mrbakhsh/HPiP
Host-Pathogen Interaction Prediction

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In mrbakhsh/HPiP: Host-Pathogen Interaction Prediction 

library("knitr")

knitr::opts_chunk$set(echo = TRUE)

suppressPackageStartupMessages({
  library("readr")
  library("dplyr")
  library("Biostrings")
  library("SummarizedExperiment")
  library("stringr")
})

\pagebreak

Introduction

Infectious disease imposes a significant threat to human health and pose substantial healthcare costs. Infectious diseases result from the cross-talks between hosts and pathogens, which are mainly mediated by protein-protein interactions between hosts and pathogen proteins (HP-PPIs). The potential (HP-PPIs) represents the crucial elements of the infection mechanism as they decide the outcome, leading to either pathogen clearance or spread of the pathogen in the host due to evading the host immune response [@rahmatbakhsh2021bioinformatic]. Therefore, the study of the host-pathogen interactome is increasingly vital to uncover the molecular attributes of infectious diseases and potentially discover novel pharmacological targets or laying a strong foundation for repurposing of existing drugs.

In the past decades, several high throughput experimental approaches have been developed to chart HP-PPIs on a large scale (e.g., yeast two-hybrid (Y2H) system [@ito2001comprehensive] or affinity purification (AP) coupled to mass spectrometry (MS) [@puig2001tandem]). However, such high-throughput experimental screens are typically laborious, time-consuming, and challenging to capture the complete interactome, resulting in limited number of experimentally validated interactome in a database of HP-PPIs [@hart2006complete]. In-silico prediction of HP-PPIs can complement wet-lab experiments by suggesting candidate interaction partners for all the host proteins and excluding partners with low interacting probability scores to reduce the range of possible PPI candidates that need to be validated via wet-lab experiments. Specialized computational approaches to predict HP-PPIs are therefore of significant importance. While many computational tools have been developed to predict intra-species PPIs (i.e., PPIs within the same species) [@wu2006prediction; @shen2007predicting; @nourani2015computational], the availability of computational tools to predict inter-species PPIs such as HP-PPIs are rare.

For this purpose, we describe HPiP (host-pathogen interaction prediction), an R package for automated prediction of HP-PPIs using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins. Briefly, HPiP extracts gold-standard of experimentally verified HP-PPIs (i.e., positive interactions) from public repository, construct negative interactions via negative sampling, retrieve and convert protein sequences to numerical representation via various descriptors, applies multivariate feature selection based on correlation and recursive feature elimination (RFE)-embedded, and finally applies ensemble averaging to predict interactions. Taken together, we hope that the HPiP package not only contributes a useful predictor to accelerate the exploration of host-pathogen PPIs, but also provides some meaningful insights into host-pathogen relationships.

Overview of HPiP

Briefly, HPiP downloads the gold-standard data sets of experimentally verified host-pathogen PPIs from the BioGRID database [@stark2006biogrid]. Such interactions serve as a positive set. In the absence of ground truth negative examples, HPiP uses negative sampling to construct a negative set. Following the construction of gold-standard data, HPiP retrieves the FASTA sequences of associated proteins. HPiP then represents protein sequences into a fixed-length feature vector using a series of structural and physicochemical descriptors. Host-pathogen feature vectors and the accompanying gold standard reference set also called the training set, are fed into the hybrid filter-wrapper feature selection method to select the most relevant features in inferring the target variable. In the following step, HPiP uses a training set to train a series of individual machine learning models (base learners) provided in the caret package [@kuhn2020package]. For each applied model, hyperparameters are tweaked throughout training via resampling techniques (e.g., k-fold cross-validation), and the best set of hyperparameters are selected based on the accuracy performance measure. The optimized models will then be applied to host-pathogen feature vectors with an unknown class label to return a classification result for each pair. The HPiP then uses ensemble averaging to average classification results over an ensemble of classifiers for each possible interaction. Finally, HPiP compares the algorithmic performance of the ensemble model with individual base learners through resampling technique (e.g., k-fold cross-validation) and various performance metrics (e.g., accuracy).

An Example of Predicting HP-PPIs

In the following sections, we explain the main components of the HPiP package, including dataset preparation (i.e., construction of the gold-standard set, FASTA sequence retrieval), feature extraction, data processing steps (i.e., imputation of missing values, feature selection), ensemble model generation and evaluation, prediction of HP-PPIs, network visualization and external validation of the predicted network using functional enrichment analysis. Furthermore, we guide users through each step by applying the HPiP to sample data derived from public databases.

Data Set Preparation

Gold Standard Reference Dataset of Host-Pathogen PPIs

In this tutorial, we use the data provided by @samavarchi2020sars as our benchmark dataset. In this study, the authors mapped interaction between 27 SARS-CoV-2 and host proteins via the proximity-dependent biotinylation (BioID) approach. We then randomly selected 500 SARS-CoV-2-host interaction pairs from all pairs as the positive samples. Since ground truth negatives are not available, here negative examples are generated from the positive set using negative sampling [@eid2016denovo]. In this approach, negative instances are sampled from all the possible pairwise combinations of host and viral proteins, as long as the possible pairs do not occur in the positive reference set. To prevent statistical differences, the same scale is assumed for the negative and positive instances (i.e., the ratio of positive to negative 1:1) [@zhou2018generalized]. The gold-reference data set can be loaded with the following command: 

# Loading packages required for data handling & data manipulation
library(dplyr)
library(tibble)
library(stringr)
# Loading HPiP package
library(HPiP)
# Loading data 
data(Gold_ReferenceSet)
dim(Gold_ReferenceSet)

As stated by dim() the gold reference set includes 1000 HP-PPIs interaction between 27 SARS-CoV-2 and 784 host proteins.

In addition, users can use get_positivePPI in the HPiP package to construct positive set from the BioGRID database [@stark2006biogrid].

This function takes the following parameters:

local = tempdir()
#Get positive interactions from BioGrid 
TP <- get_positivePPI(organism.taxID = 2697049,
                      access.key = 'XXXX',
                            filename = "PositiveInt.RData",
                            path = local)

TP <- read_csv(
          system.file("extdata/TP_set.csv", package = "HPiP"),
          show_col_types = FALSE
        )

Subsequently, we can construct negative set via negative sampling using the following command:

#pathogen proteins
prot1 <- unique(TP$`Official Symbol Interactor A`)
#host proteins
prot2 <- unique(TP$`Official Symbol Interactor B`)
#true positive PPIs 
TPset <- TP$PPI
TN <- get_negativePPI(prot1 , prot2, TPset)
dim(TN)

FASTA Sequence extraction

To compute different features from protein sequences, we must first extract their sequences (in FASTA format). The getFASTA function in the HPiP package can retrieve the sequences for any organism from the UniProt database. 

local = tempdir()
#retrieve FASTA sequences of SARS-CoV-2 virus 
id = unique(Gold_ReferenceSet$Pathogen_Protein)
fasta_list <- getFASTA(id, filename = 'FASTA.RData', path = local)

Sequence-based Features Extraction

To apply a learning algorithm on a host or pathogen protein sequence, it is needed to encode sequence information as numerical features. However, one of the critical challenges in inferring protein-protein interactions from the protein sequences is finding an appropriate way to encode protein sequences' important information fully. Also, the amino-acid sequences of different lengths should be converted to fixed-length feature vectors, which is crucial in classifying training data using machine-learning techniques as such techniques require fixed-length patterns. The HPiP offers multiple functions for generating various numerical features from protein sequences.

These feature coding schemes listed in HPiP include amino acid composition (AAC) , dipeptide composition (DC), tripeptide composition (TC), tripeptide composition (TC) from biochemical similarity classes, quadruplets composition (QC), F1, F2, CTD (composition/transition/distribution), conjoint triad, autocorrelation, k-spaced amino acid pairs, and binary encoding.

Amino acid Composition (AAC) Descriptor

The amino acid composition (AAC) has low complexity and has been widely used to predict protein-protein interactions (PPIs) [@beltran2019predicting; @dey2020machine].The AAC explains the fraction of a type of amino acid found within a protein sequence [@dey2020machine]. This gives 20-dimensional feature vectors. For example, the fraction of all 20 natural amino acids is computed as follow:

[ f_{(i)}=\frac{n_i}{L} \text{ }\ (i = 1,2,3,....,20) ]

where n~i~ is the number of amino acid type and L is the sequence length. The ACC descriptor from the protein sequences can be loaded with the following command:

# Convert the list of sequences obtained in the previous section to data.frame 
fasta_df <- do.call(rbind, fasta_list) 
fasta_df <- data.frame(UniprotID = row.names(fasta_df), 
                       sequence = as.character(fasta_df))

#calculate AAC
acc_df <- calculateAAC(fasta_df)
#only print out the result for the first row 
acc_df[1,-1] 

ex <- acc_df[1,-1] 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Dipeptide Composition (DC) Descriptor

The dipeptide composition (DC) is simply the fraction of the different adjacent pairs of amino acids within a protein sequence [@bhasin2004classification]. Also, this descriptor encapsulates the properties of neighboring amino acids. Dipeptide composition converts a protein sequence to a vector of 400 dimensions. The composition of all 400 natural amino acids can be calculated using the following equation:

[ f_{(m,k)}=\frac{n_{m,k}}{L-1} \text{ }\ (m,k = 1,2,3,....,20) ]

where n~m,k~ corresponds to the number of dipeptide compositions characterized by amino acid type m and type k, while L is the sequence length.The DC descriptor from the protein sequences can be loaded with the following command:

# using data.frame provided by getFASTA function as data input
dc_df <- calculateDC(fasta_df)
#only print out the first 30 elements for the first row 
dc_df[1, c(2:31)] 

ex <- dc_df[1, c(2:31)] 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Tripeptide Composition (TC) Descriptor

The tripeptide composition explains the occurrence of adjacent triune residues in a protein sequence [@liao2011predicting]. Tripeptide composition converts a protein sequence to a vector of 8,000 dimensions. The composition of all 8,000-dimensional descriptor can be calculated using the following equation: [ f_{(m,k,j)}=\frac{n_{m,k,j}}{L-2} \text{ }\ (m,k,j = 1,2,3,....,20) ] where n~m,k,j~ corresponds to the number of tripeptide compositions characterized by amino acid type m, k and j, while L is the sequence length.The TC descriptor from the protein sequences can be loaded with the following command:

# using data.frame provided by getFASTA function as data input
tc_df <- calculateTC(fasta_df)
#only print out the first 30 elements for the first row 
tc_df[1, c(2:31)] 

ex <-tc_df[1, c(2:31)] 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Tripeptide Composition (TC) from Biochemical Similarity Classes Descriptor

In order to reduce the dimension of length-8,000 TC descriptor, the sequence alphabet is reduced from 20 amino acids to six classes based on biochemical similarity. The classes are [{IVLM}, {FYW}, {HKR}, {DE}, {QNTP}, and {ACGS} [@ahmed2018prediction]]. This classification of amino acids converts a protein sequence to a vector of 216 (i.e., 6 * 6 * 6) different combinations of possible substrings of length 3. The frequency of triplet for each encoded class in the protein sequence can be calculated as follows:

[ q_{(i)}=\frac{f_i - M_0}{M_1-M_0} ] [ M_0 = min(f_1,f_2,...,f_{216})\text { and}\ M_1 = max(f_1,f_2,...,f_{216}) ]

Here f~i~ is the frequency of i^th^ triplet in the sequence i=1,2,...,216. To get 216-dimensional descriptor from the protein sequences, the following command can be used:

# using data.frame provided by getFASTA function as data input
TC_Sm_df <- calculateTC_Sm(fasta_df)
#only print out the first 30 elements for the first row 
TC_Sm_df[1, c(2:31)] 

ex <- TC_Sm_df[1, c(2:31)] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Quadruplets Composition from Biochemical Similarity Classes Descriptor

To compute these features, the sequence alphabet is first reduced to six classes reported above (section 3.3.2.4). This reduction converts a protein sequence to a vector of 1296 (i.e., 6 * 6 * 6 * 6) different combinations of possible substrings of length 4 [@ahmed2018prediction]. The frequency of quadruplets for each encoded class in the protein sequence can be calculated similarly to the equation mentioned above:

[ q_{(i)}=\frac{f_i - M_0}{M_1-M_0} ] [ M_0 = min(f_1,f_2,...,f_{1296})\text { and}\ M_1 = max(f_1,f_2,...,f_{1296}) ] To get 1296-dimensional descriptor from the protein sequences, the following command can be used:

# using data.frame provided by getFASTA function as data input
QD_df <- calculateQD_Sm(fasta_df)
#only print out the first 30 elements for the first row 
QD_df[1, c(2:31)] 

ex <- QD_df[1, c(2:31)]
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

F1/F2 Composition Descriptor

F1 composition gives 20-dimensional description, defined as:

[ F1(SAR)=\sum_{SAR\text{ }\epsilon\text{ } sequence}length(SAR)^2 ]

Where SAR is the sum of squared length of single amino acid repeats (SARs) in the entire protein sequence. Since F1 includes SAR of length 1, the F1 descriptor reveals global composition of amino acids and amino acid repeats [@alguwaizani2018predicting].

Figure 1: Example of calculating F1 (repeats of S) in the protein sequence.

While, to calculate feature F2, the sequence alphabet is first split into substrings of length 6 residues [@alguwaizani2018predicting]. There are two main differences between feature F2 and F1:

F2 composition gives 20-dimensional description, defined as:

[ F1(SAR)=max_{windows\text{ }\epsilon\text{ }sequence} \ sum_{SAR\text{ }\epsilon\text{ } sequence}length(SAR)^2 ]

Where SAR is the sum of squared length of single amino acid repeats (SARs) in the entire protein sequence.

# using data.frame provided by getFASTA function as data input
F1_df <- calculateF(fasta_df, type = "F1")
#only print out the result the first row 
F1_df[1,-1] 

ex <- F1_df[1,-1] 
#convert df to character vector 
x_df <- structure(as.numeric(ex), names = colnames(ex)) 
x_df

# using data.frame provided by getFASTA function as data input
F2_df <- calculateF(fasta_df, type = "F2")
#only print out the result the first row 
F2_df[1,-1] 

ex <- F2_df[1,-1] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Composition/Transition/Distribution (CTD) Descriptors

To calculate CTD descriptors developed by [@dubchak1995prediction; @dubchak1999recognition], the 20 standard amino acids is first clustered into three classes according to its attribute. Then, each amino acid in the protein sequence is encoded by one of the indices 1,2,3 depending on its grouping. The corresponding divisions for each group are shown in Table 1. According to Hydrophobicity grouping mentioned in Table 1, the protein sequence CLVIMFWGASTPHYRKEDQN is replaced by 11111112222222333333. Next, for a given attribute, three types of descriptors, composition (C), transition (T), and distribution (D) can be calculated, which will be explained in the following sections.

df1 <- HPiP:::df1
df1[is.na(df1)] <- ""
knitr::kable(df1, align = "lccrr", caption = "Amino acid attributes and the division of amino acid into three-group.", longtable = TRUE)

Composition (C) Descriptor

The composition represents the number of amino acids of a particular property (e.g., hydrophobicity) for each encoded class divided by the protein sequence length [@you2014prediction]. In the above example using the hydrophobicity attribute, the number for encoded classes 1, 2, 3 are 7,7,6 respectively. Therefore, the compositions for each class are 7/20 =35%, 7/20 =35%, and 6/20 =30%, respectively. Composition descriptor can be defined as:

[ C_{(i)}=\frac{n_i}{L} \text{ }\ (i = 1,2,3) ]

where n~i~ is the number of amino acid type i and L is the sequence length. The C descriptor from the protein sequences can be loaded with the following command:

# using data.frame provided by getFASTA function as data input
CTDC_df <- calculateCTDC(fasta_df)
CTDC_df[1, c(-1)] 

ex <- CTDC_df[1, c(-1)] 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Transition (T) Descriptor

Transition (T) characterizes the percent frequency from a type of amino acids to another type (Wang et al., 2017). For instance, a transition from class 1 to 2 or 2 to 1 is the percent frequency with which class 1 is followed by class 2 or vice versa [@xiao2015protr]. The frequency of these transitions can be computed as follow:

[ T_{(rs)}=\frac{n_{rs} + n_{sr}}{L-1} \text{ }\ (rs = 12,13,23) ]

where n~rs~,n~sr~ are the number of dipeptide encoded as rs and sr in the sequence and and L is the sequence length.The T descriptor from the
protein sequences can be calculated with the following command:

# using data.frame provided by getFASTA function as data input
CTDT_df <- calculateCTDT(fasta_df)
#only print out the result for the first row 
CTDT_df[1, -1] 

ex <- CTDT_df[1, -1] 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Distribution (D) Descriptor

The distribution measures the chain length within which the first, 25%, 50%, 75%, and 100% of the amino acids of a particular property (e.g., hydrophobicity) for a certain encoded class are located, respectively [@dubchak1995prediction]. For example, as shown in Figure 3, there are 8 residues as 1, the position for the first residue 1 , the 2nd residue 1 (25% * 8 = 2), the 5th 1 residue (50% * 8 = 4), the 7th 1 (75% * 8= 6) and the 10th residue 2 (100% * 8 =8) in the encoded sequence are 1, 2, 13, 17, 22 respectively, so that the distribution descriptors for residue 1 are : (1/22) ×100% = 4.55%, (2/22)×100% = 9.09%, (13/22) ×100% = 59.09%, (17/22)×100% = 77.27%, (22/22)×100% = 100%, respectively. Likewise, the distribution descriptor for 2 and 3 is (18.18%, 18.18%, 27.27%, 63.64%, 95.45%) and (13.64%, 31.82%, 45.45%, 54.55%, 86.36%), respectively.

CTD descriptors

Figure 2:The sequence of hypothetical protein showing the construction of CTD descriptors of a protein. The index 1, 2 and 3 indicates the position of amino acid for each encoded class. 1-2 transitions indicated the position of 12 or 21 pairs in the sequence. Similarly, 1-3 and 2-3 transitions are defined in the same way.

The D descriptor from the protein sequences can be calculated with the following command:

# using data.frame provided by getFASTA function as data input
CTDD_df <- calculateCTDD(fasta_df)
#only print out the first 30 elements for the first row 
CTDD_df[1, c(2:31)] 

ex <- CTDD_df[1, c(2:31)] 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Conjoint Triad Descriptor

The conjoint triad is one of the popular sequence-based approaches for protein-protein interactions prediction [@shen2007predicting]. This method encodes a protein sequence as a feature vector by calculating the frequency of amino acid triplets as follows (Figure 2) :

[ d_i = \frac{f_i - \min{\,f_1, f_2 , \ldots, f_{343}\,}}{\max{\,f_1, f_2, \ ldots, f_{343}\,}} ]

Where f~i~ is the frequency of i-th triplet type in the protein sequence. The numerical value of d~i~ of each protein ranges between 0 to 1, which therefore allows the comparison between proteins.

CTD descriptors

Figure 3: Schematic diagram for constructing conjoint triad method. V is the vector space of feature vectors that includes a fixed number of features; each feature (v~i~) includes amino acid triplet; F represents the frequency vector corresponding to V, and the value of i-th dimension of F(f~i~) corresponds to the frequency of that v~i~-triad observed in the sequence.

The conjoint triad Descriptor descriptor from the protein sequences can be calculated with the following command:

# using data.frame provided by getFASTA function as data input
CTriad_df <- calculateCTriad(fasta_df)
#only print out the first 30 elements for the first row 
CTriad_df[1, c(2:31)] 

ex <- CTriad_df[1, c(2:31)] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Autocorrelation (Auto) Descriptors

Autocorrelation descriptors, also known as molecular connectivity indices, explain the magnitude of the correlation between protein or peptide sequences based on their particular structural or physiochemical information, which are defined according to the distribution of amino acid properties along the protein sequence [@ong2007efficacy]. Eight default properties [@xiao2015protr] are used here for deriving the autocorrelation descriptors: normalized average hydrophobicity scales (AccNo. CIDH920105), average flexibility indices (AccNo. BHAR88010), polarizability parameter (AccNo. CHAM820101), free energy of solution in water(AccNo. CHAM820102), residue accessible surface area in tripeptide (AccNo. CHOC760101), residue volume (AccNo. BIGC670101), steric parameter (AccNo. CHAM810101), and relative mutability (AccNo. DAYM780201). Autocorrelation descriptors includes three types of descriptors (Morean-Broto, Moran, and Geary) which are described below. Prior to integrating any of the physiochemical attributes into the autocorrelation formula, these attributes need to be normalized by the following equation:

[ P_r = \frac{P_r - \bar{P}}{\sigma} ] where $\bar{P}$ is the mean value of the eight physiochemical attributes, and sigma represents the standard deviation, in which both can be defined as:

[ \bar{P} = \frac{\sum_{r=1}^{20} P_r}{20} \quad \textrm{and} \quad \sigma = \sqrt{\frac{1}{2} \sum_{r=1}^{20} (P_r - \bar{P})^2} ]

The first type of autocorrelation is known as Moreau-Broto autocorrelation [@broto1984molecular]. Application of Moreau-Broto autocorrelation to protein sequence is calculated by the following equation:

[ AC(d) = \sum_{i=1}^{L-d} P_i P_{i + d} \quad d = 1, 2, \ldots, \textrm{nlag} ]

where $P_i$ and $P_{i+d}$ represent the amino acid property at position $i$ and $i+d$ and $d$ is termed the lag of the autocorrelation along the protein sequence; $P_i$ and $P_{i+d}$. While, $\textrm{nlag}$ is the maximum value of the lag. This equation can be normalized based on peptide length to get normalized Moreau-Broto autocorrelation:

[ ATS(d) = \frac{AC(d)}{L-d} \quad d = 1, 2, \ldots, \textrm{nlag} ]

The second type of autocorrelation, named the Moran autocorrelation (Moran, 1950), can be defined as:

[ I(d) = \frac{\frac{1}{L-d} \sum_{i=1}^{L-d} (P_i - \bar{P}') (P_{i+d} - \bar{P}')}{\frac{1}{L} \sum_{i=1}^{L} (P_i - \bar{P}')^2} \quad d = 1, 2, \ldots, 30 ]

where $d$, $P_i$, and $P_{i+d}$ are described in the same fashion as that for Moreau-Broto autocorrelation; $\bar{P}'$ is the mean of the given amino acid property $P$ across the protein sequence, i.e.,

[ \bar{P}' = \frac{\sum_{i=1}^L P_i}{L} ]

$d$, $P$, $P_i$ and $P_{i+d}$, $\textrm{nlag}$ are defined as above. The main difference between Moran and Moreau-Broto autocorrelation is that, unlike Moreau-Broto, the Moran autocorrelation utilizes the mean value of the given amino acid property instead of the actual value of the property [@al2019rf].

The last type of autocorrelation , known as the Geary autocorrelation, can be calculated as: [ C(d) = \frac{\frac{1}{2(L-d)} \sum_{i=1}^{L-d} (P_i - P_{i+d})^2}{\frac{1}{L-1} \sum_{i=1}^{L} (P_i - \bar{P}')^2} \quad d = 1, 2, \ldots, 30 ]

where $d$, $P$, $P_i$, $P_{i+d}$, and $\textrm{nlag}$ are defined above. The key difference between Geary and the other two autocorrelations is that the Geary autocorrelation utilizes the square difference of the property values [@al2019rf].

Computing autocorrelation with HPiP is simple as the following commands: 

# using data.frame provided by getFASTA function as data input
moran_df <- calculateAutocor(fasta_df,type = "moran", nlag = 10)
#only print out the first 30 elements for the first row 
moran_df[1, c(2:31)] 

ex <- moran_df[1, c(2:31)] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

# using data.frame provided by getFASTA function as data input
mb_df <- calculateAutocor(fasta_df,type = "moreaubroto", nlag = 10)
#only print out the first 30 elements for the first row 
mb_df[1, c(2:31)] 

ex <- mb_df[1, c(2:31)] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

# using data.frame provided by getFASTA function as data input
geary_df <- calculateAutocor(fasta_df,type = "geary", nlag = 10)
#only print out the first 30 elements for the first row 
geary_df[1, c(2:31)] 

ex <- geary_df[1, c(2:31)] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

k-Spaced Amino Acid Pairs

The k-spaced amino acid pairs (KSAAP) feature describes the number of occurrences of all possible amino acid pairs by a distance of k, which can be any number of residues up to two less than the protein length [@al2019rf]. For instance, given 400 (20 x 20) amino acid pairs and four values for k (k = 1-4), there would be 1600 attributes resulted from the KSAAP feature, and the frequency of each amino acid pair with k spaces is calculated by sliding through protein sequence one by once. Table 2 illustrates the outputs of using KSAAP features with various values of k for protein sequence ARAQRTAAADARAKAAKAGCAARRAAATANYN. 

df2 <- HPiP:::df2
df2[is.na(df2)] <- ""
knitr::kable(df2, caption = "Composition of k-spaced amino acid pairs. Given 400 (20 × 20) amino acid pairs and four values for k (k=1–4), there are 1600 attributes generated for the KSAAP feature.", 
col.names = c("","","","","","","",""),row.names = NA,
             longtable = TRUE, align = "lccrr")

The KSAAP descriptor from the protein sequences can be calculated with the following command:

# using data.frame provided by getFASTA function as data input
KSAAP_df <- calculateKSAAP(fasta_df)
#only print out the first 30 elements for the first row 
KSAAP_df[1, c(2:31)] 

ex <- KSAAP_df[1, c(2:31)] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

Binary encoding

Binary encoding (BE) can be used to transform each residue in a protein sequence into 20 coding values [@al2019rf]. For example, ALa is described as (10000000000000000000) while Cys is defined as (01000000000000000000), etc. Thus, the total length of this feature is 400(20 * 20) vectors.

```r

using data.frame provided by getFASTA function as data input

BE_df <- calculateBE(fasta_df)

only print out the first 30 elements for the first row

BE_df[1, c(2:31)] 

```r
ex <- BE_df[1, c(2:31)] 
#convert df to character vector 
ex <- structure(as.numeric(ex), names = colnames(ex)) 
ex

BString Object as Data Input

Alternatively, we can directly read the FASTA sequences into R using Biostrings
package [@pages2019biostrings], followed by converting the protein sequences into numerical features.

#loading the package 
library(Biostrings)

#Read fasta sequences provided by HPiP package using Biostrings
fasta <- 
  readAAStringSet(system.file("extdata/UP000464024.fasta", package="HPiP"),
                  use.names=TRUE)
#Convert to df
fasta_bios = data.frame(ID=names(fasta),sequences=as.character(fasta))
#Extract the UniProt identifier
fasta_bios$ID <- sub(".*[|]([^.]+)[|].*", "\\1", fasta_bios$ID)
# for example, run ACC
acc_bios <- calculateAAC(fasta_bios)

Generate a SummarizedExperiment Objects

SummerizedExperiment objects can be used to store and merge rectangular matrices of different outputs, as long as they have similar rownames or colnames. As illustrated in section 3.3.2, all the computed data.frames have the same rownames but different features; therefore, we can easily use the cbind functions to merge multiple SummerizedExperiment objects to one object. The HPiP package provides two example SummarizedExperiment objects: viral_se and host_se. viral_se includes pre-computed (CTD) numerical features per viral proteins present in the Gold_ReferenceSet. Similarly,host_se includes (CTD) pre-computed numerical features per host proteins in the Gold_ReferenceSet. 

#loading viral_se object
data(viral_se)
viral_se

#loading host_se object
data(host_se)
host_se

The numerical features from each SummarizedExperiment object can be easily retrieved using the assays()$counts, where each row represent the viral or host proteins and each column represents one of the numerical features.

As an example, construction of SummarizedExperiment for viral proteins using CTD descriptors is as follows:

#generate descriptors
CTDC_df <- calculateCTDC(fasta_df)
CTDC_m <- as.matrix(CTDC_df[, -1])
row.names(CTDC_m) <- CTDC_df$identifier

CTDT_df <- calculateCTDT(fasta_df)
CTDT_m <- as.matrix(CTDT_df[, -1])
row.names(CTDT_m) <- CTDT_df$identifier

CTDD_df <- calculateCTDD(fasta_df)
CTDD_m <- as.matrix(CTDD_df[, -1])
row.names(CTDD_m) <- CTDD_df$identifier

#convert matrix to se object
ctdc_se <- SummarizedExperiment(assays = list(counts = CTDC_m),
                                colData = paste0(colnames(CTDC_df[,-1]),
                                                 "CTDC"))
ctdt_se <- SummarizedExperiment(assays = list(counts = CTDT_m),
                                colData = paste0(colnames(CTDT_df[,-1]),
                                                 "CTDT"))
ctdd_se <- SummarizedExperiment(assays = list(counts = CTDD_m),
                                colData = paste0(colnames(CTDD_df[,-1]),
                                                 "CTDD"))
#combine all se objects to one 
viral_se <- cbind(ctdc_se,ctdd_se,ctdt_se)

Table of Summary Descriptors

df3 <- HPiP:::df3
df3[is.na(df3)] <- ""
knitr::kable(df3, caption = "List of commonly used descriptors in HPiP.",
             align = "lccrr")

Combine Host-Pathogen Interaction Descriptors

To generate host-pathogen protein-protein interaction descriptors, sequence-based descriptors can be combined into one vector space using getHPI(), which provides two types of interactions, controlled by argument type. To illustrate the usage of getHPI, we will continue our example from section 3.2.16

1.Extraction of numerical features from viral_se and host_se objects

#extract features from viral_se
counts_v <- assays(viral_se)$counts
#extract row.names from viral_Se
rnames_v <- row.names(counts_v)

#extract features from host_se
counts_h <- assays(host_se)$counts
#extract row.names from viral_Se
rnames_h <- row.names(counts_h)

2.Map the features to the gold-standard data:

#Loading gold-standard data
gd <- Gold_ReferenceSet

x1_viral <- matrix(NA, nrow = nrow(gd), ncol = ncol(counts_v))
for (i in 1:nrow(gd)) 
  x1_viral[i, ] <- counts_v[which(gd$Pathogen_Protein[i] == rnames_v), ]

x1_host <- matrix(NA, nrow = nrow(gd), ncol = ncol(counts_h))
for (i in 1:nrow(gd)) 
  x1_host[i, ] <- counts_h[which(gd$Host_Protein[i] == rnames_h), ]

3.Generate host-pathogen interaction descriptors using getHPI:

x <- getHPI(x1_viral,x1_host, type = "combine")
x <- as.data.frame(x)
x <- cbind(gd$PPI, gd$class, x)
colnames(x)[1:2] <- c("PPI", "class")

Data Processing

It is crucial to pre-process the data (i.e., remove the noise) before feeding it into the machine learning model as the quality of data and valuable information that can be extracted from it directly affect the model's performance. The pre-processing steps are as follow:

The complete set of arguments for FSmethod function are:

Continuing our example from section 3.3, feature selection using both correlation analysis and RFE approach can be performed using the following command: 

#to use correlation analysis, make sure to drop the columns with sd zero
xx <- Filter(function(x) sd(x) != 0, x[,-c(1,2)])
xx <- cbind(x$PPI, x$class, xx)
colnames(xx)[1:2] <- c("PPI", "class")

#perform feature selection using both correlation analysis and RFE approach
set.seed(101)
x_FS <- FSmethod(xx, type = c("both"),
                 cor.cutoff = 0.8,resampling.method = "cv",
                 iter = 2,repeats =NULL, metric = "Accuracy", 
                 verbose = FALSE)

We can also visualize the results from the FSmethod analysis. For instance, the correlation matrix of unfiltered data can be visualized using the corr_plot. This will present us with a heatmap showing the correlation between all the features prior to filtration. 

#cor plot
corr_plot(x_FS$cor.result$corProfile, method = 'square' , cex = 0.1)

In addition, the variable importance of retained features after the RFE feature selection approach can also be plotted using the var_imp function. 

#var importance
var_imp(x_FS$rf.result$rfProfile, cex.x = 8, cex.y = 8)

Prediction Algorithm

Sequence features and a list of gold-standard HP-PPIs can be fed into an ensemble classifier to rank the potential HP-PPIs interaction. This is accomplished via the pred_ensmebel function. This function uses the ensemble averaging approach, to combine any base classifiers provided in the caret package to predict HP-PPIs. To score interactions, the pred_ensmebel function uses the the training data (i.e., labelled HP-PPIs with sequence features) as well as unlabeled HP-PPIs data set to learn features that allow reliable prediction of HP-PPIs, utilizing multiple ML algorithms. Following training, the trained models will be used to make predictions on unlabeled data. Then,ensemble averaging will be applied to average predictions over an ensemble of classifiers, each with different cross-validation splits. Finally, through resamplign techniques (e.g., k-fold cross-validation), this function also compares and evaluates the performance of ensemble model with individual machine learning models via commonly used measurements such as Recall (Sensitivity), Specificity, Accuracy , Precision, F1-score, and Matthews correlation coefficient (MCC). The corresponding formulae are as follows:

[ Recall=Sensitivity=TPR=\frac{TP}{TP+FN} ]

[ Specificity=1-FPR=\frac{TN}{TN+FP} ]

[ Accuracy=\frac{TP+TN}{TP+TN+FP+FN} ]

[ Precision=\frac{TP}{TP+FP} ]

[ F1=2 \text{ × } \frac{Precision \text{ × } Recall}{Precision + Recall} ]

[ MCC=\frac{TP \text{ × } TN - FP \text{ × } FN}{\sqrt{(TP+FP)\text{ × } (TP+FN)\text{ × } (TN+FP)\text{ × } (TN+FN)}} ]

The pred_ensemble takes the following parameters:

As an example, we will use three base learners , support vector machine (svmRadial), Fitting Generalized Linear Models (glm), and random forest (ranger), controlled by argument classifier, to rank potential interaction. For the sake of time, we use only five-fold cross-validation (ncross = 5). In order to perform prediction, we will use unlabel_data, retrieved from Supplementary Table 1 presented in [@gordon2020sars] , which includes unlabeled HP-PPIs along with pre-computed CTD features, as well as constructed data containing labeled HP-PPIs from section 3.4.

#load the unlabeled HP-PPIs
data('unlabel_data')
#Constructed labeled HP-PPIs
labeled_dat <- x_FS$rf.result$rfdf
labeled_dat <- labeled_dat[,-1] 
#select important features
unlabel_data <- 
  unlabel_data[names(unlabel_data) %in% names(x_FS$rf.result$rfdf)]

#merge them 
ind_data <- rbind(unlabel_data,labeled_dat)

# Get class labels
gd <-  x_FS$rf.result$rfdf
gd <-  gd[, c(2,1)]

Now we can predict interactions using pred_ensembel:

set.seed(102)
ppi <- pred_ensembel(ind_data,
                     gd,
                     classifier = c("svmRadial", "glm", "ranger"),
                     resampling.method = "cv",
                     ncross = 5,
                     verboseIter = TRUE,
                     plots = TRUE,
                     filename=file.path(tempdir(), "plots.pdf"))

To retrieve predicted interactions from the result generated by pred_ensembel function, we can just type:

pred_interactions <- ppi[["predicted_interactions"]]
head(pred_interactions)

Finally, users can subset their list of high-confidence interactions for further analysis, using a stringent classifier confidence ensemble score cutoff of 0.7:

pred_interactions <- filter(pred_interactions, ensemble >= 0.7)
dim(pred_interactions)

When the plots argument set to TRUE, the pred_ensembel function generates one pdf file indicating the performance of the ensemble classier as compared to individual base learners

Nework Visualization

Following PPI prediction, users can visualize the predicted PPI network using plotPPI and FreqInteractors functions.

S_interc <- filter(pred_interactions, 
                           str_detect(Pathogen_protein, "^ORF8:"))
#drop the first column
ppi <- S_interc[,-1]

plotPPI(ppi, edge.name = "ensemble",
            node.color ="red",
            edge.color = "grey",
            cex.node = 10,
            node.label.dist= 2)

ppi <- pred_interactions[,-1]
FreqInteractors(ppi,cex.size = 12)

GO Enrichment Analysis

To identify significantly enriched annotation terms in predicted interacting host protein partners of each pathogen protein, we can use the enrichfindP function based on the g:Profiler tool [@kolberg2020gprofiler2]. Additionally, we can use the erichfind_hp function to analyze functional characteristics of all predicted human proteins in the predicted network.

For instance, the following command can be used to performs functional enrichment analysis of host protein partners of each pathogen protein:

enrich_result <- 
  enrichfindP(ppi,threshold = 0.05,
            sources = "GO",
            p.corrction.method = "bonferroni",
            org = "hsapiens")

Complex Prediction

for this analysis, we utilizes the predicted HP-PPIS network generated in section 3.5 as input data for complex prediction. For the community detection analysis, run_clustering function provided in the HPiP package includes the five most popular complex detection algorithms in including fast-greedy, walktrap, label propagation, multi-level community, and markov clustering. For example, to detect complexes using fast-greedy algorithm, we can run the following command:

ppi <- pred_interactions[,-1]
set.seed(103)
predcpx <- run_clustering(ppi, method = "FC")

Additionally, we can analyze the functional characteristics of these predicted modules via enrichfind_cpx function. 

enrichcpx <- enrichfind_cpx(predcpx,
             threshold = 0.05,
             sources = "GO",
             p.corrction.method = "bonferroni",
             org = "hsapiens")

Session info

sessionInfo()

References



mrbakhsh/HPiP documentation built on March 28, 2023, 4:35 p.m.

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