SARS-CoV-2

The current SARS-CoV-2 outbreak has become one of the biggest threats to the global economy and financial market since World War II. The frequent spread of the SARS-CoV-2 and complex mechanism of action, makes it hard to cope up with the infection of virus. SARS-CoV-2 is considered very prone to mutations as suggested by the stats from Gisaid (A tool for real-time tracking of pathogen evolution). SARS-CoV-2 is undergoing 25.2 substitutions per month, which may cause the shortcoming of effectively planned medications. These issues can be tackled by the use of machine learning and deep learning based models. These models have already been proven very successful in mutation predictions of Influenza virus. In this project, deep learning based models are being used to achieve the task of mutation prediction in the genome of SARS-CoV-2, under the spectrum of AIMPID. AIMPID is focused on predicting mutations in SARS-CoV-2 genome based on deep neural network models like generative adversarial networks (GANs), recurrent neural networks (RNNs), encoder-decoder architectures and deep seq2seq models. These models will be tested, optimized, and evaluated using the available sequence dataset in future. In the context of AIMPID, we also collaborate with EISBACH (a biotech company) which will be developing small inhibitor molecules to target the virulent protein products of predicted mutations to block their activities. This may help in controlling the spread of pandemic as frequent mutations in SARS-CoV-2 genome (especially in potential vaccine targets) are occurring, rendering the developed medications useless.

SARS-CoV-2 has emerged in November 2019 and since then the world has been suffering from covid-19 pandemic which is hard to contain because of its frequently occurring mutations and complex mechanism of action. The World Health Organization (WHO) has reported 5775 distinct variants for SARS-CoV-2 by analysing the viral genome from USA, UK, Australia and Northern Ireland. Certain SARS-COV-2 vaccines are available in the market, these vaccines can provide short term protection but they cannot assure safety from future variations in SARS-CoV-2 genome. So it has become pivotal to be well prepared against such mutations which may serve as a menace. Our research focuses on the mutation rate prediction and prediction of mutations that are most likely to occur in the least mutated proteins which are crucial to viral activities inside the host to produce such drugs that are long lasting and effective. This is achieved by developing algorithms for prediction of mutations and their rates. Our algorithms are based on  seq2seq, GAN and RNN based models to achieve these tasks.

• Development of deep learning based models for the prediction of mutations and mutation rates in the genome of SARS-CoV-2.
• Testing, optimization and benchmarking of the designed algorithms on the genomic datasets of SARS-CoV-2 from Gisaid.
• Significance analyses of predicted mutations and mutation rates to explore  effects of the mutation on the virulence of virus.
• Protein stability analyses and computational development of inhibitor molecules.
• Experimentation on the basis of quantitative wet lab based assays to explore the behavior of the potential inhibitor molecules.
• Experimentation in Escherichia coli to analyze the behavior of the molecules against the virus.
• Testing the effect of the designed drugs on SARS-CoV-2 via human cell lines.

SARS-CoV-2
Mutation Prediction
Mutation Rates
Artificial Intelligence
Drug Designing

M.Sc. Darrak Moin Quddusi
Gottlieb-Daimler-Str. 42
67663, Kaiserslautern
Phone: +49 (0)631/205-3398
Fax: +49 (0)631/205-4201
darrak.quddusi(at)mv.uni-kl.de

 Bundesministerium für Wirtschaft und Energie

Since 2020

Eisbach Bio GmbH

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