PAWG Pilot Study on Quantification of SARS-CoV-2 Monoclonal Antibody - Part 1

Main text Under the auspices of the Protein Analysis Working Group (PAWG) of the Comité Consultatif pour la Quantité de Matière (CCQM) a pilot study, CCQM-P216, was coordinated by the Chinese National Institute of Metrology (NIM), National Research Council of Canada (NRC) and the Bureau International des Poids et Mesures (BIPM). Eleven Metrology Institutes or Designated Institutes and the BIPM participated in the first phase of the pilot study (Part 1). The purpose of this pilot study was to develop measurement capabilities for larger proteins using a recombinant humanized IgG monoclonal antibody against Spike glycoprotein of SARS-CoV-2 (Anti-S IgG mAb) in solution. The first phase of the study was designed to employ established methods that had been previously studies by the CCQM Protein Analysis Working Group, involving the digestion of protein down to the peptide or amino acid level. The global coronavirus pandemic has also led to increased focus on antibody quantitation methods. IgG are among the immunoglobulins produced by the immune system to provide protection against SARS-CoV-2. Anti-SARS-CoV-2 IgG can therefore be detected in samples from affected patients. Antibody tests can show whether a person has been exposed to the SARS-CoV-2, and whether or not they potentially show lasting immunity to the disease. With the constant spread of the virus and the high pressure of re-opening economies, antibody testing plays a critical role in the fight against COVID-19 by helping healthcare professionals to identify individuals who have developed an immune response, either via vaccination or exposure to the virus. Many countries have launched large-scale antibody testing for COVID-19. The development of measurement standards for the antibody detection of SARS-CoV-2 is critically important to deal with the challenges of the COVID-19 pandemic. In this study, the SARS-CoV-2 monoclonal antibody is being used as a model system to build capacity in methods that can be used in antibody quantification. Amino acid reference values with corresponding expanded uncertainty of 36.10 ± 1.55 mg/kg, 38.75 ± 1.45 mg/kg, 18.46 ± 0.78 mg/kg, 16.20 ± 0.67 mg/kg and 30.61 ± 1.30 mg/kg have been established for leucine, valine, phenylalanine, isoleucine and proline, respectively. Agreement between nearly all laboratories was achieved for the amino acid analysis within 2 to 2.5 %, with one participant achieving markedly higher results due to a technical issue found in their procedure; this result was thus excluded from the reference value calculations. The relatively good agreement within a laboratory between different amino acids was not dissimilar to previous results for peptides or small proteins, indicating that factors such as hydrolysis conditions and calibration procedures could be the largest sources of variability. Peptide reference values with corresponding expanded uncertainty of 4.99 ± 0.28 mg/kg and 6.83 ± 0.65 mg/kg have been established for ALPAPIEK and GPSVFPLAPSSK, respectively. Not surprisingly due to prior knowledge from previous studies on peptide quantitation, agreement between laboratories for the peptide-based analysis was slightly poorer at 3 to 5 %, with one laboratory's result excluded for the peptide GPSVFPLAPSSK. Again, this level of agreement was not significantly poorer than that achieved in previous studies with smaller or less complex proteins. To reach the main text of this paper, click on Final Report.

Pseudomonas Species Prevalence, Protein Analysis, and Antibiotic Resistance: An Evolving Public Health Challenge

Psychrotrophic Pseudomonas is one of the significant microbes that lead to putrefaction in chilled meat. One of the biggest problems in the detection of Pseudomonas is that several species are seemingly identical. Antibiotic resistance is an alternative considerable challenge worldwide. Therefore, this study was designed to apply an accurate technique for the discrepancy of Pseudomonas species and to study their resistance against various antimicrobials. A total of 320 chicken meat specimens were cultivated, and phenotypically recognized the isolated bacteria. Protein analysis was carried out for cultured isolates via Microflex LT. The resistance of Pseudomonas isolates was recorded through Vitek®ฏ 2 AST-GN83 cards. Overall, 69 samples were identified as Pseudomonas spp. and included 18 Pseudomonas lundensis (P. lundensis), 16 Pseudomonas fragi (P. fragi), 13 Pseudomonas oryzihabitans (P. oryzihabitans), 10 Pseudomonas stutzeri (P. stutzeri), 5 Pseudomonas fluorescens (P. fluorescens), 4 Pseudomonas putida (P. putida), and 3 Pseudomonas aeruginosa (P. aeruginosa) isolates. Microflex LT identified all Pseudomonas isolates (100%) correctly with a score value ≥2.00. PCA positively discriminated the identified isolates into various groups. The antimicrobial resistance levels against Pseudomonas isolates were 81.16% for nitrofurantoin, 71% for ampicillin and ampicillin/sulbactam, 65.22% for cefuroxime and ceftriaxone, 55% for aztreonam, and 49.28% for ciprofloxacin. The susceptibilities were 100% for cefotaxime, 98.55% for ceftazidime, 94.20% for each piperacillin/tazobactam and cefepime, 91.3% for cefazolin. In conclusion, chicken meat was found to be contaminated with different Pseudomonas spp., with high incidence rates of P. lundensis. Microflex LT is a potent tool for distinguishing Pseudomonads at the species level.

Deep learning model for protein disease classification

Background: Protein sequence analysis helps in the prediction of protein functions. As the number of proteins increases, it gives the bioinformaticians a challenge to analyze and study the similarity between them. Most of the existing protein analysis methods use Support Vector Machine. Deep learning did not receive much attention regarding protein analysis as it is noted that little work focused on studying the protein diseases classification. Objective: The contribution of this paper is to present a deep learning approach that classifies protein diseases based on protein descriptors. Methods: Different protein descriptors are used and decomposed into modified feature descriptors. Uniquely, we introduce using Convolutional Neural Network model to learn and classify protein diseases. The modified feature descriptors are fed to the Convolutional Neural Network model on a dataset of 1563 protein sequences classified into 3 different disease classes: Aids, Tumor suppressor, and Proto oncogene. Results: The usage of the modified feature descriptors shows a significant increase in the performance of the Convolutional Neural Network model over Support Vector Machine using different kernel functions. One modified feature descriptor improved by 19.8%, 27.9%, 17.6%, 21.5%, 17.3%, and 22% for evaluation metrics: Area Under the Curve, Matthews Correlation Coefficient, Accuracy, F1-score, Recall, and Precision, respectively. Conclusion: Results show that the prediction of the proposed modified feature descriptors significantly surpasses that of Support Vector Machine model.

Native Protein Analysis on TIMSTOF Pro mass spectrometer v2

Method described in David Robert et al., https://pubs.acs.org/doi/10.1021/jacs.1c02713