prothmm

https://github.com/semran9/prothmm

Repository

https://github.com/semran9/protHMM/blob/main/

# protHMM



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## Summary

The goal of protHMM is to help integrate profile hidden markov model
(HMM) representations of proteins into the machine learning and
bioinformatics workflow. protHMM ports a number of features from use in
Position Specific Scoring Matrices (PSSMs) to HMMs, along with
implementing features used with HMMs specifically, which to our
knowledge has not been done before. The adoption of HMM representations
of proteins derived from [HHblits](https://doi.org/10.1038/nmeth.1818)
and [HMMer](http://hmmer.org) also presents an opportunity for
innovation; it has been shown that HMMs can benefit from better multiple
sequence alignment than PSSMs and thus get better results than
corresponding HMMs using similar feature extraction techniques (Lyons et
al.2015). protHMM implements 20 different feature extraction techniques
to provide a comprehensive list of feature sets for use in
bioinformatics tasks ranging from protein fold classification to
protein-protein interaction.

#### Installation

You can install the development version of protHMM from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("semran9/protHMM")
```

protHMM can also be installed from CRAN:

``` r
install.packages("protHMM", repos = "http://cran.us.r-project.org")
#> 
#> The downloaded binary packages are in
#>  /var/folders/2z/7q42mmyn5f18vx697sj_30p40000gn/T//RtmpKmgX8K/downloaded_packages
```

## Functions List

A more comprehensive list of functions and the calculations behind the
functions can be found
[here](https://semran9.github.io/protHMM/articles/protHMM.html).

hmm_ac()

hmm_bigrams()

hmm_cc()

chmm()

hmm_distance()

fp_hmm()

hmm_GA()

hmm_GSD()

IM_psehmm()

hmm_LBP()

hmm_LPC()

hmm_MA()

hmm_MB()

pse_hmm()

hmm_read()

hmm_SCSH()

hmm_SepDim()

hmm_Single_Average()

hmm_smooth()

hmm_svd()

hmm_trigrams()

## Example

``` r
## this shows the functionality of hmm_distance, which calculates a similarity score between two proteins
## other functions are documented fully in the protHMM vignette
library(protHMM)
## these proteins are from the same fold and similar; h should be low
h <- hmm_distance(system.file("extdata", "1DLHA2-7", package="protHMM"), system.file("extdata", "1TEN-7", package="protHMM"))
## these proteins are from different folds and not similar; h_2 should be high
h_2<- hmm_distance(system.file("extdata", "1DLHA2-7", package="protHMM"), system.file("extdata", "1TAHA-23", package="protHMM"))
h < h_2
#> [1] TRUE
```

## References

Lyons, J., Dehzangi, A., Heffernan, R., Yang, Y., Zhou, Y., Sharma, A.,
& Paliwal, K. K. (2015). Advancing the Accuracy of Protein Fold
Recognition by Utilizing Profiles From Hidden Markov Models. IEEE
Transactions on Nanobioscience, 14(7), 761772.

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