ◽  
Christian Tyrchan ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
De Novo ◽  
Chemical Space ◽  
De Novo Design ◽  
Interaction Point ◽  
De Novo Drug Design ◽  
Generative Methods ◽  
Collaborative Efforts ◽  
Scientific Collaborations

With this application note we aim to offer the community a production-ready tool for de novo design. It can be effectively applied on drug discovery projects that are striving to resolve either exploration or exploitation problems while navigating the chemical space. By releasing the code we are aiming to facilitate the research on using generative methods on drug discovery problems and to promote the collaborative efforts in this area so that it can be used as an interaction point for future scientific collaborations.

scholarly journals GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
M. Sicho ◽  
X. Liu ◽  
D. Svozil ◽  
G. J. P. van Westen
Keyword(s):  
User Interface ◽  
Drug Discovery ◽  
Drug Design ◽  
Open Source ◽  
De Novo ◽  
Design Methods ◽  
Software Platform ◽  
De Novo Drug Design ◽  
Computer Aided ◽  
Generative Algorithms

AbstractMany contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.

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