IM_psehmm

Description

The first twenty numbers of this feature correspond to the means of each column of the HMM matrix H. The rest of the features in the feature vector are found in matrix T[i,j], where T[i,j] = \frac{1}{L-i}\sum_{n = 1}^{20-i} [H_{m,n}-H_{m, n+i}]^2, m = 1:L,\space i = 1:d\space and\space j = 1:20.

Usage

IM_psehmm(hmm, d = 13)

Arguments

Value

A vector of length 20+20\times d-d\times\frac{d+1}{2}

Note

d must be less than 20.

References

Ruan, X., Zhou, D., Nie, R., & Guo, Y. (2020). Predictions of Apoptosis Proteins by Integrating Different Features Based on Improving Pseudo-Position-Specific Scoring Matrix. BioMed Research International, 2020, 1–13.

Examples

h<- IM_psehmm(system.file("extdata", "1DLHA2-7", package="protHMM"))

chmm

Description

This feature begins by creating a CHMM, which is created by constructing 4 matrices, A, B, C, D from the original HMM H. A contains the first 75 percent of the original matrix H row-wise, B the last 75 percent, C the middle 75 percent and D the entire original matrix. These are then merged to create the new CHMM Z. From there, the Bigrams feature is calculated with a flattened 20 x 20 matrix B, in which B[i, j] = \sum_{a = 1}^{L-1} Z_{a, i} \times Z_{a+1, j}. H corresponds to the original HMM matrix, and L is the number of rows in Z. Local Average Group, or LAG is then calculated by splitting up the CHMM into 20 groups along the length of the protein sequence and calculating the sums of each of the columns of each group, making a 1 x 20 vector per group, and a length 20 x 20 vector for all groups. These features are then fused.

Usage

chmm(hmm)

Arguments

Value

A fusion vector of length 800.

A LAG vector of length 400.

A Bigrams vector of length 400.

References

An, J., Zhou, Y., Zhao, Y., & Yan, Z. (2019). An Efficient Feature Extraction Technique Based on Local Coding PSSM and Multifeatures Fusion for Predicting Protein-Protein Interactions. Evolutionary Bioinformatics, 15, 117693431987992.

Examples

h<- chmm(system.file("extdata", "1DLHA2-7", package="protHMM"))

fp_hmm

Description

This feature consists of two vectors, d, s. Vector d corresponds to the sums across the sequence for each of the 20 amino acid columns. Vector s corresponds to a flattened matrix S[i, j] = \sum_{k = 1}^{L} H[k, j] \times \delta[k, i] in which \delta[k, i] = 1 when A_i = H[k, j]. A refers to a list of all possible amino acids, i, j span from 1:20.

Usage

fp_hmm(hmm)

Arguments

Value

A vector of length 20.

A vector of length 400.

References

Zahiri, J., Yaghoubi, O., Mohammad-Noori, M., Ebrahimpour, R., & Masoudi-Nejad, A. (2013). PPIevo: Protein–protein interaction prediction from PSSM based evolutionary information. Genomics, 102(4), 237–242.

Examples

h<- fp_hmm(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_GA

Description

This feature calculates the Geary autocorrelation of each amino acid type for each distance d less than or equal to the lag value and greater than or equal to 1.

Usage

hmm_GA(hmm, lg = 9)

Arguments

Value

A vector of length lg \times 20, by default this is 180.

Note

The lag value must be less than the length of the protein sequence

References

Liang, Y., Liu, S., & Zhang. (2015). Prediction of Protein Structural Class Based on Different Autocorrelation Descriptors of Position–Specific Scoring Matrix. MATCH: Communications in Mathematical and in Computer Chemistry, 73(3), 765–784.

Examples

h<- hmm_GA(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_GSD

Description

This feature initially creates a grouping matrix G by assigning each position a number 1:3 based on the value at each position of HMM matrix H; 1 represents the low probability group, 2 the medium and 3 the high probability group. The number of total points in each group for each column is then calculated, and the sequence is then split based upon the the positions of the 1st, 25th, 50th, 75th and 100th percentile (last) points for each of the three groups, in each of the 20 columns of the grouping matrix. Thus for column j, S(k, j, z) = \sum_{i = 1}^{(z)*.25*N} |G[i, j] = k|, where k is the group number, z = 1:4 and N corresponds to number of rows in matrix G.

Usage

hmm_GSD(hmm)

Arguments

Value

A vector of length 300.

References

Jin, D., & Zhu, P. (2021). Protein Subcellular Localization Based on Evolutionary Information and Segmented Distribution. Mathematical Problems in Engineering, 2021, 1–14.

Examples

h<- hmm_GSD(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_LBP

Description

This feature uses local binary pattern with a neighborhood of radius 1 and 8 sample points to extract features from the HMM. A 256 bin histogram is extracted as a 256 length feature vector.

Usage

hmm_LBP(hmm)

Arguments

Value

A vector of length 256.

References

Li, Y., Li, L., Wang, L., Yu, C., Wang, Z., & You, Z. (2019). An Ensemble Classifier to Predict Protein–Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model. International Journal of Molecular Sciences, 20(14), 3511.

Examples

h<- hmm_LBP(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_LPC

Description

This feature uses linear predictive coding (LPC) to map each HMM to a 20 \times 14 = 280 dimensional vector, where for each of the 20 columns of the HMM, LPC is used to extract a 14 dimensional vector D_n

Usage

hmm_LPC(hmm)

Arguments

Value

A vector of length 280.

References

Qin, Y., Zheng, X., Wang, J., Chen, M., & Zhou, C. (2015). Prediction of protein structural class based on Linear Predictive Coding of PSI-BLAST profiles. Central European Journal of Biology, 10(1).

Examples

h<- hmm_LPC(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_MA

Description

This feature calculates the normalized Moran autocorrelation of each amino acid type, for each distance d less than or equal to the lag value and greater than or equal to 1.

Usage

hmm_MA(hmm, lg = 9)

Arguments

Value

A vector of length lg \times 20, by default this is 180.

Note

The lag value must be less than the length of the protein sequence

References

Liang, Y., Liu, S., & Zhang. (2015). Prediction of Protein Structural Class Based on Di fferent Autocorrelation Descriptors of Position–Specific Scoring Matrix. MATCH: Communications in Mathematical and in Computer Chemistry, 73(3), 765–784.

Examples

h<- hmm_MA(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_MB

Description

This feature calculates the normalized Moreau-Broto autocorrelation of each amino acid type, for each distance d less than or equal to the lag value and greater than or equal to 1.

Usage

hmm_MB(hmm, lg = 9)

Arguments

Value

A vector of length lg \times 20, by default this is 180.

Note

The lag value must be less than the length of the protein sequence

References

Liang, Y., Liu, S., & Zhang. (2015). Prediction of Protein Structural Class Based on Different Autocorrelation Descriptors of Position–Specific Scoring Matrix. MATCH: Communications in Mathematical and in Computer Chemistry, 73(3), 765–784.

Examples

h<- hmm_MB(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_SCSH

Description

This feature returns the 2 and 3-mer compositions of the protein sequence. This is done by first finding all possible 2 and 3-mers for any protein (20^2 and 20^3 permutations for 2 and 3-mers respectively). With those permutations, vectors of length 400 and 8000 are created, each point corresponding to one 2 or 3-mer. Then, the protein sequence that corresponds to the HMM scores is extracted, and put into a bipartite graph with the protein sequence. Each possible path of length 1 or 2 is found, and the corresponding vertices on the graph are noted as 2 and 3-mers. For each 2 or 3-mer found from these paths, 1 is added to the position that responds to that 2/3-mer in the 2-mer and 3-mer vectors , which are the length 400 and 8000 vectors created previously. The vectors are then returned.

Usage

hmm_SCSH(hmm)

Arguments

Value

A vector of length 400.

A vector of length 8000.

References

Mohammadi, A. M., Zahiri, J., Mohammadi, S., Khodarahmi, M., & Arab, S. S. (2022). PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles. Biology Methods and Protocols, 7(1).

Examples

h_400<- hmm_SCSH(system.file("extdata", "1DLHA2-7", package="protHMM"))[[1]]
h_8000<- hmm_SCSH(system.file("extdata", "1DLHA2-7", package="protHMM"))[[2]]

hmm_SepDim

Description

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.

Usage

hmm_SepDim(hmm, l = 7)

Arguments

Value

A vector of length 400

References

Saini, H., Raicar, G., Sharma, A., Lal, S. K., Dehzangi, A., Lyons, J., Paliwal, K. K., Imoto, S., & Miyano, S. (2015). Probabilistic expression of spatially varied amino acid dimers into general form of Chou's pseudo amino acid composition for protein fold recognition. Journal of Theoretical Biology, 380, 291–298.

Examples

h<- hmm_SepDim(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_Single_Average

Description

This feature groups together rows that are related to the same amino acid. This is done using a vector SA(k), in which k spans 1:400 and SA(k) = avg_{i = 1, 2... L}H[i, j] \times \delta(P(i), A(z)), in which H is the HMM matrix, P in the protein sequence, A is an ordered set of amino acids, the variables j, z = 1:20, the variable k = j + 20 \times (z-1) when creating the vector, and \delta() represents Kronecker's delta.

Usage

hmm_Single_Average(hmm)

Arguments

Value

A vector of length 400.

References

Nanni, L., Lumini, A., & Brahnam, S. (2014). An Empirical Study of Different Approaches for Protein Classification. The Scientific World Journal, 2014, 1–17.

Examples

h<- hmm_Single_Average(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_ac

Description

This feature calculates the covariance between two residues separated by a lag value within the same amino acid emission frequency column along the protein sequence.

Usage

hmm_ac(hmm, lg = 4)

Arguments

Value

A vector of length 20 \times the lag value; by default this is a vector of length 80.

Note

The lag value must be less than the length of the protein sequence

References

Dong, Q., Zhou, S., & Guan, J. (2009). A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics, 25(20), 2655–2662.

Examples

h<- hmm_ac(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_bigrams

Description

This feature is calculated with a 20 x 20 matrix B, in which B[i, j] = \sum_{a = 1}^{L-1} H_{a, i}H_{a+1, j}. H corresponds to the original HMM matrix, and L is the number of rows in H. Matrix B is then flattened to a feature vector of length 400, and returned.

Usage

hmm_bigrams(hmm)

Arguments

Value

A vector of length 400

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), 761–772.

Examples

h<- hmm_bigrams(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_cc

Description

The feature calculates the covariance between different residues separated along the protein sequences by a lag value across different amino acid emission frequency columns.

Usage

hmm_cc(hmm, lg = 4)

Arguments

Value

A vector of length 20 x 19 x the lag value; by default this is a vector of length 1520.

Note

The lag value must less than the length of the amino acid sequence.

References

Dong, Q., Zhou, S., & Guan, J. (2009). A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics, 25(20), 2655–2662.

Examples

h<- hmm_cc(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_distance

Description

This feature calculates the cosine distance matrix between two HMMs A and B before dynamic time warp is applied to the distance matrix calculate the cumulative distance between the HMMs, which acts as a measure of similarity, The cosine distance matrix D is found to be D[a_i, b_j] = 1 - \frac{a_ib_j^{T}}{a_ia_i^Tb_jb_j^T}, in which a_i and a_i refer to row vectors of A and B respectively. This in turn means that D is of dimensions nrow(A), nrow(b). Dynamic time warp then calculates the cumulative distance by calculating matrix C[i, j] = min(C[i-1, j], C[i, j-1], C[i-1, j-1]) + D[i, j], where C_{i,j} is 0 when i or j are less than 1. The lower rightmost point of the matrix C is then returned as the cumulative distance between proteins.

Usage

hmm_distance(hmm_1, hmm_2)

Arguments

Value

A double that indicates distance between the two proteins.

References

Lyons, J., Paliwal, K. K., Dehzangi, A., Heffernan, R., Tsunoda, T., & Sharma, A. (2016). Protein fold recognition using HMM–HMM alignment and dynamic programming. Journal of Theoretical Biology, 393, 67–74.

Examples

h<- hmm_distance(system.file("extdata", "1DLHA2-7", package="protHMM"),
system.file("extdata", "1TEN-7", package="protHMM"))

hmm_read

Description

Reads in the amino acid emission frequency columns of a profile hidden markov model matrix and converts each position to frequencies.

Usage

hmm_read(hmm)

Arguments

Value

A 20 x L matrix, in which L is the sequence length.

Examples

h<- hmm_read(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_smooth

Description

This feature smooths the HMM matrix H by using sliding window of length sw to incorporate information from up and downstream residues into each row of the HMM matrix. Each HMM row r_i is made into the summation of r_{i-(sw/2)}+... r_i...+r_{i+(sw/2)}, for i = 1:L, where L is the number of rows in H. For rows such as the beginning and ending rows, 0 matrices of dimensions sw/2, 20 are appended to the original matrix H.

Usage

hmm_smooth(hmm, sw = 7)

Arguments

Value

A matrix of dimensions L \times 20.

References

Fang, C., Noguchi, T., & Yamana, H. (2013). SCPSSMpred: A General Sequence-based Method for Ligand-binding Site Prediction. IPSJ Transactions on Bioinformatics, 6(0), 35–42.

Examples

h<- hmm_smooth(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_svd

Description

This feature uses singular value decomposition (SVD) to reduce the dimensionality of the inputted hidden markov model matrix. SVD factorizes a matrix C of dimensions i, j to U[i, r] \times \Sigma[r, r] \times V[r, j]. The diagonal values of \Sigma are known as the singular values of matrix C, and are what are returned with this function.

Usage

hmm_svd(hmm)

Arguments

Value

A vector of length 20.

References

Song, X., Chen, Z., Sun, X., You, Z., Li, L., & Zhao, Y. (2018). An Ensemble Classifier with Random Projection for Predicting Protein–Protein Interactions Using Sequence and Evolutionary Information. Applied Sciences, 8(1), 89.

Examples

h<- hmm_svd(system.file("extdata", "1DLHA2-7", package="protHMM"))

hmm_trigrams

Description

This feature is calculated with a 20 x 20 x 20 block B, in which B[i, j, k] = \sum_{a = 1}^{L-2} H_{a, i}H_{a+1, j}H_{a+2, k}. H corresponds to the original HMM matrix, and L is the number of rows in H. Matrix B is then flattened to a feature vector of length 8000, and returned.

Usage

hmm_trigrams(hmm)

Arguments

Value

A vector of length 8000

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), 761–772.

Examples

h<- hmm_trigrams(system.file("extdata", "1DLHA2-7", package="protHMM"))

pse_hmm

Description

The first twenty numbers of this feature correspond to the means of each column of the HMM matrix H. The rest of the features in the feature vector are given by correlation of the ith most contiguous values along the chain per each amino acid column, where 0<i<g+1. This creates a vector of 20 \times g, and this combines with the first 20 features to create the final feature vector.

Usage

pse_hmm(hmm, g = 15)

Arguments

Value

A vector of length 20 + g \times 20, by default this is 320.

Note

g must be less than the length of the protein sequence

References

Chou, K., & Shen, H. (2007). MemType-2L: A Web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. Biochemical and Biophysical Research Communications, 360(2), 339–345.

Examples

h<- pse_hmm(system.file("extdata", "1DLHA2-7", package="protHMM"))