The Good Hope Net project uses Russian supercomputer to develop treatment against coronavirus infection

The coronavirus pandemic in 2020 attracted significant attention of many research groups. The international and multidisciplinary The Good Hope Net project brought together researchers from Russia, Finland, Italy, Canada and China to develop a medicine for diagnostics and therapy against the coronavirus contagious disease that became the cause of the global pandemic. The project takes advantages of the latest advances in experimental physics, chemistry, and biology to investigate the life cycle of the virus and to target specifically its specific proteins. The scientists have a high priority access to RSC Tornado supercomputer deployed at Joint Supercomputing Center of Russian Academy of Sciences (JSCC RAS) for developing a computer model of a medical drug with selective interaction with receptor-binding domain of Spike protein of SARS-CoV-2 coronavirus strain. 

SARS-CoV-2 S protein plays an important role in virus infection since it mediates viral entry into host cells by binding to a host receptor through the receptor-binding domain (RBD). In this project, we present a structure-based aptamer design. It is a computational process of screening and directed mutagenesis of oligonucleotides with high affinity and selectivity to the RBD. 

We designed as starting library a 16mer sequence (5’-GGAATT NNNN AATTCC-3’) consisting of more than 200 aptamer candidates. We used docking simulations, molecular dynamics and methods of quantum chemistry to predict binding sites and evaluate a binding affinity between the aptamer and RBD. Thus, we selected the best aptamer candidate from the starting library.

In order to increase the binding affinity to key amino acids in the RBD we improved the initial sequence of chosen aptamer in the successive iterations aptamer structure modifications. As a result, we modeled four DNA oligonucleotides containing 16, 25, 27 and 31 nucleotides, where the last two are expected to show stronger interaction with the RBD. 

Affinity of the in silico predicted aptamers to recombinant RBD were analyzed in vivo using flow cytometry. According to the analyses, the fluorescence intensity of the candidate sequences was higher than that to the random sequence. In addition, 31mer aptamer showed stronger fluorescence signal against RBD than random sequence and other three candidate sequences. 

The experimental results confirmed the efficacy of computer-aided design of aptamers to improve affinity and specificity of the aptamer binding towards SARS-CoV-2 RBD domain. We are going to apply this approach both in the current project and in the development of aptamers for other targets.