Combine group’s integrated bioinformatics pipeline predicts the response of human immune system

The group from University of Catania and coordinated by Prof Pappalardo illustrates the latest developments in a paper accepted by the prestigious journal Briefings in Bioinformatics


The phenomenon of cross-reactivity is the ability of an antibody to act against viruses that are different but very similar - at least in some parts - to each other.

Researchers at the Combine Group of the University of Catania investigated cross-reactivity in relation to the possibility of finding existing vaccines that could elicit the human immune system response to SARS-CoV-2.

Prof Pappalardo and his team describe the findings in a paper that has been recently accepted for publication in the prestigious journal Briefing of Bioinformatics: the article shows in detail the multi-step and multi-scale bioinformatic problem solving protocol that can be used to discover and test potential antigen portions that could be implemented in vaccines against SARS-CoV-2.

The integrated bioinformatics pipeline used to test these portions merges the prediction power of different software that act at different scales (molecular to organ) with a final step where an in silico platform simulates the human immune system dynamics in response to SARS-CoV-2.


Technological advances in computer modelling and simulation are making the difference to better predict the efficacy and safety of new vaccines” - says Professor Francesco Pappalardo, associate professor at the University of Catania - “and help in providing quick interventional solutions to outbreaks as the COVID-19 pandemic.”


Findings from this research are based on previous observations of co-author Pedro Reche, associate professor in immunology at the Complutense University of Madrid: “We found combination vaccines for treating diphtheria, tetanus, and pertussis infectious diseases (DTP vaccine) to be significant sources of potential cross-reactive immunity to SARS-CoV-2[1], and this result has been confirmed by a recent study published in preprint in June[2]”.


The most interesting aspect of the research is that the proposed protocol can be potentially applied to every disease, as main author Dr Giulia Russo explains: “This paper[3] represents a great result for the STriTuVaD project, in which the computational platform has been tested to simulate the human immune system response to tuberculosis disease and treatment, and it is also a starting point for further investigations that will be carried out during the In Silico World project.”


More info: The "COmputational Modeling in systems BIomediciNE" (COMBINE) Group of the University of Catania, Department of Drug and Health Sciences, deals with computational biomedicine and has developed the Universal Immune System Simulator (UISS), a computational framework that makes use of a multi-scale, multi-organ, three-dimensional agent based simulator of the immune system, with an attached module able to simulate the dynamics of a biological pathway at the molecular level, named COmputational StrategieS for the analysis of Biological pAthways and moleculaR surface/binding (CrOSSBAR)

STriTuVaD (in Silico TRials for TUberculosis VAccine Development)
In Silico World - Lowering the barriers to a universal adoption of In Silico Trials
[1] Reche PA. Potential Cross-Reactive Immunity to SARS-CoV-2 From Common Human Pathogens and Vaccines. Front Immunol. 2020 Oct 16;11:586984. doi: 10.3389/fimmu.2020.586984. PMID: 33178220; PMCID: PMC7596387. [2] Monereo-Sánchez, J., J. J. Luykx, J. Pinzón-Espinosa, G. Richard, E. Motazedi, L. T. Westlye, O. A. Andreassen, D. van der Meer, Vaccination history for diphtheria and tetanus is associated with less severe COVID-19, medRxiv 2021.06.09.21257809; doi: https://doi.org/10.1101/2021.06.09.21257809
[3] Russo, G. V. Di Salvatore, G. Sgroi, G. Parasiliti Palumbo, P. A. Reche, F. Pappalardo, A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets, accepted for publication in Briefing of Bioinformatics


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