COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a new recently emerged sarbecovirus. This virus uses the human ACE2 enzyme as receptor for cell entry, recognizing it with the receptor binding domain (RBD) of the S1 subunit of the viral spike protein. We present the use of phage display to select anti-SARS-CoV-2 spike antibodies from the human naïve antibody gene libraries HAL9/10 and subsequent identification of 309 unique fully human antibodies against S1. 17 antibodies are binding to the RBD, showing inhibition of spike binding to cells expressing ACE2 as scFv-Fc and neutralize active SARS-CoV-2 virus infection of VeroE6 cells. The antibody STE73-2E9 is showing neutralization of active SARS-CoV-2 as IgG and is binding to the ACE2-RBD interface. Thus, universal libraries from healthy human donors offer the advantage that antibodies can be generated quickly and independent from the availability of material from recovering patients in a pandemic situation.

Anabel (Analysis of binding events + l) is an open source online software tool (www.skscience.org/anabel) for the convenient analysis of molecular binding interactions. Currently, exported datasets from Biacore (surface plasmon resonance [SPR]), FortéBio (biolayer interference [BLI]), and Biametrics (single color reflectometry [SCORE]) can be uploaded and evaluated in Anabel using 2 different evaluation methods. Moreover, a universal data template format is provided to upload any other binding dataset to Anabel. This enables an easier comparison of different analysis methods for all users. Furthermore, a guide was established in Anabel to help inexperienced users to obtain optimal results. In addition, expert features can be used to optimize and control the fit of the binding model to the measured data. We tried to make the process of fitting and evaluating as easy as possible through the use of an intuitive user interface. At the end of every analysis, a single excel file, containing all results and graphs of the performed analysis, can be downloaded.

In this work we show how DNA microarrays can be produced batch wise on standard microscope slides in a fast, easy, reliable and cost-efficient way. Contrary to classical microarray generation, the microarrays are generated via digital solid phase PCR. We have developed a cavity-chip system made of a PDMS/aluminum composite which allows such a solid phase PCR in a scalable and easy to handle manner. For the proof of concept, a DNA pool composed of two different DNA species was used to show that digital PCR is possible in our chips. In addition, we demonstrate that DNA microarray generation can be realized with different laboratory equipment (slide cycler, manually in water baths and with an automated cartridge system). We generated multiple microarrays and analyzed over 13,000 different monoclonal DNA spots to show that there is no significant difference between the used equipment. To show the scalability of our system we also varied the size and number of the cavities located in the array region up to more than 30,000 cavities with a volume of less than 60 pL per cavity. With this method, we present a revolutionary tool for novel DNA microarrays. Together with new established label-free measurement systems, our technology has the potential to give DNA microarray applications a new boost.

The kinetics of featured interactions (KOFFI) database is a novel tool and resource for binding kinetics data from biomolecular interactions. While binding kinetics data are abundant in literature, finding valuable information is a laborious task. We used text extraction methods to store binding rates (association, dissociation) as well as corresponding meta-information (e.g. methods, devices) in a novel database. To date, over 270 articles were manually curated and binding data on over 1705 interactions was collected and stored in the (KOFFI) database. Moreover, the KOFFI database application programming interface was implemented in Anabel (open-source software for the analysis of binding interactions), enabling users to directly compare their own binding data analyses with related experiments described in the database.