Integrating protein annotations for the in silico prioritization of putative drug target proteins in malaria

Current anti-malarial methods have been effective in reducing the number of malarial cases. However, these methods do not completely block the transmission of the parasite. Research has shown that repeated use of the current anti-malarial drugs, which include artemisinin-based drug combinations, might be toxic to humans. There have also been reports of an emergence of artemisinin-resistant parasites. Finding anti-malarial drugs through the drug discovery process takes a long time and failure results in a great financial loss. The failure of drug discovery projects can be partly attributed to the improper selection of drug targets. There is thus a need for an eff ective way of identifying and validating new potential malaria drug targets for entry into the drug discovery process. The availability of the genome sequences for the Plasmodium parasite, human host and the Anopheles mosquito vector has facilitated post-genomic studies on malaria. Proper utilizationof this data, in combination with computational biology and bioinformatics techniques, could aid in the in silico prioritization of drug targets. This study was aimed at extensively annotating the protein sequences from the Plasmodium parasites, H. sapiens and A. gambiae with data from di fferent online databases in order to create a resource for the prioritization of drug targets in malaria. Essentiality, assay feasibility, resistance, toxicity, structural information and druggability were the main target selection criteria which were used to collect data for protein annotations. The data was used to populate the Discovery resource (http://malport. bi.up.ac.za/) for the in silico prioritization of potential drug targets. A new version of the Discovery system, Discovery 2.0 (http://discovery.bi.up.ac.za/), has been developed using Java. The system contains new and automatically updated data as well as improved functionalities. The new data in Discovery 2.0 includes UniProt accessions, gene ontology annotations from the UniProt-GOA project, pathways from Reactome and Malaria Parasite Metabolic Pathways databases, protein-protein interactions data from. IntAct as well as druggability data from the DrugEBIlity resource hosted by ChEMBL. Users can access the data by searching with a protein identi er, UniProt accession, protein name or through the advanced search which lets users filter protein sequences based on different protein properties. The results are organized in a tabbed environment, with each tab displaying different protein annotation data. A sample investigation using a previously proposed malarial target, S-adenosyl-Lhomocysteine hydrolase, was carried out to demonstrate the diff erent categories of data available in Discovery 2.0 as well as to test if the available data is su fficient for assessment and prioritization of drug targets. The study showed that using the annotation data in Discovery 2.0, a protein can be assessed, in a species comparative manner, on the potential of being a drug target based on the selection criteria mentioned here. However, supporting data from literature is also needed to further validate the findings.Dissertation (MSc)--University of Pretoria, 2012.Biochemistryunrestricte

Multimorbidity in African ancestry populations: a scoping review

Objectives Multimorbidity (MM) is a growing concern linked to poor outcomes and higher healthcare costs. While most MM research targets European ancestry populations, the prevalence and patterns in African ancestry groups remain underexplored. This study aimed to identify and summarise the available literature on MM in populations with African ancestry, on the continent, and in the diaspora.Design A scoping review was conducted in five databases (PubMed, Web of Science, Scopus, Science Direct and JSTOR) in July 2022. Studies were selected based on predefined criteria, with data extraction focusing on methodology and findings. Descriptive statistics summarised the data, and a narrative synthesis highlighted key themes.Results Of the 232 publications on MM in African-ancestry groups from 2010 to June 2022—113 examined continental African populations, 100 the diaspora and 19 both. Findings revealed diverse MM patterns within and beyond continental Africa. Cardiovascular and metabolic diseases are predominant in both groups (80% continental and 70% diaspora). Infectious diseases featured more in continental studies (58% continental and 16% diaspora). Although many papers did not specifically address these features, as in previous studies, older age, being women and having a lower socioeconomic status were associated with a higher prevalence of MM, with important exceptions. Research gaps identified included limited data on African-ancestry individuals, inadequate representation, under-represented disease groups, non-standardised methodologies, the need for innovative data strategies, and insufficient translational research.Conclusion The growing global MM prevalence is mirrored in African-ancestry populations. Recognising the unique contexts of African-ancestry populations is essential when addressing the burden of MM. This review emphasises the need for additional research to guide and enhance healthcare approaches for African-ancestry populations, regardless of their geographic location