Abstract: Single-domain antibodies that bind the severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) spike protein are disclosed. The single-domain antibodies include binding domains that bind epitopes of the Spike ectodomain inside and outside the receptor binding domain. The single-domain antibodies can be used for multiple purposes including in the research, diagnosis, and prophylactic or therapeutic treatment of COVID-19.

Abstract: The present invention relates generally ultrasensitive assays for use in diagnostics and in methods of drug screening and personalizing therapy for an individual patient. Specifically, the present invention relates to improved imaging and computational methods for detecting molecular phenotypes.

Abstract: The present invention relates to a kit for mass labelling of peptides in two or more samples, comprising a set of mass tagging reagents for reaction at the N-terminals of the peptides and a set of mass balancing reagents for reaction at the C-terminals of the peptides, or vice versa, and wherein each mass tagging reagent in the set is matched with mass balancing reagent(s) in such a way that the sum of the masses from all matched mass tagging and mass balancing reagents is equal. The invention also relates to a method of using said mass labels and a database for use in said method.

Abstract: The present invention relates to a method for peptide analysis, comprising the following steps: a) tagging N-terminals of peptides in sample(s) with mass tagging reagent(s) and mass balancing C-terminals of said peptides with mass balancing reagent(s), or vice versa; and b) mass spectrometry analysis of said peptides. The present invention also relates to a kit with global mass tagging reagents and mass balancing reagents for use in said method and a database with specific peptide information.

Abstract: A method for determining the probability that a biological molecule identification is random for a chosen significance level and for a particular experimental condition, the method comprising:

Abstract: A method for determining the probability that a biological molecule identification is incorrect for a chosen significance level is provided. The method includes comparing experimental mass data of an unknown biological molecule with theoretical mass data and calculating a score for each comparison; selecting at least two scores from the scores to form a primary data set; generating artificial data sets from the primary data set; calculating a sample mean for each artificial data set; estimating population mean and population standard deviation from the sample means wherein the population is based on the distribution underlying the primary dataset; computing a Z score from the population mean and population standard deviation for each score to standardize the scores; choosing a significance level; and comparing a test Z score to a Z score of the chosen significance level to determine the probability that the biological molecule identification is incorrect.

Abstract: A method for evaluating the performance of biopolymer identification algorithms, the method comprising a) generating noise and signal distributions, wherein the noise distribution comprises identification results obtained from the search of a database for arbitrarily generated sets of database mass data and wherein the signal distribution comprises identification results obtained from the search of a database for arbitrarily generated sets of database mass data which comprises mass data of a particular biopolymer of the database designated as a signal biopolymer; and b) calculating a performance index from the distributions which evaluates the performance of the algorithm.