This is a research proposal that describes a method that attempts to use computational models to reverse engineer antibodies of recovered patients without the use of its genes found in effector B cells or the use of memory B cells samples of recovered patients. Most effector B cells are found in bone marrow and not in the serum, thus making it difficult to sample effector B cells from donors. If we concentrate on COVID19 treatments, even though current development of monoclonal antibodies specific for SARSCOV2 has been fortunate to find effector B cells and memory B cells specific for SARSCOV2 in the serum, there is a possibility that potent antibodies found in serum whose effector B cells or memory B cells specific for SARSCOV2 are not detected in samples of COVID19 survivors for the development of COVID19 monoclonal antibodies specific against SARSCOV2. Thus potentially missing an opportunity for the development of potent monoclonal antibodies specific for SARSCOV2.

The following is a method, the authors have named the HOPE method, for the development of genetically engineered monoclonal antibodies by studying the neutralizing antibodies (NAb) or broadly neutralizing antibodies (bNAb) of recovered patients of any viral infectious disease. "HOPE" is not an acronym, but named "HOPE" as a symbol of hope specifically for immunocompromised patients that may find more benefit from this proposed treatment. The HOPE method develops genetically engineered monoclonal antibodies that is most specific or most tightly bind to its epitopes of antigens of non-viral pathogens from serum samples of recovered patients of non-viral infectious diseases to be evaluated further to determine its efficacy. The HOPE method can also be applied to antibodies of oncology patients specific against tumor neoantigens for the development of personalized precision medicine and diagnostic tests. If we venture out further, certain steps of the HOPE method may also potentially be used in material science for mass production of bioproteins whose genes are unknown.

Given the efficacy of monoclonal antibodies specific against SARSCOV2 during the pandemic, one can hypothesize that neutralizing antibodies and broadly neutralizing antibodies of recovered COVID19 patients vary in potency and efficacy based on the antibodies ability to most tightly bind their FAB component to their corresponding epitopes as well as its ability of having an efficient FC region. Thus selecting the neutralizing antibody or broadly neutralizing antibody with these criteria can be used as good guides to try to reverse engineer for the development of potent monoclonal antibodies specific against SARSCOV2. One can hypothesis as well that such method in monoclonal antibody production can also be applied in various diseases that produce an adaptive immune response whose antibodies can be used as guides for monoclonal antibody production. We can also hypothesize that analyzing the epitopes that bind to selected neutralizing antibodies and broadly neutralizing antibodies of recovered patients can assist in identifying potential targets, which vaccine development can be directed to, that is by analyzing the epitope's mRNA sequence that can be added to mRNA vaccine development.

The authors would like to keep HOPE Monoclonal Antibodies (HOPE-mAb) as the nomenclature of the genetically engineered recombinant monoclonal antibodies produced via the HOPE method. Although, subsequent steps around the development of HOPE-mAb may appear specific to COVID19, the overall methodology can be broadly applied for other diseases or tumors that produce antibodies in recovered patients. HOPE mAbs specific against SARSCOV2 can be commercialized more rapidly for in vitro rapid diagnostic COVID19 tests and for laboratory research use in COVID19 studies. Rapid tests development and laboratory research use of HOPE mAbs for other diseases may also be possible with HOPE method upon showing its efficacy in binding to their intended epitopes. 

In viral infectious diseases, the neutralizing antibodies are an important part of the HOPE method by selecting the best neutralizing antibody within a population of recovered patients, whose FAB component are the most specific to the epitope of the antigen in other words the neutralizing antibodies that binds most compactly to its epitope. Detailing the HOPE method of reverse engineering an antibody of a recovered patient of viral infections even further in particular, the HOPE method is performed with the help of mass spectrometer and cryogenic electron microscope (cryo-EM) to obtain 3D protein models of the neutralizing antibodies (NAb) and run de novo peptide sequencing. Mass spectrometry and computational models are used to decode the linear amino acid sequence. The 3D protein models obtained with cryo-EM may help perfect these computational models with image datasets identifying the amino acids within the protein folded structure and its comparison with the analysis of the mass spectrometer. Computational models can be used to reverse the central dogma by predicting the codon sequence from the amino acid sequence and subsequently, the codon is decoded by another computational model or machine learning algorithm to help predict the mRNA sequence. Computational models to decode the RNA codon from the amino acid sequence can be trained with codon chart analysis. These steps would be done for both the FAB component of an effective antibody against a neoantigen of a tumor or epitope of a pathogen (like SARS-COV2 for example, from recovered COVID19 patients) and the constant region of a fully human monoclonal antibody that has proven to be effective in prior studies. This is followed by uniting the two mRNA sequences to form the mRNA of a full monoclonal antibody specific to the pathogen, like SARS-COV2. The predicted mRNA sequence of the full monoclonal antibody can be genetically engineered into plasmids and reproduced in yeast cultures with recombinant DNA technology or other cost effective methods for mass production, as detailed in this paper.