Annotation of the Giardia proteome through structure-based homology and machine learning

Background: Large-scale computational prediction of protein structures represents a cost-effective alternative to empirical structure determination with particular promise for non-model organisms and neglected pathogens. Conventional sequence-based tools are insufficient to annotate the genomes of such divergent biological systems. Conversely, protein structure tolerates substantial variation in primary amino acid sequence and is thus a robust indicator of biochemical function. Structural proteomics is poised to become a standard part of pathogen genomics research; however, informatic methods are now required to assign confidence in large volumes of predicted structures. Aims: Our aim was to predict the proteome of a neglected human pathogen, Giardia duodenalis, and stratify predicted structures into high- and lower-confidence categories using a variety of metrics in isolation and combination. Methods: We used the I-TASSER suite to predict structural models for ∼5,000 proteins encoded in G. duodenalis and identify their closest empirically-determined structural homologues in the Protein Data Bank. Models were assigned to high- or lower-confidence categories depending on the presence of matching protein family (Pfam) domains in query and reference peptides. Metrics output from the suite and derived metrics were assessed for their ability to predict the high-confidence category individually, and in combination through development of a random forest classifier. Results: We identified 1,095 high-confidence models including 212 hypothetical proteins. Amino acid identity between query and reference peptides was the greatest individual predictor of high-confidence status; however, the random forest classifier outperformed any metric in isolation (area under the receiver operating characteristic curve = 0.976) and identified a subset of 305 high-confidence-like models, corresponding to false-positive predictions. High-confidence models exhibited greater transcriptional abundance, and the classifier generalized across species, indicating the broad utility of this approach for automatically stratifying predicted structures. Additional structure-based clustering was used to cross-check confidence predictions in an expanded family of Nek kinases. Several high-confidence-like proteins yielded substantial new insight into mechanisms of redox balance in G. duodenalis-a system central to the efficacy of limited anti-giardial drugs. Conclusion: Structural proteomics combined with machine learning can aid genome annotation for genetically divergent organisms, including human pathogens, and stratify predicted structures to promote efficient allocation of limited resources for experimental investigation

TriTOX: A novel Trichomonas vaginalis assay platform for high-throughput screening of compound libraries

Trichomonas vaginalis is a neglected urogenital parasitic protist that causes 170 million cases of trichomoniasis annually, making it the most prevalent non-viral, sexually transmitted disease. Trichomoniasis treatment relies on nitroheterocyclics, such as metronidazole. However, with increasing drug-resistance, there is an urgent need for novel anti-trichomonals. Little progress has been made to translate anti-trichomonal research into commercialised therapeutics, and the absence of a standardised compound-screening platform is the immediate stumbling block for drug-discovery. Herein, we describe a simple, cost-effective growth assay for T. vaginalis and the related Tritrichomonas foetus. Tracking changes in pH were a valid indicator of trichomonad growth (T. vaginalis and T. foetus), allowing development of a miniaturised, chromogenic growth assay based on the phenol red indicator in 96- and 384-well microtiter plate formats. The outputs of this assay can be quantitatively and qualitatively assessed, with consistent dynamic ranges based on Z' values of 0.741 and 0.870 across medium- and high-throughput formats, respectively. We applied this high-throughput format within the largest pure-compound microbial metabolite screen (812 compounds) for T. vaginalis and identified 43 hit compounds. We compared these identified compounds to mammalian cell lines, and highlighted extensive overlaps between anti-trichomonal and anti-tumour activity. Lastly, observing nanomolar inhibition of T. vaginalis by fumagillin, and noting this compound has reported activity in other protists, we performed in silico analyses of the interaction of fumagillin with its molecular target methionine aminopeptidase 2 for T. vaginalis, Giardia lamblia and Entamoeba histolytica, highlighting potential for fumagillin as a broad-spectrum anti-protistal against microaerophilic protists. Together, this new platform will accelerate drug-discovery efforts, underpin drug-resistance screening in trichomonads, and contributing to a growing body of evidence highlighting the potential of microbial natural products as novel anti-protistals

Eukaryote-ConservedMethylarginine Is Absent in Diplomonads and Functionally Compensated in Giardia

Methylation is a common posttranslational modification of arginine and lysine in eukaryotic proteins. Methylproteomes are best characterized for higher eukaryotes, where they are functionally expanded and evolved complex regulation. However, this is not the case for protist species evolved from the earliest eukaryotic lineages. Here, we integrated bioinformatic, proteomic, and drug-screening data sets to comprehensively explore the methylproteome of Giardia duodenalis-a deeply branching parasitic protist. We demonstrate that Giardia and related diplomonads lack arginine-methyltransferases and have remodeled conserved RGG/RG motifs targeted by these enzymes. We also provide experimental evidence for methylarginine absence in proteomes of Giardia but readily detect methyllysine. We bioinformatically infer 11 lysine-methyltransferases in Giardia, including highly diverged Su(var)3-9, Enhancer-of-zeste and Trithorax proteins with reduced domain architectures, and novel annotations demonstrating conserved methyllysine regulation of eukaryotic elongation factor 1 alpha. Using mass spectrometry, we identify more than 200 methyllysine sites in Giardia, including in species-specific gene families involved in cytoskeletal regulation, enriched in coiled-coil features. Finally, we use known methylation inhibitors to show that methylation plays key roles in replication and cyst formation in this parasite. This study highlights reduced methylation enzymes, sites, and functions early in eukaryote evolution, including absent methylarginine networks in the Diplomonadida. These results challenge the view that arginine methylation is eukaryote conserved and demonstrate that functional compensation of methylarginine was possible preceding expansion and diversification of these key networks in higher eukaryotes