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\begin{document}

\title{Hist-i-fy: Multiple histidine function prediction based on protein
sequences using deep neural network}



\author[1]{Debashree Bandyopadhyay}%
\author[1]{Abhishek Jalan}%
\author[1]{Dibyansu Diptiman}%
\author[1]{Rishabh Pal}%
\author[1]{Sachin Dodwani}%
\affil[1]{Birla Institute of Technology \& Science Pilani - Hyderabad Campus}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Histidine (His) is the most reactive amino acid at enzyme active sites.
Multiple post-translational modifications (functions) are reported for
His side chains. The high-throughput sequencing techniques produce a
large number of protein sequences without functional annotations at the
amino acid level. Experimental characterization of His functions in
proteins is laborious and time-consuming. Computational characterization
based on protein sequences may complement the need. There are only a
handful of Histidine function prediction tools available and those
annotate only a single function. Here we curated a dataset of active
Histidine with known functions based on protein sequences obtained from
UniProt database (sample size n=1584) and trained against four machine
learning methods. The convolution neural network (CNN) model (``
\emph{Hist-i-fy}'') performed the best with 75\% overall accuracy. The
external validation of Hist-i-fy on phosphorylated histidine data
(sample size 34) showed 94.1\% prediction accuracy. For the first time,
we report multiple His function prediction, based on protein sequences
using deep neural networks. The inputs to the model are i) protein
sequence containing His, and ii) the His residue number. The model
predicts one out of the eight histidine functions, namely, acetylation,
ribosylation, glycosylation, hydroxylation, methylation, oxidation,
phosphorylation, and protein splicing. The novelty of the work is, it
predicts maximum number of histidine functions at a time with optimal
performance. There is a scope of improvement in the model upon
availability of a larger dataset. The model is available as a web
application
({[}https://histify.streamlit.app/{]}(https://histify.streamlit.app/))
and a stand-alone code
{[}https://github.com/dibyansu24-maker/Histify{]}(https://github.com/dibyansu24-maker/Histify)).%
\end{abstract}%



\sloppy


\textbf{Hist-i-fy: Multiple histidine function prediction based on
protein sequences using deep neural network}

\textbf{Abhishek Jalan\textsuperscript{\#}, Dibyansu
Diptiman\textsuperscript{\#}, Rishabh Pal\textsuperscript{\#} , Sachin
Dodwani\textsuperscript{\#}, and Debashree Bandyopadhyay*}

Department of Biological Sciences, Birla Institute of Technology and
Science, Pilani, Hyderabad, 500078, Telangana, India

\textbf{\#} equal contribution from all these authors, names are
arranged alphabetically

\textbf{*Correspondence:} Debashree Bandyopadhyay;
Email:\emph{banerjee.debi@hyderabad.bits-pilani.ac.in}

\textbf{ABSTRACT}

Histidine (His) is the most reactive amino acid at enzyme active sites.
Multiple post-translational modifications (functions) are reported for
His side chains. The high-throughput sequencing techniques produce a
large number of protein sequences without functional annotations at the
amino acid level. Experimental characterization of His functions in
proteins is laborious and time-consuming. Computational characterization
based on protein sequences may complement the need. There are only a
handful of Histidine function prediction tools available and those
annotate only a single function. Here we curated a dataset of active
Histidine with known functions based on protein sequences obtained from
UniProt database (sample size n=1584) and trained against four machine
learning methods. The convolution neural network (CNN) model
(``\emph{Hist-i-fy} '') performed the best with 75\% overall accuracy.
The external validation of Hist-i-fy on phosphorylated histidine data
(sample size 34) showed 94.1\% prediction accuracy. For the first time,
we report multiple His function prediction, based on protein sequences
using deep neural networks. The inputs to the model are i) protein
sequence containing His, and ii) the His residue number. The model
predicts one out of the eight histidine functions, namely, acetylation,
ribosylation, glycosylation, hydroxylation, methylation, oxidation,
phosphorylation, and protein splicing. The novelty of the work is, it
predicts maximum number of histidine functions at a time with optimal
performance. There is a scope of improvement in the model upon
availability of a larger dataset. The model is available as a web
application (\emph{https://histify.streamlit.app/}) and a stand-alone
code\emph{https://github.com/dibyansu24-maker/Histify}).

\textbf{KEYWORDS:}

Histidine; post-translational modifications; Artificial Neural Network
(ANN); Convoluted Neural Network (CNN); Long Short-term Memory (LSTM);
Logistic Regression; protein sequence; UNIPROT database, accuracy,
recall, precision

\textbf{1. Introduction:} Enzyme functions are primarily executed
through the catalytic residues. With the availability of the
high-throughput sequence data, a large number of protein sequences are
known without functional characterizations {[}1{]}. Computational
characterization would facilitate rapid initial screening that can be
verified further with experimental observations. Cysteine (Cys) and
Histidine (His) are the two most important amino acid residues observed
at the catalytic sites of all enzyme classes{[}2{]} {[}3{]}. The thiol
group of cysteine amino acid side chain can undergo oxidation leading to
various chemical and post-translational modifications that impact the
structure and function of proteins in different capacities. A histidine
imidazole is an electron-deficient heteroaromatic ring (pKa = 6.8) that
makes it a suitable candidate for proton buffering, metal ion chelation,
and antioxidant agents. Due to the similar values of the imidazole ring
pKa and the physiological pH (= 7.4), His efficiently participates in
enzyme catalysis. His residue is particularly important in acid base
catalysis due to its amphoteric character. Apart from that, it
participates in elimination-addition and redox reactions. Experimental
characterizations are done for various His post-translational
modifications those are involved in protein-protein interactions and
catalysis. Extensive computational characterization of the cysteine
functions has been done by our group {[}4{]}, {[}5{]}, {[}6{]}. However,
the post-translational modification of His is less explored compared to
that of Cys or Lys. The computational characterizations of His
functions, so far, were reported for single modifications only. For
example, histidine phosphorylation sites were predicted using a
convoluted neural network (CNN) - based model, PROSPECT {[}7{]}, and
support vector machine-based model, pHisPred {[}8{]}. Transition
metal-binding sites for Cys and His were predicted by exploiting
position-specific evolutionary profiles using support vector machines
and neural networks {[}9{]}. The CNN-based prediction model, PROSPECT,
inputs a protein sequence and returns predicted histidine sites with
72\% accuracy. The transition-metal-binding sites of histidine and
cysteine were predicted from protein sequences with 73\% precision. To
the best of our knowledge, prediction of multiple His post-translational
modifications is not reported. For the first time, we attempt to predict
eight post-translational modifications of His from, i) protein sequence
and ii) His residue position only, using deep neural networks. The
convolution neural network (CNN) performed the best. The output of the
models yields the most probable His modification. The internal
evaluation accuracies are comparable to the single prediction methods,
\emph{albeit}, our results showed better performances than the existing
ones. The model was blindly tested for external evaluation on
independent phosphorylated Histidine data points.

\textbf{2. METHODS AND MATERIALS}

\textbf{2.1. Histidine Chemical and Post-translational Modifications}
There are eight chemical and Post-Translational Modifications (PTM)
(functions) annotated in this work (Figure 1). These His modifications,
namely, acetylation, ribosylation, glycosylation, hydroxylation,
methylation, oxidation, phosphorylation, and protein splicing, are
discussed below.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}
\caption{{Post-Translational Modifications of Histidine Residues considered in
this study
{\label{122681}}%
}}
\end{center}
\end{figure}

\emph{}

\textbf{2.1.1 Acetylation}

Acetylation is a process of transferring an acetyl group (-CH3CO) to a
molecule, mainly to N- or O- atoms, known as N-acetylation or
O-acetylation. Acetylation of His is recently reported for
Histidine-Tyrosine (HY) and Tyrosine-Histidine (YH) dyads treated with
acetic anhydride {[}10{]}. His undergoes N-acetylation at its imidazole
ring.

\textbf{2.1.2 ADP-Ribosylation}

Histidine ADP-ribosylation is a stress-induced rare phenomenon {[}11{]},
unlike frequently observed serine-ADP-ribosylation. Mimetics of
ADP-ribosylated Histidine was recently studied {[}12{]}.

\textbf{2.1.3 Glycosylation}

Different amino acids may undergo different types of glycosylation,
namely, C-linked, N-linked, O-linked, or S-linked {[}13{]}. Histidine
undergoes N-linked glycosylation.

\textbf{2.1.4 Hydroxylation}

Protein hydroxylation, a post-translational modification, is carried out
by 2-oxoglutarate-dependent dioxygenases. This post-translational
modification can be induced by hypoxia-induced-factor alpha (HIF-a) on
proline {[}14{]}. Hydroxylation may also involve protein-protein
interactions and downstream signalling. Apart from proline, lysine,
asparagine, aspartate, and histidine can also undergo hydroxylation
modification {[}15{]}.

\textbf{2.1.5 Methylation}

The actin and myosin proteins undergo the post-translational
modification (PTM) of histidine methylation. There are two different
locations where it can happen: 1-methyl histidine (1MeH) and 3-methyl
histidine (3MeH) {[}16{]}.

\textbf{2.1.6 Oxidation}

Under unusual or stressed conditions histidine undergoes oxidation to
2-oxo-histidine (2-oxo-His) (Figure 1). Photo-induced oxidation of
Histidine leading to various cross-links, including intact His, Lys and
Cys, was observed in high-molecular weight (HMW) fractions of monoclonal
anti-bodies {[}17{]}. Oxidation of His residue is also observed in
proteins from cells undergoing oxidative stress {[}18{]}. The 2-oxo-His
changes the dissociation pattern of peptide ions in Mass-spectroscopy
studies {[}19{]}.

\textbf{2.1.7 Phosphorylation}

His phosphorylation is crucial step in various cellular processes, such
as signal transduction, cell cycle, proliferation, differentiation, and
apoptosis, Phosphorylated His contributes 6\% to all the phosphorylated
amino acids. However, phosphorylation of His is less explored compared
to phosphorylated serine, threonine and tyrosine. Recently a
consolidated database on phosphorylated His (HisPhosSite) is available
{[}20{]}. Histidine Kinase (HK)s is one of the classical non-animal
kingdom kinases that phosphorylate His, although, in a 2-step manner -
i) transfer phosphate from ATP to His and ii) then transfer the
phosphate to an aspartate residue {[}21{]}.

\textbf{2.1.8 Protein Splicing}

Protein splicing is triggered via acid-base catalysis that involves
multiple conserved His at the active site. Histidine probably plays dual
role in protein splicing, first as a general base to start acyl shift
splicing and next as a general acid to break the scissile bond at the
N-terminal splicing junction {[}22{]}.

\textbf{2.2 Sequence signatures around different His post-translational
modifications:}

Many of the His post-translational modifications were identified with
specific sequence signatures or motifs. For example, His hydroxylation
motif is a part of Hydrogen-bond (H-bond) cluster that is brought into
the register by GXXG motif {[}23{]}. For His methylation, the common
motif observed in short methylated peptides was GHXHXH {[}24{]}.
Histidine acetylation motif deduced from mass spectrometry data based on
diacetyl-fed rat lung proteins was GXPGXXGHXGXXG {[}25{]}. However, some
of the Histidine post-translation modifications do not carry sequence
signatures. For example, no specific sequence motif is reported for His
glycosylation. For His phosphorylation, no clear sequence motif was
identified {[}26{]}.

\textbf{2.3 Training dataset generation for Histidine post-translational
modifications}

There are eight His post-translational modifications (Figure 1)
annotated in this work based on the availability of protein sequences
from the UniProt database {[}27{]}. From the ``Keyword'' subsection of
UniProt, category name ''PTM'' was selected to track all possible
post-translational modifications. The text filters (not case sensitive)
-- ``His'', or ``Histidine'' were used to identify the experimentally
annotated His functions from the PTM category, curated on November 2022.
A total of sixteen modifications were identified, some of those have
very few data points. Finally, eight modifications were selected for the
training dataset with a number of data points more than or equal to
twenty (Table 1).\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Modifications} & \textbf{Number of Datapoints}\tabularnewline
Methylation & 303\tabularnewline
Acetylation & 172\tabularnewline
Ribosylation & 20\tabularnewline
Glycosylation & 105\tabularnewline
Phosphorylation & 532\tabularnewline
Oxidation & 101\tabularnewline
Protein splicing & 329\tabularnewline
Hydroxylation & 22\tabularnewline
\bottomrule
\end{longtable}

\emph{Table 1: Statistics of the His post-translational modifications
obtained from UNIPROT database}

\textbf{2.4 Test dataset generation:}

Test dataset was curated from the mass spectroscopy data {[}28{]},
available from the Supplementary Table 1 of that reference. The UniProt
ID was used to retrieve the sequence from the UniProt database and the
corresponding His residue number. All these His residues are
phosphorylated. This independent test dataset consists of 34
phosphorylated His.

\textbf{2.5 Processing of the training dataset} \textbf{2.5.1 Selection
of input parameters for deep learning models:}The training dataset was
pre-processed by selecting a stretch of amino acids from each protein
sequence with His of interest at the centre and that is flanked by amino
acids with a variable window size, from three to ten. The length of the
amino acid sequence will be 2(n)+1 for window size n. For example, amino
acid sequence length will be seven for window size three. Hence, all the
training sequences will have equal number of amino acids with His (of
interest) at the centre, for a given window size. This set of sequences
(per window size) were used as the input (X-parameter) for deep neural
network models. The Y-parameters were the post-translational
modifications. A representative input file to the deep neural network
model is shown (Table 2). The relative performances of variable window
sizes were tested.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\emph{S.No} & \emph{Modified sequence} & \emph{Residue No.} &
\emph{Modifications}\tabularnewline
\textbf{0} & PPGRRMGHAGAIIAG & 299 & Acetylation\tabularnewline
\textbf{1} & RTIYLCRHGESEFNL & 257 & Acetylation\tabularnewline
\textbf{2} & YKLIMLRHGEGAWNK & 11 & Acetylation\tabularnewline
\textbf{3} & YKLIMLRHGEGAWNK & 11 & Acetylation\tabularnewline
\textbf{4} & RSIYLCRHGESELNL & 259 & Acetylation\tabularnewline
\textbf{\ldots{}.} & \ldots{}. & \ldots{}. & \ldots{}.\tabularnewline
\textbf{1579} & VSFGSSCHGAGRKMS & 882 & protein-splicing\tabularnewline
\textbf{1580} & VSFGSSCHGAGRKMS & 881 & protein-splicing\tabularnewline
\textbf{1581} & DALFSTVHGAGRVMS & 781 & protein-splicing\tabularnewline
\textbf{1582} & EALFSTVHGAGRVMS & 790.0 &
protein-splicing\tabularnewline
\textbf{1583} & AALRSTIHGAGRVMS & 758.0 &
protein-splicing\tabularnewline
\bottomrule
\end{longtable}

\emph{Table 2: Representation of the input file used in the deep neural
networks. The total number of datapoints (including all the
modifications) is 1584 and 3 input parameters (2 X-Parameters and 1
Y-parameter) were used}

\textbf{2.5.2 Tokenization of the data:}Character tokenization was
performed to convert text (Table 2) into a list of characters using
keras pre-processing library {[}29{]}. It builds a corpus of all
characters and assigns a number to each character. After tokenization,
1584x15 dimension matrix was converted to a string of integers (Table
3). (15 corresponds to the sequence length, that is of window size 7).
Finally, these integers were considered as X parameters, rather than the
alphabetical characters (column 2 of Table 2). The Y-parameter was
processed using Label Binarizer {[}30{]} which accepts categorical data
as input and returns a NumPy array. The training dataset was randomly
split into train and test dataset in a ratio of 2:1 using the Sklearn
library function train\_test\_split. Thus, 1584 data points produced
1061 entries for training, and 523 for testing.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllllll@{}}
\toprule
& \textbf{0} & \textbf{1} & \textbf{2} & \textbf{3} & \textbf{4} &
\textbf{5} & \textbf{6} & \textbf{7} & \textbf{8} & \textbf{9} &
\textbf{10} & \textbf{11} & \textbf{12} & \textbf{13} &
\textbf{14}\tabularnewline
\textbf{0} & 15 & 15 & 1 & 9 & 9 & 16 & 1 & 2 & 4 & 1 & 4 & 7 & 7 & 4 &
1\tabularnewline
\textbf{1} & 9 & 5 & 7 & 17 & 6 & 20 & 9 & 2 & 1 & 10 & 3 & 10 & 13 & 14
& 6\tabularnewline
\textbf{2} & 17 & 12 & 6 & 7 & 16 & 6 & 9 & 2 & 1 & 10 & 1 & 4 & 19 & 14
& 12\tabularnewline
\textbf{3} & 17 & 12 & 6 & 7 & 16 & 6 & 9 & 2 & 1 & 10 & 1 & 4 & 19 & 14
& 12\tabularnewline
\textbf{4} & 9 & 3 & 7 & 17 & 6 & 20 & 9 & 2 & 1 & 10 & 3 & 10 & 6 & 14
& 16\tabularnewline
\textbf{\ldots{}.} & \ldots{}. & \ldots{}. & \ldots{}. & \ldots{}. &
\ldots{}. & \ldots{}. & \ldots{}. & \ldots{}. & \ldots{}. & \ldots{}. &
\ldots{}. & \ldots{}. & \ldots{}. & \ldots{}. & \ldots{}.\tabularnewline
\textbf{1579} & 8 & 3 & 13 & 1 & 3 & 3 & 20 & 2 & 1 & 4 & 1 & 9 & 12 &
16 & 3\tabularnewline
\textbf{1580} & 8 & 3 & 13 & 1 & 3 & 3 & 20 & 2 & 1 & 4 & 1 & 9 & 12 &
16 & 3\tabularnewline
\textbf{1581} & 11 & 4 & 6 & 13 & 3 & 5 & 8 & 2 & 1 & 4 & 1 & 9 & 8 & 16
& 3\tabularnewline
\textbf{1582} & 10 & 4 & 6 & 13 & 3 & 5 & 8 & 2 & 1 & 4 & 1 & 9 & 8 & 16
& 3\tabularnewline
\textbf{1583} & 4 & 4 & 6 & 9 & 3 & 5 & 7 & 2 & 1 & 4 & 1 & 9 & 8 & 16 &
3\tabularnewline
\bottomrule
\end{longtable}

\emph{Table 3: Tokenized 1584*15 matrix (1584 entries x 15 amino acids)}

\textbf{2.6 Algorithms}

Four classification models (namely, Logistic regression and Deep
learning algorithms - Convolutional Neural Networks (CNNs), Artificial
Neural Networks (ANNs), and Long Short-Term Memory (LSTM) networks) were
used to train and benchmark the dataset. These models can adjust the
weights of the input data to produce a value for each class, either
positive or negative, and can learn complex non-linear relationships
between features and the target variable. Logistic regression is a
simple and efficient algorithm that can be used for binary
classification problems. It works well when the relationship between the
features and the target variable is approximately linear. Deep learning
algorithms like ANNs, LSTMs, and CNNs are more powerful than logistic
regression for complex classification problems. They are designed to
automatically learn hierarchical representations of the data, which can
capture complex non-linear relationships between the features and the
target variable. In general, deep learning algorithms require more data
and computing resources than logistic regression but can produce results
with higher accuracy on complex classification tasks.

\textbf{2.6.1 Logistic regression}

It is commonly used for prediction and classification problems. In the
current study the model consists of eight classes (eight His
modifications). As logistic regression is a binary classification
method, One-Versus-Rest (OVR) logistic regression method was used where
the model is trained separately for each class to determine if an
observation is part of that class or not (making OVR a binary
classification problem). The method assumes that every classification
issue (whether involving class 0 or something else) stands on its own.

\textbf{2.6.2 ANN}

Artificial neural network (ANN) is a single hidden layer neural network
(consists of input layer, hidden layer and output layer) that attempts
to categorize each observation as one out of many discrete classes. The
input to the model could be either categorical or numeric and the
dependent variable (Y-parameter) should be categorical.

\textbf{2.6.3 LSTM}

Long-Short Term Memory (LSTM) recurrent neural networks are specialized
recurrent neural networks. Recurrent neural networks (RNN) run in cycles
that receive the input of network activations to the current time step
from the previous time step. These activations are stored (for an amount
of time, not fixed a priori) in the internal states of the network,
known as long-short-term memory. Thus LSTM-RNNs can exploit a
dynamically changing contextual information to transform an input
sequence to an output sequence. LSTM has multiple hidden layers, in
contrast to a single hidden layer in ANN.

\textbf{2.6.4 CNN}

A convolutional neural network consists of an input layer, multiple
hidden layers and an output layer. At least one or more hidden layers
should perform convolutions between the convolution kernel and the layer
of input matrix. (Convolution is a mathematical operation, that is a dot
product of two functions.) The convolution operation generates a feature
map, as the convolution kernel slides along the input matrix for the
layer and prepare the input of the next layer. This layer is followed by
pooling layers, fully connected layers, and normalization layers those
enable spatial hierarchical feature learning by backpropagation, in an
automated manner.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image21/image21}
\caption{{Workflow of CNN Model. The number of input and output features, and the
number of batch normalization are shown in parenthesis. A ``None'' value
in the shape represents any size (large than or equal to 1) in that
dimension.
{\label{720865}}%
}}
\end{center}
\end{figure}

The workflow of the CNN model on the training dataset is shown with the
number of input and output features and the batch normalization (Figure
2). The first layer, input layer, consists of neurons that are connected
to individual inputs those are passed to the next layer (embedding
layer), without having any weights (or biases). Embedding layer
transforms each word into a vector of a pre-determined length. (The
vocabulary is first encoded as a series of integers, and then the
embedding layer retrieves the embedding vector for each word-index.)
Convolutional layer is the core component of a CNN. It has a collection
of filters (128 in this case, Figure 2) whose settings will be learnt as
part of the training process. In most of the cases, the length of the
sequence is less than the size of the filters. Each filter is used to
generate an activation map by convolving with the input. Average pooling
is used to construct a down-sampled (pooled) feature map by computing
the average for each patch of the feature map. Normalization in a Neural
Network is called ''Batch Norm,'' and it takes place between the layers
of the network rather than with the raw data itself. Mini-batches are
processed instead of the entire dataset at once. Learning is simplified
as the pace of the instruction is increased. The Global Average
Pooling-1D layer is used to reduce the dimensionality of the input data
after the convolutional layers. Dense Layer is a layer of neurons within
which each neuron gets input from all the neurons in the preceding
layer, thus becomes ``dense'' and fully connected. Output layer is
typically the last layer in the network, and it consists of neurons that
represent the different classes or categories that the network is
trained to predict. Here, two dense layers were added, each with RELU
activation function and batch-normalization. The first dense layer has
256 units, and the second dense layer has 128 units respectively. The
final Dense layer has 8 units, representing the number of classes in the
classification task. The activation function used here is softmax, which
converts the output into probabilities for each class. The output layer
neurons are connected to all the neurons in the previous layer, and the
weights and biases of the neurons in the output layer determine the
strength of the predictions for each class. \textbf{}

\textbf{2.7 Evaluation meters}

To evaluate the efficacy of the models, the confusion matrix and the
classification report were used. The confusion matrix is a compact
representation of the results of a classification task prediction. It is
used to evaluate the overall performance in terms of accuracy,
precision, recall, and F1-score. A combination of correct (True) and
incorrect (False) classifications is used to determine these measures.
In this context, the projected classes may be generally referred to as
''Positive'' or ''Negative.'' Total four parameters are used, True
Positive (TP) (case is positive, prediction is also positive), True
Negative (TN) (case is negative and prediction is also negative), False
Positive (FP) (case is negative and prediction is positive) and False
Negative (FN) (case is positive and false negative). Using these four
parameters evaluation meters, namely, accuracy, precision, recall and
F1-score, were computed.

\textbf{Accuracy = (TP + TN)/ (TP + TN + FP + FN) Eq 1}

Accuracy is the proportion of the true positive and false negative
instances predicted correctly by the classifier with respect to all the
data points.

\textbf{Precision = TP/ (TP + FP) Eq 2}

Precision is the proportion of the true positive instances predicted
correctly by the classifier, with respect to all the data
points.\textbf{Recall = TP/(TP+FN) Eq 3}Recall is the proportion of the
true positive instances predicted correctly out of the true positive and
false negative data points. In other words, the ability of a classifier
to retrieve all appropriate examples is called recall (also known as
sensitivity).\textbf{F1 Score = 2*(Recall * Precision) / (Recall +
Precision) Eq 4}F1 scores include both precision and recall into their
calculation, they are often regarded as inferior to accuracy metrics. It
ranges from 0.0 to 1.0. To compare classifier models, it is recommended
to utilise the weighted average of F1 rather than overall
accuracy.\textbf{Weighted average F1-score =\(\sum_{i=1}^{8}{\mathbf{F}\mathbf{1}\mathbf{-}\mathbf{score*(no.\ of\ instances))/8}}\)Eq 53}

\textbf{RESULTS AND DISCUSSION}

\textbf{3.1 Selection of the optimal window size based on the neural
network model performances:}

To address the objective of the study - prediction of multiple His
modifications based on a given protein sequence - we initially attempted
to optimize the target amino acid sequence length (variable window size
from three to ten, as described in the method section). The results were
shown for CNN model. The accuracy of CNN models follows a Gaussian
distribution between window sizes three to ten (Table 4). The maximum
accuracy on the training dataset was observed for window size seven
(that is sequence length of 15 {[}2x7=+1{]} amino acids). Hence, window
size seven was as selected default for subsequent model creation,
training and validation.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\emph{Window size} & \emph{Respective Accuracy of CNN
Model}\tabularnewline
3 & 73.60\%\tabularnewline
4 & 73.80\%\tabularnewline
5 & 72.23\%\tabularnewline
6 & 75.14\%\tabularnewline
7 & 75.47\%\tabularnewline
8 & 69.20\%\tabularnewline
9 & 70.70\%\tabularnewline
10 & 71.70\%\tabularnewline
\bottomrule
\end{longtable}

\emph{Table 4: Performance of the CNN model on the training dataset with
variable window size}

\textbf{3.2 Selection of the optimal neural network model on the
training dataset:}

The training dataset was benchmarked against four different classifiers,
namely, Logistic regression, ANN, LSTM and CNN. Some of these
classifiers are simple and computational less expensive and two others
(LSTM and CNN) are complex.

\textbf{3.2.1 Logistic regression}

The overall performance of logistic regression is shown (Table S1). The
precision, recall and F1-score vary for different modifications.
Logistic regression was unable to predict methylation and
phosphorylation based on the current validation dataset. The possible
reasons could be that i) the target label has no linear correlation with
the features and/or ii) the sample sizes (in the validation dataset) are
uneven with respect to different classes (Table S1). The accuracy from
this classifier on the validation dataset was 0.67.

\textbf{3.2.2 ANN}

The accuracy achieved using ANN model was 70\%, slightly better than
that of the logistic regression. This is presumably due to the presence
of three layers in ANN (an input layer, a hidden layer and an output
layer) in contrast to the logistic regression. Moreover, performance of
logistic regression reduces when trained on noisy data or the samples
are unevenly distributed between classes. Variation was observed in the
prediction results for different modifications (Table S2). ANN model was
unable to predict oxidation modification from the current dataset,
although, it has successfully predicted methylation and phosphorylation
modifications, unlike logistic regression. To note, the modifications in
validation dataset varies across the classifier, as the train to test
(validation) dataset was randomly split into 2:1 ratio and each random
split contains different ratio of His modifications. For example, in
logistic regression, support (the number of validation data point) value
for phosphorylation was only 8 in contrast to 175 in ANN model. This
could presumably justify why logistic regression was unable to predict
phosphorylation whereas ANN has accomplished it successfully.

\textbf{3.2.3 LSTM}

The accuracy obtained from LSTM was 71\%, better than those obtained
from logistic regression and ANN models. The results produced by LSTM
were better, most likely due to feed data back while training. LSTM
works the best on a known set of patterns or sequences. As mentioned
above, His hydroxylation and methylation were observed with
characteristic sequence motifs {[}23{]} {[}24{]}. Moreover, protein
splicing involves multiple conserved His at the enzyme active sites
{[}22{]}. The conserved patterns for these three His modifications lead
to improved recall value (that is, high rate of true positive prediction
with respect to false negative values) (Table S3). Despite of improved
performance of LSTM, the classifier was unable to compute precision,
recall and F1-score for ribosylation and oxidation modifications.

\textbf{3.2.4 CNN}

\textbf{}The overall accuracy obtained from CNN model was the 75.47\%,
best out of all the classifiers. The notable observation was that the
CNN model was capable of predicting all the modifications, unlike other
classifiers (Table 5). The variation in predicting different
modifications also exists in CNN model as in other classifiers. The
superior performance of the CNN model is most likely due to the
application of convolutional layers, those automatically lowers the
dimensionality of sequences, yet preserving the information. The
logistic regression works the best with a predefined relation between
input and output, that was not so explicit in the training dataset. As
ANN is a simple neural network model with only one hidden layer,
learning was less accurate. LSTM works the best with pattern recognition
thus the model was capable of better prediction of hydroxylation,
methylation and protein splicing with known patterns. However, the
performance of acetylation prediction was consistently poor across all
the classifiers, although sequence-specificity of His acetylation was
reported in literature {[}10{]}{[}25{]}. Comparing the overall
performance from the above benchmarking exercise (Table S4), CNN model
was selected for further His modification prediction for an unknown
protein. This model, termed as Hist-i-fy, was tested on an independent
dataset of His phosphorylation, obtained from mass spectroscopy.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
PTM\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
precision\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Recall\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
f1-score\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Support\#\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Acetylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.32\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.20\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.24\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
46\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Ribosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.31\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.62\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.42\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Glycosylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.73\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.77\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.75\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
35\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
hydroxylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.67\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.80\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
4\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
methylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.78\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.54\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.64\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
103\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
oxidation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.53\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.69\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
45\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
phosphorylation\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.80\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.79\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.80\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
175\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
protein-splicing\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
1.00\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
107\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
macro avg\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.64\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.74\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.67\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
523\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
weighted avg\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.76\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.75\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0.74\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
523\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 5: Classification report for CNN model; \textsuperscript{\#}number
of instances from validation dataset

\textbf{3.3 Comparison of the results from the present study and the
literature reports:}

Currently, there are only a few His post-translational modification
prediction tools reported in the literature, namely, pHisPred,
iPhosH-PseAAC, Prospect and His-Cys metal binding prediction. All of
these tools can predict one His function at a time. His-Cys metal
binding prediction tool can predict metal-binding of His and Cys amino
acids. The training data sets used to develop these prediction tools
were small enough and the sizes were comparable to the dataset used in
this study (Table 6). The internal prediction accuracies for
iPhos-PseAAC, Prospect and pHisPred were 33\%, 72\% and 73\%
respectively. His-Cys metal binding sites (predicting two amino acids at
a time) have reported 73\% precision and 61\% recall values. The best
internal prediction accuracy was obtained from the current model,
Hist-i-fy. However, there is a scope of improvement for the model
performance upon availability of larger data sets. For external
validation, we have tested the Hist-i-fy model on an independent dataset
of histidine phosphorylation, generated from mass spectroscopy, sample
size, 34. The prediction accuracy of the Hist-i-fy model on the test
dataset was 94.1\% only. To note, the training and the test datasets are
independent of each other and the test dataset consists of only one
modification, phosphorylation. Moreover, the training accuracy was a
cumulative accuracy for all the modifications and the test accuracy was
only for phosphorylation. Thus, the accuracy observed in the test
dataset was higher than that in the training dataset. For comparison
purpose, the same test dataset was used for histidine phosphorylation
prediction using pHisPred tool. The prediction accuracy from pHisPred
was 94.0, comparable to the results from Hist-i-fy. For the first-time
we report prediction of eight histidine modifications from a given
protein sequence, with a reasonably high accuracy.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Single Histidine Prediction} & \textbf{Single Histidine
Prediction} & \textbf{Single Histidine Prediction}\tabularnewline
\textbf{Name of the software} & \textbf{No. of sequences used to train
the model} & \textbf{Performance of the algorithms (on the test dataset,
size = 34)}\tabularnewline
iPhosH-PseAAC & 795 & Not working\tabularnewline
PROSPECT & 172 & Not working\tabularnewline
pHispred & 560 & 94.0\%\tabularnewline
\textbf{Multiple amino acid Prediction} & \textbf{Multiple amino acid
Prediction} & \textbf{Multiple amino acid Prediction}\tabularnewline
His-Cys metal binding prediction & 2982 & Not working\tabularnewline
\textbf{Multiple Histidine Prediction} & \textbf{Multiple Histidine
Prediction} & \textbf{Multiple Histidine Prediction}\tabularnewline
\textbf{Hist-i-fy} & 1584 & 94.1\%\tabularnewline
\bottomrule
\end{longtable}

\emph{Table 6: Histidine function prediction models}

\textbf{3.4 Availability of Hist-i-fy:}

The source code for Hist-i-fy is available
at\emph{https://github.com/dibyansu24-maker/Histify}and can be easily
operated on any platform, following the instructions on GitHub.
Moreover, Hist-i-fy enables users to train the model with the
user-defined data.

Hist-i-fy prediction server is hosted
on\emph{https://histify.streamlit.app/}using Streamlit, a powerful
Python library for building interactive web applications. Through the
intuitive and user-friendly interface of Streamlit, users can predict
sequence modifications in two modes: single sequence or multiple
sequences (to be uploaded as a CSV file containing sequences and
Histidine residue numbers). Streamlit seamlessly integrates with GitHub,
facilitating the hosting process and ensuring a smooth user experience.
The Hist-i-fy model processes the input data and generates predictions
for the respective sequences, providing users with comprehensive
insights into their sequence modifications.

\textbf{4. CONCLUSION:}

\textbf{}The functional characterization of the proteins and their amino
acids lag behind the protein sequence determination. Experimental
characterization is time-consuming and laborious, those can be
complemented by computational methods. Histidine being one of the most
important amino acid at the enzyme active site, functional
characterization would potentially address many biological problems. His
undergoes multiple modifications, sixteen such was reported in the
UNIPROT database. However, only a handful of His (single) function
prediction tools are available. Objective of this study was to predict
multiple histidine modifications (functions) based on protein sequences.
Here we trained and validated eight histidine modifications using
Convoluted Neural Network model. The training dataset was curated from
UNIPORT database. The overall accuracy produced by the CNN model was
75\%, although, prediction of individual modifications varies, depending
on the data size, existing sequence pattern etc. The external validity
of the model was tested on an independent phosphorylation dataset
obtained from proteomics study. Accuracy of phosphorylation prediction
(external validation) was 94.1\% much higher than that of accuracy
(including all the eight modifications) from internal validation. The
external validity result was comparable from existing His
phosphorylation prediction tool - pHisPred. The final CNN model (termed
as Hist-i-fy) is publicly available as a web application and a
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