Improved prediction of MHC class I binders/non-binders peptides through artificial neural network using variable learning rate: SARS corona virus, a case study

What Can COVID-19 Teach Us about Using AI in Pandemics?

The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI’s weaknesses as key factors affecting AI in future pandemic preparedness and response.

Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review

The term machine learning refers to a collection of tools used for identifying patterns in data. As opposed to traditional methods of pattern identification, machine learning tools relies on artificial intelligence to map out patters from large amounts of data, can self-improve as and when new data becomes available and is quicker in accomplishing these tasks. This review describes various techniques of machine learning that have been used in the past in the prediction, detection and management of infectious diseases, and how these tools are being brought into the battle against COVID-19. In addition, we also discuss their applications in various stages of the pandemic, the advantages, disadvantages and possible pit falls.

Identification of MHC Ligands and Establishing MHC Class I Peptide Motifs

MHC class I peptide motifs are used on a regular basis to identify and predict MHC class I ligands and CD8⁺ T cell epitopes. This approach is above all an invaluable tool for the identification of disease-associated epitopes ranging from pathogen associated epitopes, tumor associated natural and neoepitopes to autoimmune disease associated epitopes. As a matter of fact, the vast majority of T cell epitopes discovered during the past two decades was identified by means of epitope prediction and MHC ligand identification. Here we describe the steps which are necessary to identify MHC epitopes from monoallelic and multiallelic cells and establish MHC class I peptide motifs to compose a reliable scoring matrix for epitope prediction. As an example, the ligands of monoallelic C1R cells and multiallelic peripheral blood mononuclear cell tissue will be identified and a scoring matrix for the prediction of HLA-C*01:02-presented T cell epitopes will be developed.

Establishing MHC class I peptide motifs

Major histocompatibility complex (MHC) class I peptide motifs are used on a regular basis to identify and predict MHC class I ligands and CD8(+) T-cell epitopes. This approach is above all an invaluable tool for the identification of disease-associated epitopes. As a matter of fact, the vast majority of T-cell epitopes discovered during the past two decades was identified by means of epitope prediction. Here we describe the steps which are necessary to establish MHC class I peptide motifs and to compose a reliable scoring matrix for epitope prediction. As an example, a scoring matrix for the prediction of HLA-B*35-presented T-cell epitopes will be developed by examining the characteristics of 76 naturally presented HLA ligands.

Improved proteomic approach for the discovery of potential vaccine targets in Trypanosoma cruzi

Chagas disease, caused by Trypanosoma cruzi, is a devastating parasitic infection affecting millions of people. Although many efforts have been made for the development of immunotherapies, there is no available vaccine against this deadly infection. One major hurdle for the rational approach to develop a T. cruzi vaccine is the limited information about the proteins produced by different phylogenetic lineages, strains, and stages of the parasite. Here, we have adapted a 1D nanoHPLC system to perform online 2D LC-MS/MS, using the autosampler to inject the eluting salt solutions in the first dimension separation. The application of this methodology for the proteomic analysis of the infective trypomastigote stage of T. cruzi led to the identification of 1448 nonredundant proteins. Furthermore, about 14% of the identified sequences comprise surface proteins, most of them glycosylphosphatidylinositol (GPI)-anchored and related to parasite pathogenesis. Immunoinformatic analysis revealed thousands of potential peptides with predicted high-binding affinity for major histocompatibility complex (MHC) class I and II molecules. The high diversity of proteins expressed on the trypomastigote surface may have many implications for host-cell invasion and immunoevasion mechanisms triggered by the parasite. Finally, we performed a rational approach to filter potential T-cell epitopes that could be further tested and validated for development of a Chagas disease vaccine.