This R package complements the performance evaluation analysis for the manuscript entitled "Identifying novel functional linear motifs using the host-viral protein interaction network and the principle of convergent evolution".
This repository contains all the materials needed to reproduce Wadie, Bishoy, et al. "Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs." bioRxiv (2021). These materials are presented as an R Package which contains code used for analyses, code used to develop figures, raw data used for all analyses, and a set of functions for handling de-nove short linear motif predictions based on [SLiMSuite](https://github.com/slimsuite/SLiMSuite) tools.
You can find our bioarxiv preprint. [Here](https://www.biorxiv.org/content/10.1101/2021.06.25.449930v1.full).
## Abstract
Linear motifs have an integral role in dynamic cell functions including cell signalling, the cell cycle and others. However, due to their small size, low complexity, degenerate nature, and frequent mutations, identifying novel functional motifs is a challenging task. Viral proteins rely extensively on the molecular mimicry of cellular linear motifs for modifying cell signalling and other processes in ways that favour viral infection. This study aims to discover human linear motifs convergently evolved also in disordered regions of viral proteins, under the hypothesis that these will result in enrichment in functional motif instances. We systematically apply computational motif prediction, combined with implementation of several functional and structural filters to the most recent publicly available human-viral and human-human protein interaction network. By limiting the search space to the sequences of viral proteins, we observed an increase in the sensitivity of motif prediction, as well as improved enrichment in known instances compared to the same analysis using only human protein interactions. We identified > 7,300 motif instances at various confidence levels, 105 of which were supported by all functional and structural filters applied. Overall, we provide a pipeline to improve the identification of functional linear motifs from interactomics datasets and a comprehensive catalogue of putative human motifs that can contribute to our understanding of the human domain-linear motif code and the mechanisms of viral interference with this.
## Installation
You can install the released version of HVSlimPred from gitlab with:
For protein-level enrichment, we measured the enrichment of true-positives in our predicted dataset using a one-tailed fisher-exact test, where the odds ratio represents the magnitude of the enrichment. True-positives are the number of motif-carrying proteins present in both the predicted dataset and the ELM dataset regardless of whether the predicted protein has the right motif or found in the right location
To reproduce the same results and figures as in the manuscript, it is recommended to clone the repository and run the analysis scripts in the [analysis](https://gitlab.ebi.ac.uk/petsalakilab/HVSlimPred/-/tree/master/analysis) folder locally.
The output of the following command is a data frame containing all the relevant protein-level performance metrics for each domain enrichment filter in addition to the non-filtered qslim output.
For motif-level enrichment, we simply cannot use a binary classification as we did for protein-level evaluation because in reality, predicted motifs are partially correct to some extent as they might contain true-positive residues in a given sequence stretch, and therefore we used a re-implemented version of the evaluation protocol proposed in [Prytuliak et al. 2017](https://academic.oup.com/nar/article/45/W1/W470/3782606) instead of binary classification, where we computed the common performance metrics (Recall, precision F1, etc .. ) both residue-wise and site-wise given that the motif-carrying proteins are also found in the ELM benchmarking dataset. So this analysis was not performed on proteins not reported in the ELM dataset.
The output of the following command is a data frame containing all the relevant motif-level performance metrics for each domain enrichment filter in addition to the non-filtered qslim output.
```{r Motif-level evaluation, eval=FALSE}
library(HVSlimPred)
motif_eval_metrics = HV_motif_level_eval()
```
## Get Protein-domain interactions evaluation metrics
For evaluating protein-domain interactions we measured the enrichment of true-positive interactions between a given motif-carrying protein and its associated domains as reported in the ELM interaction dataset. As in the motif-level evaluation, this analysis was performed only on the motif-carrying proteins reported in the ELM interactions dataset, where true-positives represents the number of correctly associated domains for a given motif-carrying protein and then summed over all motif-carrying proteins in the predicted dataset.
The output of the following command is a data frame containing all the relevant protein-domain interactions' performance metrics for each domain enrichment filter.
