NAR Genomics and BioinformaticsBioinformaticsPeer-reviewed journal article
Distinct Repeat Architecture Landscapes in the Proteomes of Protozoan Parasites
Abstract
Protozoan parasites cause major infectious diseases and pose persistent challenges to global health, particularly the emergence of drug-resistant strains. Tandem repeats (TRs) and other repetitive architectures are widespread in proteomes, especially in protozoan proteins, where they have been implicated in host-parasite interactions, immune evasion, and antigenicity. However, repeat-containing proteins (RPs) exhibit highly diverse architectures that often extend beyond the simple reiteration of a single motif, making comprehensive and quantitative characterization challenging. In this study, we performed bioinformatics analysis of repeat architectures in protozoan proteins. In addition to the established repeat-detection approaches, we developed a new algorithm, Drepper, which quantifies repeat-architecture complexity. By integrating diverse repeat-related features, we clustered RPs across species and identified distinct groups associated with parasite lineages. Notably, we detected a Plasmodium-specific RP cluster and a Trypanosoma/Leishmania-specific RP cluster; both were characterized by large repeat regions but exhibited contrasting repeat-structure complexity. The Plasmodium-specific RPs showed high complexity, whereas the Trypanosoma/Leishmania-specific RPs displayed significantly low complexity. Functional enrichment analyses indicated that these lineage-associated clusters were enriched in parasite-specific factors. Furthermore, evolutionary analyses suggested that low-complexity repeat architectures may be actively maintained through concerted evolution. Taken together, our results reveal lineage-specific strategies in protozoan repeat architectures and provide a quantitative framework for studying their biological and evolutionary roles.
- assay: proteomics
- research focus: clustering
- research focus: gene regulatory network inference
- research focus: benchmarking
- research focus: database / resource
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 38th percentile
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