Bozkurt, E. U. | bioRxiv Science

bioRxiv2021

A Highly Potent SARS-CoV-2 Blocking Lectin Protein

COVID-19 pandemic effected more than 180 million people around the globe causing more than four million deaths as of July 2021. Sars-CoV-2, the new coronavirus, has been identified as the primary cause of the infection. The number of vaccinated people is increasing however prophylactic drugs are highly demanded to ensure a secure social contact. There have been a number of drug molecules repurposed to fight against Sars-CoV-2, however the proofs for the effectiveness of these drug candidates is limited. Here we demonstrated griffithsin (GRFT), a lectin protein, to block the entry of the Sars-CoV2 into the Vero6 cell lines and IFNAR-/-mouse models by attaching to spike protein of the Sars-CoV-2. Given the current mutation frequency of the Sars-CoV-2 we believe that GRFT protein-based drugs will have a high impact in preventing the transmission both on Wuhan strain as well as any other emerging variants including delta variant causing high speed spread of COVID-19.

biochemistry↗

bioRxiv2020

Genetic Circuits Combined with Machine Learning Provides Fast Responding Living Sensors

Whole cell biosensors (WCBs) have become prominent in many fields from environmental analysis to biomedical diagnostics thanks to advanced genetic circuit design principles. Despite increasing demand on cost effective and easy-to-use assessment methods, a considerable amount of WCBs retains certain drawbacks such as long response time, low precision and accuracy. Furthermore, the output signal level does not correspond to a specific analyte concentration value but shows comparative quantification. Here, we utilized a neural network-based architecture to improve the aforementioned features of WCBs and engineered a gold sensing WCB which has a long response time (18 h). Two Long-Short Term-Memory (LSTM)-based networks were integrated to assess both ON/OFF and concentration dependent states of the sensor output, respectively. We demonstrated that binary (ON/OFF) network was able to distinguish between ON/OFF states as early as 30 min with 78% accuracy and over 98% in 3 h. Furthermore, when analyzed in analog manner, we demonstrated that network can classify the raw fluorescence data into pre-defined analyte concentration groups with high precision (82%) in 3 h. This approach can be applied to a wide range of WCBs and improve rapidness, simplicity and accuracy which are the main challenges in synthetic biology enabled biosensing.

synthetic biology↗