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

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


## Background
The composition of amino acids and the overall chemistry of Intrinsically
Disordered Proteins (IDPs) are distinctly different from that of ordered 
proteins. Each amino acid has a tendency to favor a compact or extended 
secondary and tertiary structures based on the chemistry of the residue.
Disorder-promoting residues, those enriched in IDPs, are typically hydrophilic, 
charged, or small. Order promoting residues, those enriched in 
structured proteins, tend to be aliphatic, hydrophobic, aromatic, or form
tertiary structures. Disorder neutral residues neither favor order or disordered
structures (Uversky, 2019).  


Disorder-promoting residues are P, E, S, Q, K, A, and G; 
order-promoting residues are M, N, V, H, L, F, Y, I, W, and C; 
disorder‐neutral residues are D, T, and R (Uversky, 2013).  



## Installation  

The 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")
```

## Quick-use guide

```{r setup}
library(idpr)
```


The structuralTendency function is used to convert an amino acid sequence
into a data frame with each residue within the sequence in one column and the
corresponding structural tendency in another column.

This example will use the *H. sapiens* TP53 sequence, acquired from UniProt 
(UniProt Consortium 2019)
and stored within the **idpr** package for examples.

```{r}
P53_HUMAN <- TP53Sequences[2]
print(P53_HUMAN)
```

```{r}
tendencyDF <- structuralTendency(P53_HUMAN)
head(tendencyDF)
```


For convenient plotting, use structuralTendencyPlot().
Results can be as a pie chart or bar plot. 

```{r}
structuralTendencyPlot(P53_HUMAN)

structuralTendencyPlot(P53_HUMAN,
                       graphType = "bar")
```





## structuralTendency In Detail

Like most functions in **idpr**, the structuralTendency function will accept an
amino acid sequence as a vector of single-letter residues or as a character 
string. It will also accept the path to a .fasta file as a character string 
(".fasta" must be included within the string).

It will result in 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  "Tendency", which indicates the structural 
tendency of the residue that was matched by the function. 

The following examples will use the *M. musculus* Tp53 sequence, 
acquired from UniProt (UniProt Consortium 2019)
and stored within the **idpr** package for examples.
```{r}
P53_MOUSE <- TP53Sequences[1]
print(P53_MOUSE)
```


This will get the data frame.
```{r}
tendencyDF <- structuralTendency(P53_MOUSE)
head(tendencyDF)
```


The data frame can be used for custom plotting. 
Another possibility is the use of the sequenceMap() function within **idpr**. 
```{r}
sequenceMap(
  sequence = tendencyDF$AA,
  property = tendencyDF$Tendency,
  customColors = c("#999999", "#E69F00", "#56B4E9"))  
```

structuralTendency defines order- and disorder-promoting residues based on 
Uversky (2013).

* Disorder-promoting: P, E, S, Q, K, A, and G
* Order-promoting: M, N, V, H, L, F, Y, I, W, and C
* Disorder‐neutral: D, T, and R.  

However, there are other definitions of these categories. For example, 
Dunker et al. (2001) defines these categories as:

* Disorder-promoting: A, R, G, Q, S, P, E, and K
* Order-promoting: W, C, F, I, Y, V, L, and N
* Disorder‐neutral: H, M, T, and D.

structuralTendency allows for the user to specify different definitions. 
An example of this using the Dunker et al. (2001) definitions rather than
the default is below.

```{r}
tendencyDF <- structuralTendency(P53_MOUSE,
                 disorderPromoting = c("A", "R", "G", "Q", "S", "P", "E", "K"),
                 disorderNeutral = c("H", "M", "T", "D"),
                 orderPromoting = c("W", "C", "F", "I", "Y", "V", "L", "N"))
head(tendencyDF)
  
  
sequenceMap(
  sequence = P53_MOUSE,
  property = tendencyDF$Tendency,
  customColors = c("#999999", "#E69F00", "#56B4E9"))    
```



## structuralTendencyPlot In Detail

structuralTendencyPlot is a function for summarizing and plotting results from 
structuralTendency. 
This function accepts the same arguments as structuralTendency()
for sequence and definitions of residue tendency.

The function will get the amino acid composition for the protein and match
it to the tendency definition.

The results can be visualized in the form of a pie chart (default)
or a bar chart. 
```{r}
structuralTendencyPlot(P53_MOUSE)

structuralTendencyPlot(P53_MOUSE,
                       graphType = "bar",
                       proteinName = names(P53_MOUSE))
```


In addition to the compositional profile of each residue, a summary of the 
profile focused only on the structural tendency can be given by setting
summarize = TRUE. This shifts the focus from amino acid identity to the general 
composition. The graphType is preserved.

```{r}
structuralTendencyPlot(P53_MOUSE,
                       summarize = TRUE)

structuralTendencyPlot(P53_MOUSE,
                       graphType = "bar",
                       proteinName = names(P53_MOUSE),
                       summarize = TRUE)
```

In addition to the default plotting, a data frame of the compositional profile
can be produced by setting graphType = "none". This can be used for further data
analysis or for custom plotting. The "summarize" argument is preserved. 

```{r}
compositionDF <- structuralTendencyPlot(P53_MOUSE,
                                        graphType = "none")
head(compositionDF)


summaryDF <- structuralTendencyPlot(P53_MOUSE,
                                    graphType = "none",
                                    summarize = TRUE)
head(summaryDF)
```


## References

### Citations

Dunker, A. K., Lawson, J. D., Brown, C. J., Williams, R. M., Romero, P., 
Oh, J. S., . . . Obradovic, Z. (2001). Intrinsically disordered protein. 
Journal of Molecular Graphics and Modelling, 19(1), 26-59.
doi:https://doi.org/10.1016/S1093-3263(00)00138-8


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


Uversky, V. N. (2013). A decade and a half of protein intrinsic disorder: 
Biology still waits for physics. Protein Science, 22(6), 693-724. 
doi:10.1002/pro.2261


Uversky, V. N. (2019). Intrinsically Disordered Proteins and Their “Mysterious”
(Meta)Physics. Frontiers in Physics, 7(10). doi:10.3389/fphy.2019.00010


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

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

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












