---
title: "IUPred Vignette"
author: "William McFadden"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{IUPred Vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 6, 
  fig.height = 4
)
```


# Fetching IUPred Predictions of Intrinsic Disorder


## Quick Start

The functions iupred(), iupredAnchor(), and iupredRedox() are all 
designed to fetch predictions of intrinsic disorder from the IUPred2A REST API. 
To fetch results, a UniProt ID is needed. 

Predictions are made on a scale of 0-1, where any residues with a score over 
0.5 are predicted to be in a disordered region, and any residue scoring below 
0.5 are predicted to be ordered.

iupredAnchor() and iupredRedox() provide contet-dependent predictions.

```{r}
library(idpr) #Attach the package

p53_ID <- "P04637"
iupred(p53_ID,
       proteinName = "HUMAN P53")
```

**If you use any iupred-based function, please cite the appropriate articles.**
The following are the most recent papers (as of 6/2020)

* [Mészároset al. (2018)](https://doi.org/10.1093/nar/gky384)
* [Erdős & Dosztányi (2020)](https://doi.org/10.1002/cpbi.99)

## Background
The primary structure of a protein, also known as the amino acid sequence, 
can be an accurate predictor of protein folding. Because of this, many different
tools have been developed to make probabilistic predictions of intrinsic 
disorder based on various known properties of Intrinsically 
Disordered Proteins (IDPs) (Li et al., 2015). 

Some examples of these come from IUPred2A. 
IUPred2 analyzes an amino acid sequence and returns a score of 
intrinsic disorder depending on a model of the estimated energy potential for 
residue interactions (Mészáros, Erdős, & Dosztányi, 2018). This is because 
structured proteins have the ability to create a network of interactions, while
IDPs lack abundant interactions. The reduced number of interactions leads to an 
IDP's lack of secondary and tertiary structure (Dosztányi, 2018). 

Predictions are made on a scale of 0-1, where any residues with a score over 0.5 
are predicted to be in a disordered region, and any residue scoring below 0.5
are predicted to be ordered.

The IUPred2A website is located at https://iupred2a.elte.hu/. 
For detailed information on using IUPred2A, please refer to Erdős & Dosztányi
(2020) “Analyzing protein disorder with IUPred2A”. Current Protocols in
Bioinformatics, 70, e99. 
Additionally, please see Mészáros et al. (2019) for further information, 
theory, and applications of IUPred2A.

All iupred functions in **idpr** make a connection to the IUPred2A REST API 
based on the type of analysis and UniProt accession number within the function’s
arguments. To make this connection, the user must provide the UniProt
accession number for their protein of interest (UniProt Consortium, 2019). 
Additionally, the user will need a connection to the internet. 
The results are then formatted to match output of other **idpr** functions. 

The results for all predictions fetched can be represented graphically, 
with plotResults = TRUE (default) or as a data frame with plotResults = FALSE.
Both type of results will be shown for examples. 


## Installation  

The idpr package can be installed from Bioconductor with the following line of 
code. It requires the BiocManager package to be installed.
```{r}
#BiocManager::install("idpr") 
```

The most recent version of the package can be installed with the following line 
of code. This requires the devtools package to be installed.

```{r}
#devtools::install_github("wmm27/idpr")
```


## iupred function and iupredType arguments.

iupred() has is the core prediction of intrinsic disorder.
The argument iupredType =  is important to specify depending on the 
goal of the analysis. 

plotResults = FALSE returns a data frame with 3 columns. 
The first column is "Position", which indicates the numeric position of the 
residue in the submitted sequence. 
The second column is "AA", which indicates the amino acid residue as a single
letter. 
The third column is  "IUPred2", which indicates the prediction of intrinsic 
disorder by IUPred2. 

```{r}
library(idpr) #Attach the package
```


### iupredType = "long"
iupredType =  “long” is the default setting, and the setting that is recommended 
for predicting intrinsic disorder in proteins (Dosztányi, 2018). 
This predits relevant disordered segments like those
curated within the DisProt Database (Dosztányi, 2018; Hatos et al., 2019). 

```{r}
p53_ID <- "P04637"
iupred(p53_ID,
       proteinName = "HUMAN P53",
       iupredType = "long")
```


```{r}
iupredLongDF <- iupred(p53_ID,
                       proteinName = "HUMAN P53",
                       iupredType = "long",
                       plotResults = FALSE)
head(iupredLongDF)
```

### iupredType = "short"
iupredType =  “short” is the setting to predict small regions of intrinsic
disorder in proteins, optimized for missing regions of protein structures saved 
to the Protein Databank (PDB). Its goal is to predict 
regions that are not represented in crystallographic experiments.
It is important to note that this tends to favor
disorder at the N- and C- terminus (Dosztányi, 2018). 

```{r}
p53_ID <- "P04637"
iupred(p53_ID,
       proteinName = "HUMAN P53",
       iupredType = "short")
```

```{r}
iupredShortDF <- iupred(p53_ID,
                        iupredType = "short",
                        plotResults = FALSE)
head(iupredShortDF)
```

### iupredType = "glob"
iupredType =  “glob” is the setting that is to help reduce the noise of small 
disordered regions in otherwise ordered regions and to help identify sequences 
that are likely to have a specific and rigid fold. 
(Dosztányi, 2018). 
```{r}
p53_ID <- "P04637"
iupred(p53_ID,
       proteinName = "HUMAN P53",
       iupredType = "glob")
```


```{r}
iupredGlobDF <- iupred(p53_ID,
                       iupredType = "glob",
                       plotResults = FALSE)
head(iupredGlobDF)
```


-----------

## iupredAnchor
IDPs and IDRs serve many important roles in a cell, one prominent role is the
ability to act as a hub for protein-protein interactions (Uversky, 2013). 
Additionally, many disordered regions undergo what is known as 
“induced folding”. This is a phenomenon where under native conditions the IDP is 
unstructured, however when entering a specific environment, such as those that
occur when binding to other proteins, higher-order structures may form and allow
the IDP to execute its function (Kovacs, Szabo, Pancsa, & Tompa, 2013). It is 
important to note that not all IDPs experience induced folding. 

ANCHOR2 is a context-dependent predictor of binding regions for protein-protein
interactions (Mészáros et al., 2018). Similarly to IUPred2, ANCHOR2 gives a
score of 0-1 indicating if a region is predicted to be involved in protein-protein 
interactions. 

iupredAnchor() is used to combine the output of IUPred2 long (plot is the
same as shown prior) with ANCHOR2 predictions (shown as a maroon line).
```{r}
p53_ID <- "P04637"
iupredAnchor(p53_ID,
             proteinName = "HUMAN P53")
```

The data frame for iupredAnchor has a similar layout to iupred(),
with an additional column for ANCHOR2 scores.
```{r}
iupredAnchorDF <- iupredAnchor(p53_ID, 
                               plotResults = FALSE)
head(iupredAnchorDF)
```

## iupredRedox
Another factor influencing the environmental chemistry is the redox potential. 
As mentioned before, under native conditions IDPs are unstructured, however when 
entering a different environment higher-order structures may form and allow IDPs
to execute their function (Kovacs et al., 2013).

iupredRedox() is used to predict redox-sensitive regions that may experience 
induced folding upon changing environments. This is a context-dependent 
predictor of disordered regions depending on a reducing (plus) or 
oxidizing (minus) environment. The prediction is done by replacing all 
cystine residues to serine when simulating a reducing or
“redox-plus” environment.
This eliminates any structural stabilization by disulfide bonds 
(Mészáros et al., 2018).

Redox-plus predictions are shown in blue, Redox-minus predictions are shown
in purple. Any region identified as "Redox Sensitive" will be highlighted in
light green (does not appear if there are no sensitive regions predicted).
```{r}
p53_ID <- "P04637"
iupredRedox(p53_ID,
             proteinName = "HUMAN P53")
```

The data frame has two IUPred2 long scores. One in a redox-plus environment
(Cys --> Ser) and a redox-minus environment (standard prediction). 
An additional column is provided of logical values indicating if a redox 
sensitive region was predicted. When redoxSensitive == TRUE, the residue is 
predicted to be in a redox sensitive region, when FALSE the residue is not 
predicted to be in a redox sensitive region. 
```{r}
iupredRedoxDF <- iupredRedox(p53_ID, 
                             plotResults = FALSE)
head(iupredRedoxDF)
```

## Additional Example


While the aesthetics of the plots above are meant to represent a middleground of
the graphics available on 
and the other plots generated by **idpr**, a user may wish to use the data
frames for data analysis or unique graphics. Another way to represent the data
is using the sequenceMap() function.

```{r}
iupredLongDF <- iupred(p53_ID,
                       proteinName = "HUMAN P53",
                       iupredType = "long",
                       plotResults = FALSE)

sequenceMap(sequence = iupredLongDF$AA,
            property = iupredLongDF$IUPred2,
            customColors = c("darkolivegreen3", "grey65", "darkorchid1")) + 
  ggplot2::labs(title = "Prediction of Intrinsic Disorder in HUMAN P53",
                subtitle = "By IUPred2A long")


                         
```


**For further details, please refer to idpr's **
**"Sequence Map Vignette" file.**


## Getting the UniProt Accession

To make a connection to the IUPred2A REST API, a UniProt Accession ID is 
required. 
If a user does not have the ID, it is reccomended to first search for it
via the UniProt website at https://www.uniprot.org/ .
If a user does not have the protein name or info to search, a BLAST search on
UniProt may be helpful at https://www.uniprot.org/blast/ 
(UniProt Consortium, 2019).

## Use
Please note that these functions are only meant to access the IUPred2A REST API. 
The functions within **idpr** are **not** designed by the IUPred2A developers. 
The authors of **idpr** do not control, manage, or maintain any 
aspect of IUPred2A. Therefore, **idpr** is unable to guarantee access to 
the API.


The user MUST follow the IUPred2A Terms of Use in addition to the terms
for use of **idpr**.

When publishing or using any data generated with IUPred2A, the user must cite the 
appropriate publication(s) for the IUPred2A service. This may change as the
program updates or improves. **idpr** does not control updates to IUPred2A.


The current website (as of 10/15/20) for IUPred2A is found here:
[https://iupred2a.elte.hu/](https://iupred2a.elte.hu/). 
The authors of **idpr** strongly recommend visiting this page to follow any
updates and changes as well as confirming appropriate use per the IUPred2A
terms of use. 


## References

Dosztányi, Z. (2018). Prediction of protein disorder based on IUPred. 
Protein Sci, 27(1), 331-340. doi:10.1002/pro.3334

Erdős, G., & Dosztányi, Z. (2020). Analyzing protein disorder with IUPred2A. 
Current Protocols in Bioinformatics, 70, e99. https://doi.org/10.1002/cpbi.99

Hatos, A., Hajdu-Soltész, B., Monzon, A. M., Palopoli, N., Álvarez, L., 
Aykac-Fas, B., . . . Piovesan, D. (2019). DisProt: intrinsic protein disorder
annotation in 2020. Nucleic acids research, 48(D1), D269-D276. 
doi:10.1093/nar/gkz975


Kovacs, D., Szabo, B., Pancsa, R., & Tompa, P. (2013).
Intrinsically disordered proteins undergo and assist folding transitions in 
the proteome. Archives of Biochemistry and Biophysics, 531(1), 80-89. 
doi:https://doi.org/10.1016/j.abb.2012.09.010


Li, J., Feng, Y., Wang, X., Li, J., Liu, W., Rong, L., & Bao, J. (2015).
An Overview of Predictors for Intrinsically Disordered Proteins over 2010-2014.
International journal of molecular sciences, 16(10), 23446-23462. 
doi:10.3390/ijms161023446


Mészáros, B., Erdős, G., & Dosztányi, Z. (2018). IUPred2A: 
context-dependent prediction of protein disorder as a function
of redox state and protein binding. Nucleic acids research, 46(W1), W329-W337. 


UniProt Consortium. (2019). UniProt: a worldwide hub of protein knowledge. 
Nucleic acids research, 47(D1), D506-D515. 


Uversky, V. N. (2013). Unusual biophysics of intrinsically disordered proteins.
Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, 1834(5), 932-951.
doi:https://doi.org/10.1016/j.bbapap.2012.12.008




```{r}
citation("ggplot2")
```


### Additional Information
R Version
```{r}
R.version.string
```

System Information
```{r}
as.data.frame(Sys.info())
```

```{r, results="asis"}
citation()
```
