Project description:In this paper several computer programs were used to simulate in situ synthesis of peptides using shadow masks and BOC synthesis. The peptides were designed to be random, or pseudo-random, but fulfill requirements of immunosignaturing. This file contains data from actual 330,000 peptide arrays that used the first iteration of the peptide generation algorithm. Monoclonal antibodies were bound to the microarrays and the total number of peptides that distinguished each monoclonal was measured. This provides a baseline against which to compare purely random sequences.
Project description:In this paper several computer programs were used to simulate in situ synthesis of peptides using shadow masks and BOC synthesis. The peptides were designed to be random, or pseudo-random, but fulfill requirements of immunosignaturing. This file contains data from actual 330,000 peptide arrays that used the first iteration of the peptide generation algorithm. Monoclonal antibodies were bound to the microarrays and the total number of peptides that distinguished each monoclonal was measured. This provides a baseline against which to compare purely random sequences. One replicate of each peptide was printed on 1 330k peptide microarray. One microarray were tested for each sample. Image was qualified using in-house metrics for quality assurance.
Project description:A computer program was used to create random amino acid sequences based on and restricted by physical shadow masks which will be used for lithography-based synthesis of peptides. The output from this algorithm was used to create peptides that were synthesized by Sigma Aldrich, and printed onto glass slides. The arrays contained 384 peptides printed in duplicate for each of 4 different mask designs. 52 different monoclonal antibodies were incubated on these microarrays and analyzed for their propensity to bind the peptides created from each mask set. The diversity of binding served as a proxy for the 'randomness' of these peptides, and provided information about how many masks are needed to truly generate random sequence peptides.
Project description:A computer program was used to create random amino acid sequences based on and restricted by physical shadow masks which will be used for lithography-based synthesis of peptides. The output from this algorithm was used to create peptides that were synthesized by Sigma Aldrich, and printed onto glass slides. The arrays contained 384 peptides printed in duplicate for each of 4 different mask designs. 52 different monoclonal antibodies were incubated on these microarrays and analyzed for their propensity to bind the peptides created from each mask set. The diversity of binding served as a proxy for the 'randomness' of these peptides, and provided information about how many masks are needed to truly generate random sequence peptides. two replicates of each peptide was printed on 1 Mask peptide microarray. A minimum of Two microarrays were tested for each sample. Image was qualified using in-house metrics for quality assurance.
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes severe health crisis and huge socioeconomic upheaval internationally. This study proposes an ab initio design strategy to obtain antiviral peptides to against SARS-CoV-2 infection. The study employed database filtering technology to generate 7 amphipathic-symmetric peptides named DFTavPs with low cytotoxicity and random coil structure. Three DFTavPs promoted SARS-CoV-2 pseudoviruses infection and three DFTavPs inhibited virus infection, which are accompanied by up-regulation or down-regulation of SARS-CoV receptor angiotensin-converting enzyme 2 mRNA levels. Particularly, microRNA profiling showed that some differentially expressed microRNAs had potential to target key factors for cell entry of SARS-CoV-2. Furthermore, we explored the relationship of parameters and antiviral efficacy index (AEI). The results suggested that higher AEI of coronavirus was most likely to occur at mean amphipathic moment between 0.3 and 0.4. Automated machine learning was used to construct parameters-AEI regression models for various viruses. The Extra-Trees and CatBoost had a good predicting performance for AEI of coronavirus (R2=0.794 and Rpearson=0.897) and human immunodeficiency virus (R2=0.735 and Rpearson=0.859), respectively. Overall, this strategy is expected to efficiently obtain huge amounts of potential peptide drugs with anti-SARS-CoV-2 activity, and machine learning models could contribute to discovery of high antivirus-activity peptides.