Assigning protein function from domain-function associations using DomFun

Background Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. Results We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer’s method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of $$F_{max}$$ F max and $$S_{min}$$ S min We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer’s method led to the top performance in almost all scenarios. Conclusions DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer’s method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun. Code maintained at https://github.com/ElenaRojano/DomFun. Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project.

Holistic evaluation of biodegradation pathway prediction: assessing multi-step reactions and intermediate products

The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental sciences, drug discovery, and (eco)toxicology. Several systems predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their performance is commonly evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are caused by specific properties of biotransformation experiments. That is, missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rule-based prediction systems evaluate the performance only based on the defined set of transformation rules. Therefore, the performance of these models cannot be directly compared. In this paper, we introduce a new evaluation framework that extends the evaluation of biotransformation prediction from single transformations to whole pathways, taking into account multiple generations of metabolites. We introduce a procedure to address transient intermediates and propose a weighted scoring system that acknowledges the uncertainty of higher-generation metabolites. We implemented this framework in enviPath and demonstrate its strict performance metrics on predictions of in vitro biotransformation and degradation of xenobiotics in soil. Our approach is model-agnostic and can be transferred to other prediction systems. It is also capable of revealing knowledge gaps in terms of incompletely defined sets of transformation rules.

The relationship between mucosal microbiota, colitis and systemic inflammation in Chronic Granulomatous Disorder

Background: Chronic granulomatous disorder (CGD) is a primary immunodeficiency which is frequently complicated by an inflammatory colitis and is associated with systemic inflammation. Objective: To investigate the role of the microbiome in the pathogenesis of colitis and systemic inflammation. Methods: We performed 16S rDNA sequencing on mucosal biopsy samples from each segment of 10 CGD patients colons, and conducted compositional and functional pathway prediction analyses. Results: The microbiota in samples from colitis patients demonstrated reduced taxonomic alpha diversity compared to unaffected patients, even in apparently normal bowel segments. Functional pathway richness was similar between the colitic and non-colitic mucosa, although metabolic pathways involved in butyrate biosynthesis or utilisation were enriched in patients with colitis and correlated positively with faecal calprotectin levels. One patient with very severe colitis was dominated by Enterococcus spp., while among other patients Bacteroides spp. abundance correlated with colitis severity measured by faecal calprotectin and an endoscopic severity score. In contrast, Blautia abundance associated with low severity scores and mucosal health. Several taxa and functional pathways correlated with concentrations of inflammatory cytokines in blood but not with colitis severity. Notably, dividing patients into High and Low systemic inflammation groups demonstrated clearer separation than on the basis of colitis status in beta diversity analyses. Conclusion: The microbiome is abnormal in CGD-associated colitis and altered functional characteristics probably contribute to pathogenesis. Furthermore, the relationship between the mucosal microbiome and systemic inflammation, independent of colitis status, implies that the microbiome in CGD can influence the inflammatory phenotype of the condition.

Engineering cellular metabolite transport for biosynthesis of computationally predicted tropane alkaloid derivatives in yeast

Microbial biosynthesis of plant natural products (PNPs) can facilitate access to valuable medicinal compounds and derivatives. Such efforts are challenged by metabolite transport limitations, which arise when complex plant pathways distributed across organelles and tissues are reconstructed in unicellular hosts without concomitant transport machinery. We recently reported an engineered yeast platform for production of the tropane alkaloid (TA) drugs hyoscyamine and scopolamine, in which product accumulation is limited by vacuolar transport. Here, we demonstrate that alleviation of transport limitations at multiple steps in an engineered pathway enables increased production of TAs and screening of useful derivatives. We first show that supervised classifier models trained on a tissue-delineated transcriptome from the TA-producing plant Atropa belladonna can predict TA transporters with greater efficacy than conventional regression- and clustering-based approaches. We demonstrate that two of the identified transporters, AbPUP1 and AbLP1, increase TA production in engineered yeast by facilitating vacuolar export and cellular reuptake of littorine and hyoscyamine. We incorporate four different plant transporters, cofactor regeneration mechanisms, and optimized growth conditions into our yeast platform to achieve improvements in de novo hyoscyamine and scopolamine production of over 100-fold (480 μg/L) and 7-fold (172 μg/L). Finally, we leverage computational tools for biosynthetic pathway prediction to produce two different classes of TA derivatives, nortropane alkaloids and tropane N-oxides, from simple precursors. Our work highlights the importance of cellular transport optimization in recapitulating complex PNP biosyntheses in microbial hosts and illustrates the utility of computational methods for gene discovery and expansion of heterologous biosynthetic diversity.

Holistic Evaluation of Biodegradation Pathway Prediction: Assessing Multi-Step Reactions and Intermediate Products

The prediction of metabolism and biotransformation pathways of xenobiotics is a highly desired tool in environmental and life sciences. There are several systems that currently predict single transformation steps or complete pathways as series of parallel and subsequent steps. Their accuracy is often evaluated on the level of a single transformation step. Such an approach cannot account for some specific challenges that are related to the nature of the biotransformation experiments. This is particularly true for missing transformation products in the reference data that occur only in low concentrations, e.g. transient intermediates or higher-generation metabolites. Furthermore, some rulebased prediction systems evaluate accuracy only based on the defined set of transformation rules. Therefore, the performance of different models cannot be directly compared.