Dataset Information

De novo ssRNA Aptamers against the SARS-CoV-2 Main Protease: In Silico Design and Molecular Dynamics Simulation.

ABSTRACT: Herein, we have generated ssRNA aptamers to inhibit SARS-CoV-2 Mpro, a protease necessary for the SARS-CoV-2 coronavirus replication. Because there is no aptamer 3D structure currently available in the databanks for this protein, first, we modeled an ssRNA aptamer using an entropic fragment-based strategy. We refined the initial sequence and 3D structure by using two sequential approaches, consisting of an elitist genetic algorithm and an RNA inverse process. We identified three specific aptamers against SARS-CoV-2 Mpro, called MAptapro, MAptapro-IR1, and MAptapro-IR2, with similar 3D conformations and that fall in the dimerization region of the SARS-CoV-2 Mpro necessary for the enzymatic activity. Through the molecular dynamic simulation and binding free energy calculation, the interaction between the MAptapro-IR1 aptamer and the SARS-CoV-2 Mpro enzyme resulted in the strongest and the highest stable complex; therefore, the ssRNA MAptapro-IR1 aptamer was selected as the best potential candidate for the inhibition of SARS-CoV-2 Mpro and a perspective therapeutic drug for the COVID-19 disease.

SUBMITTER: Morena F

PROVIDER: S-EPMC8267631 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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<i>De novo</i> ssRNA Aptamers against the SARS-CoV-2 Main Protease: In Silico Design and Molecular Dynamics Simulation.

Morena Francesco F Argentati Chiara C Tortorella Ilaria I Emiliani Carla C Martino Sabata S

International journal of molecular sciences 20210626 13

Herein, we have generated ssRNA aptamers to inhibit SARS-CoV-2 M<sup>pro</sup>, a protease necessary for the SARS-CoV-2 coronavirus replication. Because there is no aptamer 3D structure currently available in the databanks for this protein, first, we modeled an ssRNA aptamer using an entropic fragment-based strategy. We refined the initial sequence and 3D structure by using two sequential approaches, consisting of an elitist genetic algorithm and an RNA inverse process. We identified three speci ...[more]

PMID: 34206794

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Project description:The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Dataset Information

De novo ssRNA Aptamers against the SARS-CoV-2 Main Protease: In Silico Design and Molecular Dynamics Simulation.

Publications

<i>De novo</i> ssRNA Aptamers against the SARS-CoV-2 Main Protease: In Silico Design and Molecular Dynamics Simulation.

Similar Datasets

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