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\begin{document}

\title{ Poxviridae Protein kinases as targets for control of LSDV , Monkeypox
like outbreaks}



\author[1]{Ramars Amanchy}%
\author[1]{Poornachandra Yedla}%
\author[1]{Saikumar Nalla}%
\author[1]{Vyshnavi Racha}%
\author[1]{Pranav Bhamidipati}%
\affil[1]{Indian Institute of Chemical Technology}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Lumpy skin disease virus has infected cows across the world resulting in
the death or destruction of at least a million cows. Poxviridae family
includes 88 such viruses that cause deadly diseases like smallpox,
monkeypox and lumpy skin disease. Pox virus induced infections have been
known to be sporadic and zoonotic. We identified two novel protein
kinases in poxviridae genomes. Multiple sequence alignment and
phylogenetic analysis of these pox viral protein kinases revealed the
sequence conservation and evolutionary relationships of these viral
protein kinases. AI driven methods revealed 3D structures and active
site of the two protein kinases. The viral kinases also showed
remarkable structural conservation. Virtual screening of pox virus
kinases identified LSTK and LYK as druggable targets and 2 FDA approved
kinase inhibitors (lapatinib and pazopanib) were recognized as potential
inhibitors of LSTK and LYK. Molecular dynamics simulations identified
these inhibitors to be competitive inhibitors of the new kinases. These
findings provide valuable insights that can be leveraged in the
development of antiviral therapeutics for pox virus infections LSDV and
Monkeypox.%
\end{abstract}%



\sloppy


\section*{\texorpdfstring{\emph{Poxviridae} Protein kinases as targets
for control of \emph{LSDV} , Monkeypox like
outbreaks}{Poxviridae Protein kinases as targets for control of LSDV , Monkeypox like outbreaks}}

{\label{poxviridae-protein-kinases-as-targets-for-control-of-lsdv-monkeypox-like-outbreaks}}

Poornachandra Yedla\textsuperscript{1,2}, Saikumar
Nalla\selectlanguage{greek}\textsuperscript{φ1}\selectlanguage{english}, Vyshnavi Racha\selectlanguage{greek}\textsuperscript{φ1}\selectlanguage{english}, Pranav
Bhamidipati\textsuperscript{1,3}, Ramars Amanchy*\textsuperscript{1}

Author Affiliations:

\textsuperscript{1} Division of Applied Biology, CSIR-IICT (Indian
Institute of Chemical Technology), Ministry of Science and Technology
(GOI), Hyderabad 500007, Telangana, India.

\textsuperscript{2} Department of Pharmacogenomics, Institute of
Translational Research, Asian Healthcare Foundation, Survey No 136, Plot
No 2/3/4/5, 1, Mindspace Road, Gachibowli, Hyderabad 500032, Telangana,
India.

\textsuperscript{3}Department of Life Sciences, Imperial College London,
London SW7 2AZ, UK.

\selectlanguage{greek}\section*{\texorpdfstring{\textsuperscript{φ}both authors contributed
equal}{φboth authors contributed equal}}\selectlanguage{english}

{\label{ux3c6both-authors-contributed-equal}}

\section*{\texorpdfstring{*Corresponding author: Dr. Ramars Amanchy,
Email:
ramars@iict.res.in\emph{}}{*Corresponding author: Dr. Ramars Amanchy, Email: ramars@iict.res.in}}

{\label{corresponding-author-dr.-ramars-amanchy-email-ramarsiict.res.in}}

Abstract:

Lumpy skin disease virus has infected cows across the world resulting in
the death or destruction of at least a million cows. Poxviridae family
includes 88 such viruses that cause deadly diseases like smallpox,
monkeypox and lumpy skin disease. Pox virus induced infections have been
known to be sporadic and zoonotic. We identified two novel protein
kinases in poxviridae genomes. Multiple sequence alignment and
phylogenetic analysis of these pox viral protein kinases revealed the
sequence conservation and evolutionary relationships of these viral
protein kinases. AI driven methods revealed 3D structures and active
site of the two protein kinases. The viral kinases also showed
remarkable structural conservation. Virtual screening of pox virus
kinases identified LSTK and LYK as druggable targets and 2 FDA approved
kinase inhibitors (lapatinib and pazopanib) were recognized as potential
inhibitors of LSTK and LYK. Molecular dynamics simulations identified
these inhibitors to be competitive inhibitors of the new kinases. These
findings provide valuable insights that can be leveraged in the
development of antiviral therapeutics for pox virus infections LSDV and
Monkeypox.

\emph{\textbf{Abbreviations:} LSDV} -- Lumpy skin disease virus; LSTK
--\emph{LSDV} serine threonine kinase; LYK -- \emph{LSDV} tyrosine
kinase; MSTK- Monkeypox serine threonine kinase; LYK -- \emph{LSDV}
tyrosine kinase; MLKL -- Mixed lineage kinase domain-like.

1. Introduction

Lumpy skin disease (LSD) is caused by a virus that belongs to the
genus~\emph{Capripoxvirus} ~within the family~\emph{Poxviridae} . Lumpy
Skin Disease virus (LSDV) primarily infects cows \textsuperscript{1} and
also affect other species such as water buffaloes, sheep,
goats\textsuperscript{2}. Lumpy skin disease virus has infected millions
of cows across the world and the zoonotic nature of these viruses
threatens other closely related animals. Monkeypox is one such member
of~\emph{Poxviridae} ~family (Subfamily:~\emph{Chordopoxvirinae} ,
Genus~\emph{Capripoxvirus} ) that has shocked the entire planet during
COVID by infecting humans who are immunocompromised. Many of the pox
virus induced infections are sporadic and zoonotic in
nature\textsuperscript{3}. Lumpy skin disease is characterized by the
formation of nodules or lumps on the skin and mucous membranes, fever
and reduced milk production \textsuperscript{4,5}. Pox viruses such
as~\emph{Molluscum contagiosum} ~can cause human skin
infections\textsuperscript{6} as well.

Poxviridae viruses are double stranded DNA viruses that contain
typically 100-150 genes\textsuperscript{7} that appear to have been
captured primarily from hosts by genetic recombination events during
evolution \textsuperscript{8}. LSDV genome has 156 putative genes and
three of these genes encode viral protein kinases, that may
phosphorylate specific proteins to control various cell signalling
processes. These kinases might play a role in the LSDV's replication,
pathogenesis and other cellular functions like translation
initiation\textsuperscript{9} and nuclear egress, evasion of host immune
response as seen from other viruses \textsuperscript{10}. Protein
Serine/threonine \textsuperscript{10} and tyrosine
kinases\textsuperscript{11} have been known to be present in viral
genomes and protein phosphorylation has also been found an important
mechanism that regulates viral replication \textsuperscript{12}. The
discovery of a vaccinia virus kinase (VPK2) has shown the importance of
protein phosphorylation in poxviral biogenesis \textsuperscript{13}.
Targeting LSDV and other conserved family protein kinases could be an
essential step in containing the infection, preventing the global spread
of the virus and to stop economic burden arising due to cattle death.

In the present work, we have employed biochemical-informatics~approaches
for the identification of LSDV viral protein kinases, predict their
structures and model their active sites, conservation followed by
virtual screening for inhibitors. Two interesting LSDV kinases; a
serine/threonine kinase (LSTK) and a tyrosine kinase (LYK) were
identified with homologs and high degree of conservation across various
viruses. Prediction of a structure has taken a big leap forward from the
major experimental approaches to \emph{in
silico}methods\textsuperscript{14-16}. Modern structure prediction tools
using deep learning and artificial intelligence-based approaches
demarcate the beginning of a new era in protein structural
bioinformatics~\textsuperscript{17}. A myriad of programs incorporate
information from amino acid sequences to predict the distance and
torsion distribution of the protein by employing deep learning, machine
learning based computational algorithms by effectively combining
homology modeling, threading and \emph{ab initio}
strategies\textsuperscript{18,19}. Among
these,~ALPHAFOLD2\textsuperscript{20}~and~ESMfold
\textsuperscript{21}~have been used to predict the protein structure
models approximated to high accuracy. Protein kinases have been the most
druggable targets\textsuperscript{22}.~~Since, the kinases in most
organisms have well conserved active sites and catalytic domains, we
adopted\emph{bioinformatics} approaches to repurpose FDA approved human
kinase inhibitors to LSDV and monkeypox kinases. The utilization of
existing drugs for the treatment of LSD through repurposing strategies
may yield promising outcomes.

\textbf{2. Materials and Methods}

2.1. Multiple sequence alignment of LSDV Serine/Threonine kinase and
Tyrosine kinases

Putative protein kinases predicted from \emph{LSDV} genome
analysis\textsuperscript{7} were screened in SMART\textsuperscript{23},
CDART\textsuperscript{24}, CDD \textsuperscript{25} and
pfam\textsuperscript{26} to confirm their protein kinase domains and
families. Sequences of~a serine/threonine protein kinase (LSTK) and a
tyrosine kinase (LYK)~from~\emph{LSDV} and other poxviridae were
obtained from NCBI. BLASTp was performed for both sequences in NCBI and
the related sequences were examined for multiple sequence alignment. The
multiple sequence alignment of sequences obtained from protein clusters
from NCBI for both LSTK and LYK were aligned using
ClustalW\textsuperscript{27}.

2.2. Phylogenetic analysis of LSDV Kinases: The IQ-TREE
program\textsuperscript{28}, was employed to construct phylogenetic tree
based on maximum likelihood. Best-fit substitution model was selected
using ModelFinder algorithm\textsuperscript{29}, based on the Bayesian
information criterion (BIC), with ultrafast bootstrapping (1000
iterations) as described in detailed in Supplementary methods. The tree
was visualized using the FigTree software (v1.4.4)\textsuperscript{30}.

2.3. Structure prediction for LSTK and LYK and structural comparison:
The sequences of LSTK (NCBI Gene ID- 921586; Accession: NP\_150459) and
LYK (Gene ID- 22595678; Accession: AAN02711.1) were used to model 3D
structure of the kinases. Structures were predicted by comparing two AI
based programs, AlphaFold2 (AF2) \textsuperscript{20}and ESMFold (EF)
\textsuperscript{31}, \textsuperscript{21}. The quality and accuracy of
the predicted models were evaluated using Ramachandran plot, using
Gaussian KDE (kernel density estimation) to plot the density of favoured
torsion angles (\selectlanguage{greek}φ \selectlanguage{english}and \selectlanguage{greek}ψ\selectlanguage{english}) in the protein structure. The structures of the
human MLKL (Mixed Lineage Kinase Domain-Like Protein) and
\emph{Monkeypox} STK were predicted using AlphaFold2 for structural
comparisons of \emph{LSDV} kinases were performed using
Chimera-X\textsuperscript{32}. The degree of similarity between the
proteins' structures was assessed by determining their RMSD (Root Mean
Square Deviation) and structure alignment (TM-Template Modelling) scores
by UCSF Chimera\textsuperscript{32} and TM-score\textsuperscript{33}.
Structures obtained from AlphaFold2 and ESMFold were compared by their
RMSD, pLDDT\textsuperscript{34}, perplexity\textsuperscript{21}, and pTM
scores.

2.4 Ligand preparation for virtual screening: A total of 88 approved
human kinase inhibitors were obtained from the DrugBank database
\textsuperscript{35}. The SMILES to 3D-structures generation was done
using Open Babel\textsuperscript{36}. Ligprep\textsuperscript{37}was
used to prepare the structures of the inhibitors by assigning bond order
and bond angles and by minimizing the structures using the OPLS-2005
force field. The Epik\textsuperscript{38}feature is used to ensure the
correct protonation state of the ligand and to generate tautomers of the
compounds at a target pH of 7.4 \selectlanguage{ngerman}±1.0. A maximum of 5 stereoisomers were
created for each chiral molecule\textsuperscript{39}.

2.5 Receptor preparation and grid generation: The target proteins (LSTK
and LYK) were prepared by addition of missing hydrogens, partial charges
and side chains using Protein Preparation Wizard module (Schrödinger
suite 2020-3). Energy minimization was carried out using default
constraint of 0.3 Å RMSD and OPLS-3 force field. The Sitemap tool
\textsuperscript{40} was used to identify the active site region of the
Kinases. Receptor grid is generated using the Receptor Grid Generation
Wizard in the Glide Grid module (Schrödinger suite 2020-3).

2.6. Virtual screening - Drug Repurposing: The predicted structures of
LSTK and LYK were screened against a library of FDA-approved kinase
inhibitors by High-Throughput Virtual Screening (HTVS) using the
Schrödinger Maestro module \textsuperscript{41}. HTVS and Standard
Precision (SP) docking was used to assess the effectiveness of ligand
binding towards target protein. PyMol and Maestro tools were used for
molecular visualization\textsuperscript{42}.

2.7. Molecular simulations: The classical molecular dynamics (MD)
simulations were carried out on selected inhibitors, using GROMACS
2022\textsuperscript{43}, to evaluate the substrate and inhibitor
binding interactions of LSTK and LYK. The protein was parameterized with
CHARMM-36force field provided in GROMACS\textsuperscript{44}. Ligand
topologies and coordinate files were generated for the CHARMM-36 force
field using the SwissParam tool\textsuperscript{45}. Two types of
protein-ligand complexes for LSTK and LYK were designed for the
simulation, one with bound ATP and the other with docked inhibitor at
the active site in presence of Magnesium ions. The protein-ligand
complexes underwent energy minimization using the steepest-descent
algorithm\textsuperscript{46} and the Verlet cut-off
scheme\textsuperscript{47}. An ensemble equilibration with a restricted
constant number of particles, volume, and temperature /canonical
ensemble (NVT) and an ensemble equilibration with a constant pressure,
and temperature (NPT) ensemble were then performed for a total time of
1ns under Particle Mesh Ewald (PME) electrostatics and 3D periodic
boundary conditions\textsuperscript{48}. Berendsen
thermostat\textsuperscript{49} was used in both the NVT and NPT runs.
For the NPT run an additional Berendsen barostat was used. Finally, a
production MD run of 10ns was carried out using a modified Berendsen
thermostat and Parinello-Rahman barostat\textsuperscript{50} before
proceeding further with ligand binding energy calculations and active
site modelling.

2.8. Principal component analysis

The FDA approved veterinary drugs for different diseases were selected
for the comparative analysis against human kinase inhibitor drug dataset
(88 compounds). 16 Chemical and molecular descriptors (molecular weight
(M.W), heavy atoms, aromatic heavy atoms, Fraction of
CSP\textsuperscript{3}, rotatable bonds, Hydrogen bond acceptors, H-bond
donors, Molecular refractivity (MR), Topological polar surface area
(TPSA), Bioavailability, lead likeness, Synthetic accessibility and
lipophilicity factors such as iLogP, XLogP, WLogP and MLogP were
calculated using SwissADME \textsuperscript{51}, and
ChemDraw\textsuperscript{52}. The PCA analysis was performed by
classical mean-centering and the normalization using R open source
program to reduce 18-dimensional vectors of each compound to a 2D
vector.

\emph{Results and Discussion}

3.1. Conservation of LSDV protein kinases: Around 350 viral genomes have
been identified to encode S/T/Y protein kinases\textsuperscript{53}.
Viruses with double stranded DNA and single stranded RNA as genetic
material encode kinases in their genomes.\emph{Poxviridae} family of
double stranded DNA viruses have 2 primitive protein kinases embedded in
their genomes. In Lumpy skin disease virus, we identified a
serine/threonine kinase (LSTK) and a tyrosine kinase (LYK) from their
sequences. Eukaryotic protein kinases typically have 7 \selectlanguage{greek}α-\selectlanguage{english}helices and 5
\selectlanguage{greek}β-\selectlanguage{english}strands and 2 long loops representing catalytic site containing HRD
domain and activation site that has the well-known DFG motif
\textsuperscript{54}. A comprehensive sequence and structure-based
analysis to find related kinases among viruses enabled us to identify
the conserved residues or motifs in kinase catalytic domain
across\emph{Poxviridae} . We observed no related kinases in eukaryotic
genomes, that resemble LSTK, whereas LYK had a distant relative amongst
vertebrates called mixed lineage kinase~domain-like protein (MLKL) that
is critical for necroptosis\textsuperscript{55}. We note that these
novel kinases from \emph{Poxviridae} may have functional assignments due
to their replication dependency for cellular machinery, in higher
organisms. They may also have similarity to viral oncogenes in function
in regulating cell signalling, human cancers and diseases. The viral
protein kinases are different from eukaryotic and prokaryotic protein
kinases \textsuperscript{56} in having only kinase domain.

LSTK has shown a high degree of conservation and presented clear
structural motifs and sequence motifs revealing the importance of the
conserved sequences (\textbf{Figure 1A, 1B} ). LYK clustered in 2
different groups of kinases based on their sequence conservation as
overall LYK from all animals has not shown great level of conservation
(\textbf{Figure 2AI, 2BI} ). These 2 different groups of viruses having
different sequences and seem to have categorically enriched certain
sequences in confinement to infect land animals (\textbf{Figure 2AI,
2BI} ) or birds (\textbf{Figure 2AII, 2BII} ) primarily. Although, LYK
from both groups differed significantly in sequence similarity yet
revealed structurally similarity as well that may be an index of
functional or substrate specificity. Variations reflected true viral
nature of modified sequences, to adapt to different host tissue and
cellular milieu and to bind to specific sequences when infecting a
specific host. Short sequence motifs were highly conserved amongst STKs
of \emph{poxviridae} (\textbf{Figure 1A} ) whereas LYK showed high level
of variations (\textbf{Figure 2A} ). Motifs that were highly conserved
in\emph{LSDV} kinases were present in both \selectlanguage{greek}α-\selectlanguage{english}helices, \selectlanguage{greek}β-\selectlanguage{english}strands as well
as loops (Figure 1B and 2B). Short Linear Motifs (SLiMs) were
predominant, but in a catalytic active site we see major kinase domain
like long sequence conservation as well. While LSTK presented 19
\selectlanguage{greek}α-\selectlanguage{english}helices (18 in monkeypox kinase) and 11 \selectlanguage{greek}β-\selectlanguage{english}sheets (\textbf{Figure 1B}
), LYK whose homolog was absent in \emph{monkeypox} showed 13-14
\selectlanguage{greek}α-\selectlanguage{english}helices and 7-8 \selectlanguage{greek}β-\selectlanguage{english}sheets (\textbf{Figure 2B} ). Each of the kinases
showed unique motifs that aid in structure conservation and that might
be necessary for protein function. A unique YVVKFVYE (\textbf{Figure
1AI,1BI} ) at \selectlanguage{greek}β\selectlanguage{english}4 gave an impression of FYVE domains, that bind to
phospholipids\textsuperscript{56}. Domain / motif prediction software
tools (Interpro\textsuperscript{57}, Scansite\textsuperscript{58},
pfam\textsuperscript{59}, Cofactor\textsuperscript{60}and Schrodinger),
anticipated active site of LSTK to span residues 80-359, while the ATP
binding site (predicted by Schrodinger and ATPbind\textsuperscript{61})
is expected to be located within residues 95-346. Similarly, for LYK,
the predicted active site ranges from residues 14-188, with the ATP
binding site also predicted to be within residues 95-346. These regions
exhibit a high degree of conservation, with minimal variations observed
(\textbf{Figure 1C, 2C} ). Prokaryotic proteins were believed to be
enriched with rare amino acids as opposed to modern living
entities\textsuperscript{62}.

We constructed maximum likelihood phylogenetic trees using protein
sequences from 26 different viruses belonging to 7 genera for LSTK and
22 different viruses from 7 genera for LYK. The phylogenetic analysis of
LSTK and LYK proteins revealed a remarkable level of conservation among
the related viruses with a high degree of similarity. The evolutionary
distance between the viral kinases was found to be very small,
indicating common evolutionary lineage (\textbf{Figure 1C} ). The
phylogenetic analysis of viral kinases revealed conserved structure and
sequence for both LSTK and LYK proteins across the studied genera. The
LSTK of Lumpy skin disease virus showed close relationship with Sheeppox
and Goatpox virus (\textbf{Figure 1C} ). While LYK of \emph{LSDV} showed
similarities with Myxoma, Deerpox, Sheeppox, and Goatpox virus with a
common ancestor (\textbf{Figure 2C} ). This suggests that viral kinases
have undergone minimal diversification, indicating their critical role
and functional significance in viral replication and pathogenesis.

The phylogenetic analysis of LSTK and LYK also revealed several notable
amino acid substitutions across the protein sequences and varying
degrees of conservation and divergence in the sequences. Amino acid
frequency analysis revealed enrichment of lysine, leucine and asparagine
in both LSTK and LYK (\textbf{Figure 1D, 2D} ) but no selective
enrichment of tryptophan, cysteine, tyrosine, phenylalanine, which were
generally~found to be abundant in the prebiotic environment. Hence it
can be concluded that there is possibility that these kinases have been
acquired by gene capture from higher organisms or happened due to
current evolutionary and environmental conditions. In LSTK, amino acids
like isoleucine, leucine, lysine, and phenylalanine, exhibited a higher
frequency of substitution (\textgreater{} 0.09) (\textbf{Figure 1D} ).
Similarly, in LYK, isoleucine, leucine, and lysine showed a higher
frequency (\textgreater{} 0.09) (\textbf{Figure 2D} ). These amino acids
displayed a relatively higher rate of substitution, suggesting their
potential functional or structural adaptability to
host\textsuperscript{63}. On the other hand, in LSTK, amino acids such
as cysteine, tryptophan, and methionine had lower substitution values,
with a frequency below 0.02 indicating their importance in structural
integrity. In LYK, cysteine, tryptophan, histidine, alanine, glutamine,
and methionine also exhibited a lower frequency (\textless{}0.02). These
amino acids exhibited a relatively lower rate of substitution,
indicating a higher degree of conservation in their respective positions
within the protein sequences.

\emph{\textbf{3.2. AI based modeling of 3D structures of LSTK and
LYK:}LSDV} has several variants that have been sequenced by independent
efforts \textsuperscript{64-66}. Efforts are ongoing to identify the
complete genome sequence of all variants of
monkeypox\textsuperscript{67} although whole genome sequencing of hMPXV1
has been completed \textsuperscript{68}. Bioinformatics analysis of
these sequences have identified putative protein kinase sequences in
these genomes \textsuperscript{7,69}. We hypothesized that if we could
predict their structures, we could repurpose FDA approved kinase
inhibitors on the potential kinase sequences to combat \emph{LSDV} and
monkeypox. Since, there are no experimentally derived structures of the
\emph{LSDV}kinases in the protein data bank, few were challenged to
employ the 2 most recent AI based protein prediction algorithms.AF2 and
ESMfold2 employ AI algorithms based on template-free
modelling\textsuperscript{20}. LSTK and LYK whose structures were
predicted, were also compared with two similar proteins:
\emph{Monkeypox} MSTK and human MLKL homologs of the \emph{LSDV} kinases
(\textbf{Figure 3A-D} ). For each of these four proteins, the AF2
algorithm generated top-ranked models. Both kinases are monomeric and
have only one domain (protein kinase domain). In order for the
predictions to be practically valuable, it is necessary to have a
confidence measure that is well-calibrated and resolved at the sequence
level. The AF2 model based predicted local distance difference test
(pLDDT) that ranges from 0 to 100, where higher scores indicate greater
confidence in the predicted structure\textsuperscript{20,70} enabled us
to confidently compare and screen the predicted structures. Ramachandran
plots were used to validate predicted structures.

In the current study, all the top-ranked protein models had a pLDDT
score more than 85, indicating that the predicted structures are
reliable. Various metrics were utilized to assess the quality of the
structure, among which pLDDT stands out as a better index of quality of
predicted protein structure in approximation to its native
structure\textsuperscript{33}. The pLDDT scores of the LSTK and LYK
structures demonstrated commendable confidence values (\textbf{Figure
3A} ), indicating a noteworthy level of accuracy in the prediction of
the protein's main chain. The structure details were compared and
analysed to gather information about number of \selectlanguage{greek}α-\selectlanguage{english}helices and \selectlanguage{greek}β-\selectlanguage{english}sheets,
while, the LYK showed 14 helices and 8 sheets in the foreseen structure.
To further validate the generated protein structure quality, the SAVES
(v 6.0) server (https://saves.mbi.ucla.edu/) was utilized
\textsuperscript{71}. Ramachandran plot indicated a significant
proportion of residues falling in right quadrants (\textbf{Figure 3B} ).
The low percentage of residues in the generously allowed regions is
indicative of good structural quality, although their presence suggests
the existence of structural irregularities or flexibility in some areas
of the protein (\textbf{Figure 3B} ). In the LSTK structure, only 0.5\%
(2 out of 447 residues) were found in the disallowed region. In
contrast, no residues were located in the disallowed region in the LYK
structure, which is another positive indicator of good protein structure
quality. According to the ERRAT server \textsuperscript{72}, the overall
quality factor was determined to be 90.3 for LSTK and 89.7 for LYK. AF2
also utilizes a pairwise error prediction technique to generate
pTM/predicted template modeling scores, wherein the contribution of each
residue is weighted based on the probability of its experimental
resolution. The assessment results revealed that the pTM scores of the
predicted LSTK and LYK structures were 0.85 and 0.88, respectively,
indicating a remarkable level of accuracy in protein structure
prediction.

In addition to the use of the AF2 method \textbf{(Figure 3A)} , the
ESMFold (Evolutionary Scale Modeling) deep learning-based method was
also employed for the prediction of protein
structures\textsuperscript{21} (\textbf{Figure 3C} ). The findings
suggested that the predicted structures displayed high accuracy. The
results of both AF2 and ESMFold methods demonstrated significant
predictive ability for the structures of LSTK and LYK proteins, with
several mathematical and statistical validations. The combination of
these multiple metrics provides compelling evidence to support the
notion that the predicted LSTK and LYK models possesses a high level of
quality, thereby making it suitable for further studies \textbf{(Figure
3C)} . Ramachandran validation confirmed the accuracy
model\textbf{(Figure 3D)} .

3.3. Prediction of the 3D structures of MSTK and hMLKL: The results of
the sequence homology analysis indicated that the\emph{LSDV} kinases,
LSTK and LYK, exhibit significant similarity with\emph{Monkeypox}
Ser/Thr kinase and human MLKL proteins, respectively. AF2 and ESMFold
were utilized to predict the structures of these two proteins,
facilitating their subsequent comparative analysis\textbf{(Figure
3A-3D)} . The reasons to compare the \emph{LSDV} kinases with their
homologs were because; i) Comparing structures may give us a chance to
understand their functions, ii) designing inhibitors can come in handy
to target the kinases (Monkeypox for instance) for other similar pox
outbreaks if required. iii) knowing what MLKL binds to and regulates in
its signaling may give us an approximate idea of LYK mechanism as well
when the virus infects the host. The aforementioned validation measures
and matrices were computed for the predicted structures, including pLDDT
scores for both AF2 and ESMFold models. Additionally, both AF2 and
ESMFold predicted models for MSTK displayed similar matrices as their
homologs.

Overall AF2 and ESMFold produced high-quality 3D structures for all four
proteins, with relatively minor variations in their predictions that can
be attributed to differences in their respective algorithmic
architectures for protein structure prediction. Since AF2 demonstrated
greater accuracy by the measure of the index of the computed matrices
for both, LSTK and LYK, we have chosen AF2-predicted models for further
analyses \textsuperscript{31}.

3.4. Structural comparison (LSTK vs MSTK and MLKL vs LYK): We compared
structural similarities for LSTK/LYK with their nearest homologues, by
superimposition of the AF2 predicted LSTK model with MSTK and LYK with
hMLKL proteins and observed significant structural similarities and
differences \textbf{(Figure 4).} The LSTK super imposed with MSTK
exhibited a moderate structural similarity, whereas, the LYK displayed
major structural difference with predicted and PDB based structure of
hMLKL (\textbf{Table 1} ). Though the LSTK and MSTK (\textbf{Figure 4A}
); LYK and hMLKL have good sequence homology in sequences
(\textbf{Figure 4B} ), their structural alignments (RMSD scores) reveal
structural differences (\textbf{Table 2} ). RMSD can reveal structural
deviations even if two structures are almost identical with slight
variations like minor differences in a flexible terminus or a loop
\textsuperscript{31}. MSTK and LSTK presented similar structural
features but for the flexible loop (\textbf{Figure 4A} ). The primary
structural difference between LSTK and MSTK is the presence of an
additional \selectlanguage{greek}α-\selectlanguage{english}helix in LSTK, resulting in a total of 19 helices in LSTK
compared to 18 in MSTK and the structure of LYK differs from that of
hMLKL in having one additional helix and sheet. We also compared hMLKL
that has been predicted by AI and also the MLKL whose structure has been
solved by x-ray crystallography and deposited in PDB for a gain of
confidence on our superposition studies (\textbf{Figure 4C} ). These
findings may point to the functional similarities as well as diversity
on offer due to enzymatic flexibility, adaptability as an aspect of
specificity found in closely related proteins.

3.5. Virtual screening / Drug Repurposing: Protein kinases in general
have immense potential as anti-viral drug
targets\textsuperscript{73,74}. LSTK and LYK were subjected to virtual
screening against a total of 88 FDA approved human kinase inhibitors.
With the structural prediction followed by active site prediction we
subjected LSTK (\textbf{Figure 5A-5B} ) and LYK (\textbf{Figure 6A-6B}
), to virtual screening. We also examined the druggability index
(D-score) calculated by Sitemap tool \textsuperscript{40} of both LSTK
and LYK, and the scores (1.032 and 1.087) point out to their capacity to
bind small molecules. We have used molecular dynamics to measure the
likelihood of inhibitors to bind to the active sites of the two kinases.
Majority of the inhibitors exhibited comparable binding affinities
towards LSTK and LYK. Erdafitinib exhibited promising binding affinity
with a docking score of -8.39 Kcal/mol followed by Lapatinib (-7.66
Kcal/mol) and Baricitinib (-7.52 Kcal/mol) towards LSTK (\textbf{Figure
5C-E} ). The three ligands Erdafitinib, Lapatinib and Baricitinib
(growth factor receptor antagonists) interacted with LSTK via three
(Lys112, Ala259 and Asp346), three (Lys112, Gly99 and Asn315) and two
(Gln80 and Ala259) hydrogen bonds, respectively. LSTK-inhibitor
complexes demonstrated the presence of hydrophobic interactions from
neighbouring residues contributing to the stability of the complexes.
All 88 kinase drugs were found to interact with the active site of LSTK
at varying affinities, and the top 15 inhibitors showed binding
affinities/ docking scores, ranging -8.39 to -6.65 Kcal/mol
(\textbf{Figure 5F-5G} )

The molecular docking studies of LYK also showed promising candidates;
with top three molecules Pazopanib, Ponatinib (tyrosine kinase
inhibitors) and selumetinib (MEK1/2 inhibitor) exhibited promising
binding with LYK (docking scores of -9.24, -7.01 and -6.73 Kcal/mol)
(\textbf{Figure 6C-6E} ). Hydrogen bonds are involved in the
stabilization of interaction between the top three inhibitors with LYK.
While, Pazopanib forms four hydrogen bonds (Asn29, Pro30, and Tyr104)
that facilitate its interaction. Selumetinib, interacts with LYK through
three hydrogen bonds (Asn18, Lys90, and Ile103) and Ponatinib forms a
single hydrogen bond (Asn29) with LYK. Hydrophobic interactions have
also played the significant role in the stabilization of LYK-ligand
complexes. Similar to LSTK, the active site of LYK was found to interact
with varying affinities to all 88 kinase compounds, and the top 15
inhibitors exhibited the binding scores ranging -9.24 to -5.11 Kcal/mol
(\textbf{Figure 6F-6G} ). No prior computational studies have been
conducted on \emph{LSDV} /LYK kinases. These studies can be used to test
the activity and inhibition of these kinases \emph{in vitro} .

\emph{3. 6. MD simulations and Binding energy:} To assess the dynamic
stability of the protein-ligand complexes, MD simulations were conducted
on the LSTK and LYK complexes with the hit compounds. Protein bound to
ATP-Mg2+ and the protein- inhibitor complexes were used virtual
screening. The best inhibitor from the binding energy calculation was
subjected to bind to the ATP-binding site to proceed with molecular
dynamics simulations. An all-atom 10ns MD simulation was conducted on
the ATP-Mg\textsuperscript{2+}-LSTK (\textbf{Figure 7A} ),
Lapatinib-LSTK (\textbf{Figure 7B} ), ATP-Mg\textsuperscript{2+}-LYK
(\textbf{Figure 7E} ) and Pazopanib-LYK (\textbf{Figure 7F} ),
complexes. Lapatinib, showed increased binding energy two times to that
of the ATP-protein complex showing that it can displace ATP as a
competitive inhibitor. LYK binding to pazopanib also displaced ATP from
the protein complex. The amino acids Asp314, Asp346, and Ser96 located
in the ligand binding site of LSTK interact with both ATP and lapatinib
(\textbf{Table 3} ). All three amino acids form interactions with the
phosphate groups of the ATP molecule (\textbf{Figure 7A} ).
Specifically, Ser96 and Asp346 form hydrogen bonds with the oxygen atoms
of the beta phosphate, while Asp314 interacts with the oxygens on the
phosphate groups of the ATP using a salt-bridge mode of interaction. The
inhibitor lapatinib interacts with the same three amino acids in the
same regions as ATP, forming hydrogen bonds and utilizing the key
ATP-binding sites (\textbf{Figure 7B)} . This suggests a possible
competitive inhibition mechanism for lapatinib (\textbf{Figure 7C} ).
The GROMACS calculation confirms this observation, as the binding energy
value of the lapatinib-LSTK (-810.956) complex is much higher than that
of the ATP-LSTK pair (-366.012) (\textbf{Table 4} ). All the calculated
binding energies are a combination of Lennard Jones and Coulombic
interaction energy functions that describe the effect of polar and
non-polar interactions between the binding site amino acids and the
ligand. For the LYK, both the pazopanib and ATP share some, but not all,
common residues. Specifically, the ATP molecule interacts with Asn29,
Asn62, Arg94, Tyr104, and Lys102 via salt-bridge interactions
(\textbf{Figure 7E} ). The inhibitor pazopanib forms hydrogen bonds with
Asn29, Asn62, and Tyr27, out of which Asn29 and Asn62 also interact with
ATP (\textbf{Figure 7F} ). Both ligands bind to different tyrosine
residues in the same binding pocket (\textbf{Figure 7G} ). The
calculated binding energy values suggest that pazopanib (-473.983
KJ/mol) exhibits greater stability upon binding to the kinase pocket in
comparison to its ATP (-434.901 KJ/mol) counterpart (\textbf{Table 2} ).
This observation allows for the possibility of another instance of
competitive inhibition.

\emph{Competitive binding of the Inhibitor and protein complexes:} To
evaluate the stability of the inhibitors bound to LSTK and LYK, the
inhibitors, lapatinib-LSTK (2.685\selectlanguage{ngerman}±0.36 Å) and pazopanib-LYK (2.078±0.37
Å), both exhibited low RMSD values (\textbf{Figure 7D \& 7H} )
indicating their stability. Notably, the pazopanib bound to LYK showed a
lower RMSD than the native ATP-Mg2+ ligand. In contrast, the lapatinib
bound to LSTK showed a slightly higher RMSD than ATP-Mg2+ (ATP-LSTK =
2.030 ± 0.47 Å). A comparison of the binding energies of the complexes
revealed that the pazopanib-protein complex (E = -473.983±19.09 KJ/mol)
was more stable than the ATP-protein complex (E = -434.901±14.56 KJ/mol)
in the case of LYK. Lapatinib-protein complex (E =-810.956±12.20 KJ/mol)
was highly stable relative to the ATP-protein complex (E =
-366.012±14.08 KJ/mol) in the case of LSTK (\textbf{Table 4} ). Notably,
the magnitude difference in the binding free energy values between the
ATP and lapatinib in the case of LSTK was substantial. These
observations clearly demonstrate that the screened inhibitors more
effectively bind to the protein active site than the conventional
ATP-Mg\textsuperscript{2+} ligand molecule. Further, the findings of the
present investigation deduced that these hits (lapatinib and pazopanib)
hold the promising prospect to be a candidate lead compounds
against\emph{LSDV} kinases in Lumpy skin disease and potentially
monkeypox as well.

3.7. Principal component analysis: The structural
features/physiochemical properties of small molecules are the key
molecular descriptors that determine the molecule's pharmacokinetic and
pharmacodynamic behaviour \textsuperscript{75} and are critical to drug
design, including solubility, stability, and bioavailability, which are
important considerations in the early steps of drug
design\textsuperscript{76}. These molecular representations are used to
assess the structural diversity/similarity of small
molecules\textsuperscript{77}. By computing the descriptors, we assessed
the distribution of FDA-kinase inhibitors in the chemical space of
FDA-Cattle drugs as well as human and cattle anti-viral drugs. We
performed a comprehensive analysis of molecular descriptors of FDA
approved drug molecules (Kinase inhibitors, cattle drugs, human
antiviral and cattle antiviral) followed by their clustering into
different groups to identify similar molecules sharing physicochemical
descriptor space with other drugs. We performed PCA analysis and
compared structural similarity/diversity on the set of 16
physicochemical descriptors (\textbf{Supplementary Table S1} ) for a
total of 492 drugs including kinase inhibitors (88), cattle drugs (319),
human antiviral (74) and cattle antiviral (11). We computed a total of
ten principal components (PCs) and observed that the first three PCs
account for a substantial portion of the total variance in the data.
Hence, the first three PCs were utilized to generate PC1 vs PC2 vs PC3
plots and to compare the molecular representation between various drugs
based on their properties (\textbf{Figure 8} ). The three principal
components account for 90.983\% of the variance in the complete dataset
(PC1-57.83\%, PC2-23.75\% and PC3-9.39\% respectively) (\textbf{Figure
8A-8C} ). In the distinct chemical spaces occupied by the kinase
inhibitors, the analyzed data showed considerable overlap between Kinase
inhibitors with both cattle FDA approved and cattle antiviral drugs
(\textbf{Figure 8D} ) implying that they all possess similar drug like
properties. Based on the virtual screening results the top three kinase
inhibitors for both LSTK (Erdafitinib, Lapatinib and Baricitinib) and
LYK (Pazopanib, Selumetinib and Ponatinib) were compared with cattle
antiviral drugs. The six kinase inhibitors were found to occupy a common
chemical space with cattle antivirals. From the results we have
identified the top three cattle antiviral drugs such as ganciclovir,
acyclovir and vidarabine, that showed overlap with kinase inhibitors
(\textbf{Figure 8E} ). The spatial molecular property distributions of
kinase inhibitors are comparable to those of FDA-approved cattle drugs
and cattle antiviral drugs. We surmise that the newer LSTK, LYK binding
inhibitors do fall in the chemical space intended for anti-virals used
to treat cattle and they can be further tested to control \emph{LSDV}and
Monkeypox infections.

Acknowledgements:

The authors would like to acknowledge CSIR-IICT and AIG research
foundations for research facilities. RA and PCY are thankful for the
research grant support from ~SERB ~(EMR/2016/005994/HS). RA acknowledges
the research start up grant funding from DIICT, CSIR-IICT.

References:

1. Sprygin A, Pestova Y, Wallace DB, Tuppurainen E, Kononov AV.
Transmission of lumpy skin disease virus: A short review. \emph{Virus
Res.} 2019;269:197637.

2. Tuppurainen ES, Pearson CR, Bachanek-Bankowska K, et al.
Characterization of sheep pox virus vaccine for cattle against lumpy
skin disease virus. \emph{Antiviral Res.} 2014;109:1-6.

3. McFadden G. Poxvirus tropism. \emph{Nat Rev
Microbiol.}2005;3(3):201-213.

4. Babiuk S, Bowden TR, Boyle DB, Wallace DB, Kitching RP.
Capripoxviruses: an emerging worldwide threat to sheep, goats and
cattle. \emph{Transbound Emerg Dis.} 2008;55(7):263-272.

5. Molla W, de Jong MCM, Gari G, Frankena K. Economic impact of lumpy
skin disease and cost effectiveness of vaccination for the control of
outbreaks in Ethiopia. \emph{Prev Vet Med.} 2017;147:100-107.

6. Chen X, Anstey AV, Bugert JJ. Molluscum contagiosum virus
infection.\emph{Lancet Infect Dis.} 2013;13(10):877-888.

7. Tulman ER, Afonso CL, Lu Z, Zsak L, Kutish GF, Rock DL. Genome of
lumpy skin disease virus. \emph{J Virol.} 2001;75(15):7122-7130.

8. Senkevich TG, Yutin N, Wolf YI, Koonin EV, Moss B. Ancient Gene
Capture and Recent Gene Loss Shape the Evolution of Orthopoxvirus-Host
Interaction Genes. \emph{mBio.} 2021;12(4):e0149521.

9. Kawaguchi Y, Matsumura T, Roizman B, Hirai K. Cellular elongation
factor 1delta is modified in cells infected with representative alpha-,
beta-, or gammaherpesviruses. \emph{J Virol.} 1999;73(5):4456-4460.

10. Jacob T, Van den Broeke C, Favoreel HW. Viral serine/threonine
protein kinases. \emph{J Virol.} 2011;85(3):1158-1173.

11. Patschinsky T, Sefton BM. Evidence that there exist four classes of
RNA tumor viruses which encode proteins with associated tyrosine protein
kinase activities. \emph{J Virol.} 1981;39(1):104-114.

12. Kawaguchi Y, Kato K. Protein kinases conserved in herpesviruses
potentially share a function mimicking the cellular protein kinase
cdc2.\emph{Rev Med Virol.} 2003;13(5):331-340.

13. Lin S, Broyles SS. Vaccinia protein kinase 2: a second essential
serine/threonine protein kinase encoded by vaccinia virus. \emph{Proc
Natl Acad Sci U S A.} 1994;91(16):7653-7657.

14. Yang Q, Xia D, Syed AAS, Wang Z, Shi Y. Highly accurate protein
structure prediction and drug screen of monkeypox virus proteome.\emph{J
Infect.} 2023;86(1):66-117.

15. Abduljalil JM, Elfiky AA. Repurposing antiviral drugs against the
human monkeypox virus DNA-dependent RNA polymerase; in silico
perspective. \emph{J Infect.} 2022;85(6):702-769.

16. Sahu A, Gaur M, Mahanandia NC, Subudhi E, Swain RP, Subudhi BB.
Identification of core therapeutic targets for Monkeypox virus and
repurposing potential of drugs against them: An in silico
approach.\emph{Comput Biol Med.} 2023;161:106971.

17. Durairaj J, de Ridder D, van Dijk ADJ. Beyond sequence:
Structure-based machine learning. \emph{Comput Struct Biotechnol
J.}2023;21:630-643.

18. Senior AW, Evans R, Jumper J, et al. Improved protein structure
prediction using potentials from deep learning.
\emph{Nature.}2020;577(7792):706-710.

19. Kuhlman B, Bradley P. Advances in protein structure prediction and
design. \emph{Nat Rev Mol Cell Biol.} 2019;20(11):681-697.

20. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein
structure prediction with AlphaFold.
\emph{Nature.}2021;596(7873):583-589.

21. Lin Z, Akin H, Rao R, et al. Evolutionary-scale prediction of
atomic-level protein structure with a language model.
\emph{Science.}2023;379(6637):1123-1130.

22. Cohen P. Protein kinases--the major drug targets of the twenty-first
century? \emph{Nat Rev Drug Discov.} 2002;1(4):309-315.

23. Letunic I, Doerks T, Bork P. SMART 7: recent updates to the protein
domain annotation resource. \emph{Nucleic Acids Res.} 2012;40(Database
issue):D302-305.

24. Geer LY, Domrachev M, Lipman DJ, Bryant SH. CDART: protein homology
by domain architecture. \emph{Genome Res.} 2002;12(10):1619-1623.

25. Marchler-Bauer A, Anderson JB, DeWeese-Scott C, et al. CDD: a
curated Entrez database of conserved domain alignments. \emph{Nucleic
Acids Res.} 2003;31(1):383-387.

26. Sonnhammer EL, Eddy SR, Durbin R. Pfam: a comprehensive database of
protein domain families based on seed alignments.
\emph{Proteins.}1997;28(3):405-420.

27. Miyake K, Yasujima T, Takahashi S, Yamashiro T, Yuasa H.
Identification of the amino acid residues involved in the
species-dependent differences in the pyridoxine transport function of
SLC19A3. \emph{J Biol Chem.} 2022;298(8):102161.

28. Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and
effective stochastic algorithm for estimating maximum-likelihood
phylogenies. \emph{Mol Biol Evol.} 2015;32(1):268-274.

29. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS.
ModelFinder: fast model selection for accurate phylogenetic
estimates.\emph{Nat Methods.} 2017;14(6):587-589.

30. Ronquist F, Teslenko M, van der Mark P, et al. MrBayes 3.2:
efficient Bayesian phylogenetic inference and model choice across a
large model space. \emph{Syst Biol.} 2012;61(3):539-542.

31. Callaway E. AlphaFold's new rival? Meta AI predicts shape of 600
million proteins. \emph{Nature.} 2022;611(7935):211-212.

32. Pettersen EF, Goddard TD, Huang CC, et al. UCSF ChimeraX: Structure
visualization for researchers, educators, and developers. \emph{Protein
Sci.} 2021;30(1):70-82.

33. Zhang Y, Skolnick J. Scoring function for automated assessment of
protein structure template quality. \emph{Proteins.} 2004;57(4):702-710.

34. Tunyasuvunakool K, Adler J, Wu Z, et al. Highly accurate protein
structure prediction for the human proteome.
\emph{Nature.}2021;596(7873):590-596.

35. Wishart DS, Knox C, Guo AC, et al. DrugBank: a comprehensive
resource for in silico drug discovery and exploration. \emph{Nucleic
Acids Res.} 2006;34(Database issue):D668-672.

36. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison
GR. Open Babel: An open chemical toolbox. \emph{J Cheminform.}2011;3:33.

37. Chen IJ, Foloppe N. Drug-like bioactive structures and
conformational coverage with the LigPrep/ConfGen suite: comparison to
programs MOE and catalyst. \emph{J Chem Inf Model.} 2010;50(5):822-839.

38. Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya
M. Epik: a software program for pK( a ) prediction and protonation state
generation for drug-like molecules. \emph{J Comput Aided Mol
Des.}2007;21(12):681-691.

39. Greenwood JR, Calkins D, Sullivan AP, Shelley JC. Towards the
comprehensive, rapid, and accurate prediction of the favorable
tautomeric states of drug-like molecules in aqueous solution. \emph{J
Comput Aided Mol Des.} 2010;24(6-7):591-604.

40. Halgren TA. Identifying and characterizing binding sites and
assessing druggability. \emph{J Chem Inf Model.} 2009;49(2):377-389.

41. Chen W, Cui D, Jerome SV, et al. Enhancing Hit Discovery in Virtual
Screening through Absolute Protein-Ligand Binding Free-Energy
Calculations. \emph{J Chem Inf Model.} 2023;63(10):3171-3185.

42. Seeliger D, de Groot BL. Ligand docking and binding site analysis
with PyMOL and Autodock/Vina. \emph{J Comput Aided Mol
Des.}2010;24(5):417-422.

43. Kutzner C, Kniep C, Cherian A, et al. GROMACS in the Cloud: A Global
Supercomputer to Speed Up Alchemical Drug Design. \emph{J Chem Inf
Model.} 2022;62(7):1691-1711.

44. Musiani F, Rossetti G, Capece L, et al. Molecular dynamics
simulations identify time scale of conformational changes responsible
for conformational selection in molecular recognition of HIV-1
transactivation responsive RNA. \emph{J Am Chem
Soc.}2014;136(44):15631-15637.

45. Zoete V, Cuendet MA, Grosdidier A, Michielin O. SwissParam: a fast
force field generation tool for small organic molecules. \emph{J Comput
Chem.} 2011;32(11):2359-2368.

46. Schlegel HB. Ab Initio Direct Dynamics. \emph{Acc Chem
Res.}2021;54(20):3749-3759.

47. Trnka D, Engelke AD, Gellert M, et al. Molecular basis for the
distinct functions of redox-active and FeS-transfering
glutaredoxins.\emph{Nat Commun.} 2020;11(1):3445.

48. Salomon-Ferrer R, Gotz AW, Poole D, Le Grand S, Walker RC. Routine
Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2.
Explicit Solvent Particle Mesh Ewald. \emph{J Chem Theory
Comput.}2013;9(9):3878-3888.

49. Margraf JT, Hennemann M, Clark T. EMPIRE: a highly parallel
semiempirical molecular orbital program: 3: Born-Oppenheimer molecular
dynamics. \emph{J Mol Model.} 2020;26(3):43.

50. Martonak R, Laio A, Parrinello M. Predicting crystal structures: the
Parrinello-Rahman method revisited. \emph{Phys Rev
Lett.}2003;90(7):075503.

51. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to
evaluate pharmacokinetics, drug-likeness and medicinal chemistry
friendliness of small molecules. \emph{Sci Rep.} 2017;7:42717.

52. Kutchukian PS, Dropinski JF, Dykstra KD, et al. Chemistry informer
libraries: a chemoinformatics enabled approach to evaluate and advance
synthetic methods. \emph{Chem Sci.} 2016;7(4):2604-2613.

53. Leader DP. Viral protein kinases and protein
phosphatases.\emph{Pharmacol Ther.} 1993;59(3):343-389.

54. Kanev GK, de Graaf C, de Esch IJP, et al. The Landscape of Atypical
and Eukaryotic Protein Kinases. \emph{Trends Pharmacol
Sci.}2019;40(11):818-832.

55. Xie T, Peng W, Yan C, Wu J, Gong X, Shi Y. Structural insights into
RIP3-mediated necroptotic signaling. \emph{Cell Rep.} 2013;5(1):70-78.

56. Tyagi N, Anamika K, Srinivasan N. A framework for classification of
prokaryotic protein kinases. \emph{PLoS One.} 2010;5(5):e10608.

57. Hunter S, Jones P, Mitchell A, et al. InterPro in 2011: new
developments in the family and domain prediction database. \emph{Nucleic
Acids Res.} 2012;40(Database issue):D306-312.

58. Obenauer JC, Cantley LC, Yaffe MB. Scansite 2.0: Proteome-wide
prediction of cell signaling interactions using short sequence
motifs.\emph{Nucleic Acids Res.} 2003;31(13):3635-3641.

59. Mistry J, Bateman A, Finn RD. Predicting active site residue
annotations in the Pfam database. \emph{BMC Bioinformatics.} 2007;8:298.

60. Zhang C, Freddolino PL, Zhang Y. COFACTOR: improved protein function
prediction by combining structure, sequence and protein-protein
interaction information. \emph{Nucleic Acids Res.}2017;45(W1):W291-W299.

61. Hu J, Li Y, Zhang Y, Yu DJ. ATPbind: Accurate Protein-ATP Binding
Site Prediction by Combining Sequence-Profiling and Structure-Based
Comparisons. \emph{J Chem Inf Model.} 2018;58(2):501-510.

62. Brooks DJ, Fresco JR, Lesk AM, Singh M. Evolution of amino acid
frequencies in proteins over deep time: inferred order of introduction
of amino acids into the genetic code. \emph{Mol Biol
Evol.}2002;19(10):1645-1655.

63. Chi PB, Kim D, Lai JK, et al. A new parameter-rich structure-aware
mechanistic model for amino acid substitution during
evolution.\emph{Proteins.} 2018;86(2):218-228.

64. Agianniotaki EI, Mathijs E, Vandenbussche F, et al. Complete Genome
Sequence of the Lumpy Skin Disease Virus Isolated from the First
Reported Case in Greece in 2015. \emph{Genome Announc.} 2017;5(29).

65. Mathijs E, Vandenbussche F, Haegeman A, et al. Complete Genome
Sequences of the Neethling-Like Lumpy Skin Disease Virus Strains
Obtained Directly from Three Commercial Live Attenuated
Vaccines.\emph{Genome Announc.} 2016;4(6).

66. Sprygin A, Babin Y, Pestova Y, Kononova S, Byadovskaya O, Kononov A.
Complete Genome Sequence of the Lumpy Skin Disease Virus Recovered from
the First Outbreak in the Northern Caucasus Region of Russia in
2015.\emph{Microbiol Resour Announc.} 2019;8(8).

67. Chen NFG, Chaguza C, Gagne L, et al. Development of an
amplicon-based sequencing approach in response to the global emergence
of human monkeypox virus. \emph{medRxiv.} 2023.

68. Forni D, Moltrasio C, Sironi M, et al. Whole-genome sequencing of
hMPXV1 in five Italian cases confirms the occurrence of the predominant
epidemic lineage. \emph{J Med Virol.} 2023;95(2):e28493.

69. Shchelkunov SN, Totmenin AV, Safronov PF, et al. Analysis of the
monkeypox virus genome. \emph{Virology.} 2002;297(2):172-194.

70. Mariani V, Biasini M, Barbato A, Schwede T. lDDT: a local
superposition-free score for comparing protein structures and models
using distance difference tests.
\emph{Bioinformatics.}2013;29(21):2722-2728.

71. Abdelkader A, Elzemrany AA, El-Nadi M, et al. In-Silico targeting of
SARS-CoV-2 NSP6 for drug and natural products
repurposing.\emph{Virology.} 2022;573:96-110.

72. Colovos C, Yeates TO. Verification of protein structures: patterns
of nonbonded atomic interactions. \emph{Protein
Sci.}1993;2(9):1511-1519.

73. Pillaiyar T, Laufer S. Kinases as Potential Therapeutic Targets for
Anti-coronaviral Therapy. \emph{J Med Chem.} 2022;65(2):955-982.

74. Garcia-Carceles J, Caballero E, Gil C, Martinez A. Kinase Inhibitors
as Underexplored Antiviral Agents. \emph{J Med Chem.}2022;65(2):935-954.

75. Nakamura T, Sakaue S, Fujii K, Harabuchi Y, Maeda S, Iwata S.
Selecting molecules with diverse structures and properties by maximizing
submodular functions of descriptors learned with graph neural
networks.\emph{Sci Rep.} 2022;12(1):1124.

76. Saldivar-Gonzalez FI, Medina-Franco JL. Approaches for enhancing the
analysis of chemical space for drug discovery. \emph{Expert Opin Drug
Discov.} 2022;17(7):789-798.

77. Clemons PA, Wilson JA, Dancik V, et al. Quantifying structure and
performance diversity for sets of small molecules comprising
small-molecule screening collections. \emph{Proc Natl Acad Sci U S
A.}2011;108(17):6817-6822.

\emph{}

\emph{Figure legends}

\textbf{Figure 1:} Multiple sequence alignment (MSA) of serine/threonine
protein kinase (LSTK) from~(AI, AII) \emph{Poxviridae} . Sequences have
been divided into two groups for the ease of identification of motifs.
Sequence logos displayed on top of aligned sequences. Evolutionary
conservation of amino acid residues was highlighted for all the aligned
blocks of the MSA. The number of \selectlanguage{greek}α-\selectlanguage{english}helices (blue) and \selectlanguage{greek}β-\selectlanguage{english}strands (yellow)
were displayed and highlighted with different colors in the sequences.
Short Linear Motifs (SLiMs) that are predominant and conserved in both
\selectlanguage{greek}α-\selectlanguage{english}helices and \selectlanguage{greek}β-\selectlanguage{english}strands of the (BI) \emph{LSDV} and
(BII)\emph{Monkeypox.} (C) Phylogram showing LSTK homologs
in\emph{Poxviridae} inferred by IQ-TREE. (D) Amino acid substitution
frequency of LSTK across \emph{Poxviridae.}

\textbf{Figure 2:} Multiple sequence alignment (MSA) of tyrosine kinase
(LYK)~from~(A1, AII) \emph{Poxviridae} . Sequences have been divided
into two groups for the ease of identification of motifs. Sequence logos
displayed on top of aligned sequences. The sequences of (BI)
\emph{LSDV}and (BII) \emph{Fowlpox} were visually represented, with
\selectlanguage{greek}α-\selectlanguage{english}helices highlighted in blue and \selectlanguage{greek}β-\selectlanguage{english}strands in yellow. (C) The phylogram
of LYK in\emph{LSDV} and other closely related \emph{Poxviridae} was
constructed using IQ-TREE. Different colors were used to represent each
genus within the \emph{Poxviridae} family. (D) The frequency of amino
acid substitutions in the LYK protein sequences among the diverse
members of the \emph{Poxviridae} family.

\textbf{Figure 3:} AI based prediction and assessment of protein
structures using two different learning models. (A) AlphaFold2 predicted
structures for LSTK, LYK, MSTK and hMLKL. (B) Ramachandran plots for the
predicted LSTK, LYK, MSTK and hMLKL structures. (C) ESMFold predicted
structures for LSTK, LYK, MSTK and hMLKL. (D) Ramachandran plots for the
ESMFold predicted LSTK, LYK, MSTK and hMLKL structures. The model
confidence for each protein was assessed with pLDDT and pTM scores,
which are represented below respective structures. The blue color region
in each protein structure indicates the high model confidence.

\emph{Figure 4}

Comparative structural analysis of LSTK and LYK with other related
serine/threonine kinases. (A) Superimposition of predicted protein
structures of \emph{Monkeypox} serine/threonine kinase (MSTK) with LSTK.
(B) Superimposition of predicted LYK structure with predicted hMLKL. (C)
Superimposition of predicted LYK structure with predicted hMLKL along
with PDB derived hMLKL structure.

\emph{Figure 5}

Virtual screening of the FDA-approved kinase library targeting LSTK. (A)
The predicted LSTK structure (shown as a line representation) along with
the depiction of its active site. (B) Surface representation of the
predicted binding site of LSTK. (C,D,E) Surface and 2D representations
of the ligand-protein complexes with the top three inhibitors. (F) A
common binding site shared by all the kinase inhibitors. (G) Docking
scores of the top fifteen inhibitors towards LSTK.

\emph{Figure 6}

In-silico screening of the FDA-approved kinase inhibitors against LYK.
(A) The predicted structure of LYK, represented as a line model, along
with its active site. ((B) The binding site of LYK was predicted using
sitemap tool and visualized as a surface representation. The molecular
interactions of top three inhibitors of LYK were depicted in three
different formats: (C, D, E) Surface and 2D illustration of the
ligand-protein complexes. (F) A common binding site shared by all the
kinase inhibitors. (G) Docking scores of the top fifteen inhibitors
towards LYK.

\emph{Figure 7}

MD simulations for the LSTK and LYK proteins with ATP, Mg+2, and
inhibitor complexes. (A) The binding poses of ATP and
Mg\textsuperscript{+2} and (B) lapatinib, with LSTK protein represented
in surface conformation along with the zoomed view in a circle. (C) The
cartoon representation of LSTK binding with lapatinib, ATP and
Mg\textsuperscript{+2} in the same active site. (D) RMSD plot of the
LSTK-ATP and LSTK-Lapatinib complexes over a simulation time of 10 ns.
The binding pose conformations of (E) ATP and Mg\textsuperscript{+2},
(F) pazopanib with the LYK protein, represented in a surface
conformation and visualized with a circular zoomed view. (G) The cartoon
representation of LYK binding with pazopanib, ATP and
Mg\textsuperscript{+2} in the same binding site. (H) RMSD plot of the
LYK-ATP and LYK- pazopanib complexes over a simulation time of 10 ns.

\emph{Figure 8}

Principal component analysis (PCA) using structural and physiochemical
descriptors of the 88 human kinase FDA approved inhibitors compared with
human anti-viral (74), cattle anti-viral (11) and other general cattle
(319) drugs. A set of 16 molecular descriptors were used to generate the
PCA plots. (A) PCA plot of PC1 versus PC2; (B) PC2 vs PC3; (C) PC1 vs
PC3; (D) Three dimensional presentation of PC1 vs PC2 vs PC3. (E) The
comparison of top three inhibitors obtained from virtual screening
results of LSTK and LYK with cattle anti-viral drugs. The top three
inhibitors (FDA approved human kinase inhibitors), represented by green
square dots, were found to share common descriptors with the cattle
anti-viral drugs, indicated by red triangle dots.

\emph{Supporting Figure S1}\textbf{:}

Molecular binding of pazopanib and lapatinib against receptor tyrosine
kinases. (A) \emph{In silico} binding interactions of pazopanib with
Vascular endothelial growth factor receptor 2 (VEGFR2) in different
mammals. (B) \emph{In silico} binding interactions of lapatinib with
Erb-B2 Receptor Tyrosine Kinase 4 (ERBB4) across various mammals. The
cartoon representation of ligand-protein complexes is displayed, with
the corresponding binding free energies (\selectlanguage{greek}Δ\selectlanguage{english}G) mentioned below each
complex.

\textbf{Supporting Table S1} : The predicted molecular descriptors used
for PCA analysis in detailed.

\textbf{Supporting Table S2:} The predicted structural and
physiochemical descriptors of FDA approved kinase inhibitors, Human
anti-viral drugs, Cattle anti-viral and Veterinary drugs.

\textbf{Supporting Table S3:} The contributed percentage of each
principal component in a PCA plot represents the proportion of variance
explained by each component.

\textbf{Supplementary Methods} : Complete detailed methods for each
section have been described.

\emph{Tables}

\textbf{Table 1} : Structure prediction index:\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Protein}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Alphafold2}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Alphafold2}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Alphafold2}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Alphafold2}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{ESMFold}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{ESMFold}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{ESMFold}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{ESMFold}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Sequence Identity}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Sequence Similarity}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{α- Helix}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{β sheets}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{PLDDT}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{PTM}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{α-Helices}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\selectlanguage{greek}\textbf{β sheets}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{PLDDT}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{PTM}\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
\textbf{Perplexity}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
LSTK\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
73 \%\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
85\%\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
88.8\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.857\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
63.475\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.67\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2.705\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
MSTK\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
73 \%\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
85\%\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
88.4\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.863\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
66.981\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.737\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2.728\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
LYK\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
30\%\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
55\%\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
86.9\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.881\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
85.311\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
0.89\strut
\end{minipage} & \begin{minipage}[t]{0.06\columnwidth}\raggedright\strut
2.613\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
hMLKL\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
30\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
55\%\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
86.1\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.859\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
83.281\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.876\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.633\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} : Sequence structure alignment index:\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Superimposed proteins & \textbf{RMSD} & \textbf{Sequence alignment
score} & \textbf{Tm score}\tabularnewline
\midrule
\endhead
MSTK + LSTK & 0.676 A? & 1874.4 & 0.5515\tabularnewline
hMLKL + LYK & 0.987 A? & 567.8 & 0.2394\tabularnewline
hMLKL(Pdb) + LYK & 0.953 A? & 545.4 & 0.1099\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} : Amino acids present in the binding pocket of LSTK and
LYK. A list of all the amino acids present in the binding pocket of
kinase domain that hold the ligand in its position. Residues in the same
binding pocket are highlighted with a common colour (For LSTK, the color
green was used to represent it, while the color red was used for the
LYK).\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\emph{Protein}\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\emph{Ligand}\strut
\end{minipage} & \begin{minipage}[b]{0.30\columnwidth}\raggedright\strut
\emph{Amino acids in the ATP binding site that show interactions}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
LSTK\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
ATP-Mg\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
I95,S96,T97,G98,Y100,G101,V103,K105,V110,K112,A259,K260,F261,S262,K265,K312,D314,N315,L317,F319,D346,D348.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Lapatinib\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Q80,F85,Y86,P87,I95,S96,T97,G98,G99,Y100,V103,K105,V110,K112,Y230,F256,P257,L258,A259,K260,F261,S262,K265,H306,D310,K312,D314,N315,L317,F319,N345,D346,F347,D348,F349.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
LYK\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
ATP-Mg\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Y27,I28,N29,R32,F61,N62,R94,S97,K102,Y104.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
pazopanib\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
E17,N18,Q20,I22,Y27,I28,N29,P30,I31,R32,F61,N62,I84,N87,I89,K90,I100,L101,K102,I103,Y104,H188.\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4} : The binding energies of each LSTK and LYK in complex
with ATP and inhibitor. The total interaction energy is a summation of
the Lennard-Jones interaction energy (LJ) and the Coulombic interaction
energy (Coul). SR stands for short-range interactions which are the key
interactions determining the local stability of the ligand in the
binding pocket.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Protein-Ligand complex & Energy (KJ/mol) & Energy
(KJ/mol)\tabularnewline
\midrule
\endhead
& Average & Err. Estimate\tabularnewline
LJ-SR-LSTK-ATP & -148.18 & 5.4\tabularnewline
Coul-SR-LSTK-ATP & -217.832 & 13\tabularnewline
Total-Energy-LSTK-ATP & -366.012 & 14.0769315\tabularnewline
LJ-SR-LSTK- lapatinib & -196.575 & 2.2\tabularnewline
Coul-SR-LSTK- lapatinib & -614.381 & 12\tabularnewline
Total-Energy-LSTK- lapatinib & -810.956 & 12.2\tabularnewline
LJ-SR-LYK-ATP & -190.515 & 4\tabularnewline
Coul-SR- LYK -ATP & -244.386 & 14\tabularnewline
Total-Energy- LYK -ATP & -434.901 & 14.5602198\tabularnewline
LJ-SR- LYK -- pazopanib & -218.201 & 1.9\tabularnewline
Coul-SR- LYK -- pazopanib & -255.782 & 19\tabularnewline
Total-Energy-LYK- pazopanib & -473.983 & 19.0947637\tabularnewline
\bottomrule
\end{longtable}



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\selectlanguage{english}
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