hmm_SepDim

This feature calculates the probabilistic expression of amino acid dimers that are spatially separated by a distance \(l\).
Mathematically, this is done with a 20 x 20 matrix \(F\), in which \(F[m, n] = \sum_{i = 1}^{L-l} H_{i, m}H_{i+k, n}\).
\(H\) corresponds to the original HMM matrix, and \(L\) is the number of rows in \(H\). Matrix \(F\) is then flattened to
a feature vector of length 400, and returned.

Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins.
Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take
advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012)
<DOI:10.1038/nmeth.1818> and 'HMMer' Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by
this package can be used for protein fold classification, protein structural class prediction,
sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features
extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>,
Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.

Shayaan Emran

protHMM

Protein Feature Extraction from Profile Hidden Markov Models

hmm_SepDim function

<dl><dt>hmm</dt>
<dd>The name of a profile hidden markov model file.</dd>
<dt>l</dt>
<dd>Spatial distance between dimer residues.</dd></dl>

Arguments

hmm_SepDim — hmm_SepDim

<dl>

<dt>hmm</dt>
<dd>The name of a profile hidden markov model file.</dd>


<dt>l</dt>
<dd>Spatial distance between dimer residues.</dd>

</dl>

hmm_SepDim: hmm_SepDim

Description

Usage

Value

Arguments

References

Examples