AI Article Synopsis

  • * This study aimed to identify potential protein targets for vaccine candidates and drug development by analyzing genome sequences of four fungal strains using bioinformatics techniques like reverse vaccinology and subtractive genomics.
  • * Four promising protein candidates for vaccine development were identified, along with four natural compounds that may effectively interact with these proteins, offering hope for better treatments and preventive measures against histoplasmosis.

Article Abstract

is a thermodymorphic fungus that causes histoplasmosis, a systemic mycosis that presents different clinical manifestations, ranging from self-limiting to acute lung infection, chronic lung infection and disseminated infection. Usually, it affects severely immunocompromised patients although immunocompetent patients can also be infected. Currently, there are no vaccines to prevent histoplasmosis and the available antifungal treatment presents moderate to high toxicity. Additionally, there are few options of antifungal drugs. Thus, the aim of this study was to predict possible protein targets for the construction of potential vaccine candidates and predict potential drug targets against . Whole genome sequences from four previously published strains were analyzed and submitted to different bioinformatic approaches such as reverse vaccinology and subtractive genomics. A total of four proteins were characterized as good protein candidates (vaccine antigens) for vaccine development, three of which are membrane-bound and one is secreted. In addition, it was possible to predict four cytoplasmic proteins which were classified as good protein candidates and, through molecular docking performed for each identified target, we found four natural compounds that showed favorable interactions with our target proteins. Our study can help in the development of potential vaccines and new drugs that can change the current scenario of the treatment and prevention of histoplasmosis.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963733PMC
http://dx.doi.org/10.3390/jof9020193DOI Listing

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