Abstract

Background

   Target drugs play an important role in the clinical treatment of virus
   diseases. Virus-encoded proteins are widely used as targets for target
   drugs. However, they cannot cope with the drug resistance caused by a
   mutated virus and ignore the importance of host proteins for virus
   replication. Some methods use interactions between viruses and their
   host proteins to predict potential virus–target host proteins, which
   are less susceptible to mutated viruses. However, these methods only
   consider the network topology between the virus and the host proteins,
   ignoring the influences of protein complexes. Therefore, we introduce
   protein complexes that are less susceptible to drug resistance of
   mutated viruses, which helps recognize the unknown virus–target host
   proteins and reduce the cost of disease treatment.

Results

   Since protein complexes contain virus–target host proteins, it is
   reasonable to predict virus–target human proteins from the perspective
   of the protein complexes. We propose a coverage
   clustering-core-subsidiary protein complex recognition method named
   CCA-SE that integrates the known virus–target host proteins, the human
   protein–protein interaction network, and the known human protein
   complexes. The proposed method aims to obtain the potential unknown
   virus–target human host proteins. We list part of the targets after
   proving our results effectively in enrichment experiments.

Conclusions

   Our proposed CCA-SE method consists of two parts: one is CCA, which is
   to recognize protein complexes, and the other is SE, which is to select
   seed nodes as the core of protein complexes by using seed expansion.
   The experimental results validate that CCA-SE achieves efficient
   recognition of the virus–target host proteins.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12859-022-04792-x.

   Keywords: Virus–target protein, Host protein, Protein complexes
   recognition, Protein–protein interaction network

Introduction

   The novel coronavirus (2019-nCov for short) outbroke at the end of
   2019, and has been causing varying degrees of disease symptoms in
   people of different ages, such as gastrointestinal symptoms in children
   while other ages experienced pneumonia, stroke, and blood clots
   [[39]1]. 2019-nCov belongs to the
   [MATH: <mi>β</mi> :MATH]
   coronavirus, which is a single-stranded RNA virus. In addition to the
   discovery of 2019-nCov in coronavirus, six human coronaviruses have
   been found and studied in depth. There are four types of
   [MATH: <mi>β</mi> :MATH]
   coronavirus group. In 2002, there were 8000 cases of severe acute
   respiratory syndrome coronavirus (SARS-CoV) worldwide, and the
   mortality rate was about 10%. The Middle East respiratory syndrome
   coronavirus (MERS-CoV) produced in 2012 had a higher fatality rate of
   36% [[40]2, [41]3]. The other two beta-viruses (HCov-HKU1 and
   HCov-OC43) are related to respiratory diseases, but with low incidence.
   There are two kinds of
   [MATH: <mi>α</mi> :MATH]
   coronavirus groups [[42]4], such as HCoV-229E in 2000, but the
   pathogenicity and mild symptoms. The HCov-NL63 virus, found in the
   Netherlands in 2004, had little damage to humans and can be ignored.
   2019-nCov can be transmitted by face-to-face communication or direct
   contact of faces [[43]5]. For patients infected with 2019-nCov, there
   are few drugs available to treat the virus, such as Lopinavir,
   Remdesivir, and symptom-based treatment [[44]6].

   Proteins with similar functions tend to form protein complexes and work
   together, as do virus proteins. Therefore, it is very important to
   recognize protein complexes in predicting virus–target host proteins.
   Detecting complexes from Protein–Protein Interaction (PPI) has become
   an important research field in proteomics. Many graph clustering
   algorithms use it to find community structures in networks, and
   recognize protein complexes. Nepusz et al. [[45]7] proposed ClusterONE,
   which for detecting potentially overlapping protein complexes from PPI
   data. Liu et al. developed CMC [[46]8] to discover complexes from a
   weighted PPI network. Min Wu et al. presented COACH [[47]9] which
   detected protein complexes in two stages. COACH first detects
   protein-complex cores as the “hearts” of protein complexes and then
   includes attachments into these cores to form biologically meaningful
   structures.

   According to the World Health Organization, over 17 million people die
   of infectious diseases every year. It is necessary to identify
   interactions between viruses and human proteins in virus disease
   treatment. Ho-Joon Lee [[48]10] presented an approach to predict
   virus–host PPI by multi-label machine learning classifiers of random
   forests and XGBoost using amino acid composition profiles of virus and
   human proteins, which predict that histone H2A components are targeted
   by multiple 2019-nCov proteins. Jack Lanchantin [[49]11] proposed
   DeepVHPPI, combining a self-attention-based transformer architecture
   and a transfer learning training strategy to predict interactions
   between human proteins and virus proteins that have novel sequence
   patterns, achieving great results in predicting virus–human protein
   interactions for H1N1 and Ebola. Babak Khorsand [[50]12] adopted
   ensemble learning methods to predicting PPI between human proteins and
   Alphainfluenzavirus proteins, he extracted several features from
   physicochemical properties of amino acids, combined with different
   centralities of human PPI.

   Studying PPI between the new virus and its known host proteins can
   accurately predict the virus. Fatma-Elzahraa Eid [[51]13] proposed
   DeNovo, a sequence-based negative sampling and machine learning
   framework, he learns from the PPI of different viruses and predicts a
   new virus using shared host proteins. Experiments show that this method
   can better predict the PPI of human virus infection. Dyer et al.
   [[52]14] proposed a method to predict the physical interaction between
   human proteins and HIV proteins based on a variety of features, such as
   protein domains, sequence information, and the properties of human
   proteins in human PPI networks. Zahiri predicted the HIV1–human PPI
   network [[53]15]. Ray predicted HCV–human PPI network [[54]16]. Chen
   revealed the west Nile virus–human PPI network [[55]17].

   However, most existing methods often ignore the inherent biological
   structure of the complexes, and many only consider the structure as
   dense subgraphs. Few of them consider the modularity of PPI, let alone
   search for potential virus–target proteins from protein complexes, such
   as Babak Khorsand [[56]18]. Here we adopt CCA to recognize protein
   complexes, showing the feasibility of seed selection, and then we used
   CCA-SE to predict potential virus–target human proteins. At first, a
   network representation learning technique called node2vec is utilized
   to learn a dense vector for each vertex to represent the topological
   information. Secondly, based on edge clustering coefficient (ECC) and
   degree, we introduce a new seed selection strategy, and the core
   structure of protein complexes is detected based on SE. Then according
   to network topology, we design a fitness function to identify protein
   complexes with various densities and modularity. At last, we apply
   CCA-SE to predict the 2019-nCov-target proteins, which play a
   fundamental role in detecting virus drug targets.

Result

Principle of the CCA-SE method

   We developed the CCA-SE method based on the principle of coverage
   clustering-core-subsidiary structure, which contained two parts, CCA
   was used to recognizing protein complexes, and SE was applied to select
   seed nodes. Especially, We demonstrated a new selection strategy for
   seed nodes. We clustered seed nodes to obtain the nuclear structure of
   protein complexes. Then, based on the density and modularity, we
   expanded the core structure and formed protein complexes. Next,
   integrate downloaded complexes data with our results. At last, we
   calculated the similarity between unknown human proteins and known
   virus–target proteins in the same protein complexes. We defined a
   scoring function, which was helpful in getting potential virus–target
   human proteins. Figure [57]1 shows the algorithm.

Fig. 1.

   Fig. 1
   [58]Open in a new tab

   Process of CCA-SE

Construction of virus–host PPI network

   We constructed a virus–target human PPI network based on the human
   protein interaction database HIPPIE and the human protein dataset of
   2019-nCov infection. Researchers often regard direct neighbor proteins
   as potential host proteins in some typical virus–host proteins research
   [[59]19]. Therefore, G = (V, E) represents human PPI. V includes not
   only host proteins attached by the 2019-nCov directly but also direct
   neighbors of those host proteins. E shows human PPI. After removing
   noisy data, we got 2308 human proteins. We reported details in
   “[60]Methods” section.

   Node2Vec [[61]20] not only makes similar nodes closer in the vector
   space but also retains network structure, captures the diversity
   connection between nodes. We embedded Node2Vec into the PPI network to
   extract hidden information and used a low-dimensional vector to
   represent each node.

   We extracted the hidden layer weight of PPI and demonstrated each
   protein node with a 128-dimensional vector [[62]21]. In Fig. [63]2,
   nodes included not only the proteins attached by virus but also those
   known interacted with virus–host proteins directly.

Fig. 2.

   [64]Fig. 2
   [65]Open in a new tab

   An illumination of virus–host PPI Network

Selection of seed nodes

   In a real protein network, many protein complexes are overlapped and
   share multiple nodes, so that the core nodes and the overlapped nodes
   cannot be distinguished simply by degree [[66]22]. Therefore we use two
   topological properties, ECC and nodes degree, which can evaluate the
   importance of nodes. The score of each protein is obtained by the sum
   of degree and ECC. According to an existing work [[67]9], if the score
   of a protein node is greater than the average score, we consider it as
   a seed protein that would achieve the best results. The scoring
   function for each protein u is defined in Eq. ([68]1). We proved Eq.
   ([69]1) feasibility in Table [70]1.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e
   </mi><mrow><mo stretchy="false">(</mo><mi>u</mi><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mfrac><mrow><mi>s</mi><mi>u</m
   i><mi>m</mi><mi>E</mi><mi>E</mi><mi>C</mi><mo
   stretchy="false">(</mo><mi>u</mi><mo
   stretchy="false">)</mo></mrow><mrow><mo>deg</mo><mi>r</mi><mi>e</mi><mi
   >e</mi><mo stretchy="false">(</mo><mi>u</mi><mo
   stretchy="false">)</mo></mrow></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   1

   where sum
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">EEC</mi></mrow></msub> :MATH]
   (u) indicates summation of ECC and degree(u) indicates degree of node
   u. Equation ([71]1) considers network topological structure and reduces
   the overlapped nodes that include the sum of ECC by dividing each node
   degree. Meanwhile, it avoids overlapped nodes mistaken as seed nodes
   and improves seed selection accuracy.

Table 1.

   Effects in different scores
   Score  Seed Precision Recall F-score
   0.1398 1035 0.9317    0.6399 0.7587
   0.1498 982  0.9350    0.6352 0.7565
   0.1598 939  0.9390    0.6381 0.7598
   0.1698 868  0.9414    0.6406 0.7624
   0.1798 821  0.9383    0.6334 0.7563
   0.1898 781  0.9272    0.6119 0.7373
   0.1998 726  0.9399    0.6078 0.7382
   [72]Open in a new tab

   We formed Table [73]1 based on our results. “Score” means the standard
   score when we choose seed nodes. “Seed” means the number of seeds. We
   use 0.1698 as the seed selection standard also is the average score. As
   we can see from Table [74]1, whether the score is higher than average
   or lower, their precision, recall, and F-score are not good as the
   average score’s effect.

   We can use Gene Ontology (GO) database to predict and analyze gene
   function. Usually, a gene or gene product is annotated by one or more
   GO terms. We can calculate the similarity between genes to analyze and
   predict gene function. The traditional coverage clustering algorithms
   (CCA) [[75]23] are less time-consuming, handle large amounts of data,
   and have no overlapping region among obtained clusters. However, these
   methods would generate much false positive data without a reasonable
   clustering radius. Therefore, we use GO function similarity to obtain a
   reasonable radius and improve clustering accuracy. The clustering
   radius is computed as follows.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><msub><mi>d</mi><mi>x</mi></msub><mo>=</mo><m
   frac><msub><mi>r</mi><mi>x</mi></msub><mrow><mn>1</mn><mo>+</mo><mi>G</
   mi><msub><mi>O</mi><mrow><mi
   mathvariant="italic">similarity</mi></mrow></msub></mrow></mfrac></mrow
   ></mtd></mtr></mtable></mrow> :MATH]
   2
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>r</mi><mi>a</mi><mi>d</mi><mi>i</mi><mi>u
   </mi><mi>s</mi><mo>=</mo><mfrac><mrow><msub><mo>∑</mo><mrow><mi>x</mi><
   mo>∈</mo><mi>D</mi></mrow></msub><msub><mi>d</mi><mi>x</mi></msub></mro
   w><mrow><msub><mo>∑</mo><mrow><mi>x</mi><mo>∈</mo><mi>D</mi></mrow></ms
   ub><mn>1</mn></mrow></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   3

   We denote rx, the distance of all unlearned seed nodes to the
   clustering center based on the Euclidean distance. We redefine the
   distance as shown in Eq. ([76]2). The clustering radius of each final
   cluster is obtained by Eq. ([77]3). D indicates the seed nodes that
   have not learned yet.

   It shows that in PPI networks, high-density subgraphs incline to form
   protein complexes [[78]24]. Also, in a subgraph, if the internal weight
   is much greater than the external weight, it is more likely to form
   protein complexes. Therefore, density [[79]25] and modularity [[80]26]
   are two important factors, determining whether the subgraph could form
   protein complexes or not. We demonstrate a new method to evaluate
   protein complexes.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e
   </mi><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mi>t</mi><mrow></mrow><mo>∗</m
   o><mi>d</mi><mi>e</mi><mi>n</mi><mi>s</mi><mi>i</mi><mi>t</mi><mi>y</mi
   ><mrow><mo stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow><mo>+</mo><mrow><mo
   stretchy="false">(</mo><mn>1</mn><mo>-</mo><mi>t</mi><mo
   stretchy="false">)</mo></mrow><mrow></mrow><mo>∗</mo><mspace
   width="0.277778em"></mspace><mo>mod</mo><mspace
   width="0.277778em"></mspace><mi>u</mi><mi>l</mi><mi>a</mi><mi>r</mi><mi
   >i</mi><mi>t</mi><mi>y</mi><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   4
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>d</mi><mi>e</mi><mi>n</mi><mi>s</mi><mi>i
   </mi><mi>t</mi><mi>y</mi><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mfrac><mrow><mrow><mn>2</mn><m
   row></mrow><mo>∗</mo><mo
   stretchy="false">|</mo></mrow><msub><mi>E</mi><msub><mi>c</mi><mi>u</mi
   ></msub></msub><mrow><mo
   stretchy="false">|</mo></mrow></mrow><mrow><mrow><mo
   stretchy="false">|</mo></mrow><msub><mi>V</mi><msub><mi>c</mi><mi>u</mi
   ></msub></msub><mrow><mo stretchy="false">|</mo></mrow><mrow><mo
   stretchy="false">(</mo><msub><mi>V</mi><msub><mi>c</mi><mi>u</mi></msub
   ></msub><mo>-</mo><mn>1</mn><mo
   stretchy="false">)</mo></mrow></mrow></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   5
   [MATH: <mrow><mtable><mtr><mtd columnalign="right"><mrow><mspace
   width="0.277778em"></mspace><mo>mod</mo><mspace
   width="0.277778em"></mspace><mi>u</mi><mi>l</mi><mi>a</mi><mi>r</mi><mi
   >i</mi><mi>t</mi><mi>y</mi><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mfrac><mrow><mo>deg</mo><mi>r<
   /mi><mi>e</mi><msub><mi>e</mi><mrow><mi
   mathvariant="italic">in</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow><mo>-</mo><mo>deg</mo><mi>r</mi><mi>e</mi
   ><msub><mi>e</mi><mrow><mi
   mathvariant="italic">out</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow></mrow><mrow><mo>deg</mo><mi>r</mi><mi>e<
   /mi><msub><mi>e</mi><mrow><mi
   mathvariant="italic">in</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow><mo>+</mo><mo>deg</mo><mi>r</mi><mi>e</mi
   ><msub><mi>e</mi><mrow><mi
   mathvariant="italic">out</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><msub><mi>c</mi><mi>u</mi></msub><mo
   stretchy="false">)</mo></mrow></mrow></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   6

   degree
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">in</mi></mrow></msub> :MATH]
   (c
   [MATH: <msub><mrow></mrow><mi>u</mi></msub> :MATH]
   ) is the sum of internal edges of the complexes, degree
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">out</mi></mrow></msub> :MATH]
   (c
   [MATH: <msub><mrow></mrow><mi>u</mi></msub> :MATH]
   ) is the sum number of other edges connected with the complexes, E
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">Cu</mi></mrow></msub> :MATH]
   is the number of all edges in the complexes, and V
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">Cu</mi></mrow></msub> :MATH]
   is the number of all nodes in the complexes. Moreover, we use a
   parameter t to balance the weight of subgraph modularity and subgraph
   density, as shown in Eq. ([81]4).

Obtaining potential virus–host proteins

   We obtain a set of protein complexes cores. To form complete protein
   complexes, we add subsidiary structures to each core with the following
   steps and name them SE: (i) The first-order neighbor nodes of each
   protein complexes core are obtained from the network diagram as
   candidate proteins of the complexes. (ii) Calculating the sum of
   functional similarity between the core and their neighbor nodes based
   on edge weight. (iii) ranking nodes according to the functional
   similarity score, completing protein complexes by adding affiliated
   nodes. The local score of the protein complexes calculates when a new
   node adds. (iv) Repeating (iii) until no more nodes can improve the
   local score, then terminating the expansion of the protein complexes
   core.

   Protein complexes are molecular polymers that participate in the same
   functional region at the same time and space and have direct or
   indirect effects [[82]27]. Studies have shown that the network is
   modular, and proteins in the same complexes are more likely to
   undertake the same life activity or in the same pathway [[83]28]. At
   the same time, the more host proteins contained by the complexes
   indicate that the complexes are more likely to participate in life
   activities such as virus replication [[84]29].

   We obtained 255 protein complexes on the human PPI network dataset.
   Considering incomplete data, we downloaded all human complexes data
   from the CORUM database, including 1948 protein complexes. Then we
   integrated them with our results, choosing protein complexes data with
   nodes than three and known host proteins while deleting the redundant
   data. Therefore we got 455 human complexes, with complexes that contain
   more than two known proteins having sixty. The more known host proteins
   contained in the complexes, the more likely it participate in viral
   survival and reproduction. Since the proteins phenotypes in the same
   protein complexes are similar, the remaining proteins may also become
   the proteins required for virus replication.

   Table [85]2 lists some protein complexes, in which Complex_size
   represents the size of the module, Host_protein is the host protein of
   the virus, and Complexes is the collection of all proteins.

Table 2.

   Partial protein complex data
   ID Complex_size Host_protein Complex
   1 4 [86]P67870; [87]P19784; [88]P09429; [89]P19784; [90]P67870;
   [91]P68400;
   2 5 [92]Q15370; [93]P62877; [94]Q15369; [95]Q13617; [96]P40337;
   [97]Q15370; [98]P62877; [99]Q15369; [100]Q13617;
   [101]Q15369; [102]Q13617; [103]Q15369; [104]Q13617;
   3 5 [105]Q15370; [106]Q15369; [107]Q15369; [108]Q15370; [109]Q93034;
   [110]Q9UBF6; [111]Q9Y576;
   4 7 [112]Q92769; [113]O00422; [114]O75446; [115]Q09028; [116]Q13547;
   [117]Q16576; [118]Q92769; [119]Q96ST3;
   5 3 [120]P13861; [121]P13861; [122]P24588; [123]Q08209;
   6 5 [124]Q9UHD2; [125]P18124; [126]P08238; [127]Q04206; [128]Q9UHD2;
   [129]Q00653;
   7 3 [130]Q86VM9; [131]Q86VM9; [132]Q9BXB5; [133]Q15287;
   8 5 [134]P11940; [135]O75534; [136]P11940; [137]O75534; [138]Q16549;
   [139]Q14103;
   [140]O60506;
   9 16 [141]O43633; [142]Q70EL1; [143]Q9BY43; [144]O95630; [145]Q9UN37;
   [146]Q70EL1;
   [147]Q96FZ7; [148]Q96CF2; [149]Q9NZZ3; [150]Q9H444;
   [151]Q70EL1; [152]Q9HD42; [153]O43633; [154]O75351;
   [155]Q9UN37; [156]Q7LBR1; [157]Q9H444; [158]Q9BY43;
   10 9 [159]O60885; [160]Q7L2J0; [161]O94992; [162]O60563; [163]O60583;
   [164]O60563;
   [165]P06400; [166]O94992; [167]O60885; [168]P50750; [169]Q7L2J0;
   [170]Open in a new tab

   Based on Table [171]2, we can speculate that the unknown human proteins
   in the same complexes are closely related to the known virus–host
   proteins. The table also shows that not only other proteins in the
   module are related to the virus, but also the module itself is related
   to the virus, which jointly completes some biological functions and
   promotes the reproduction of the virus. Therefore, these remaining
   proteins are potential virus–target proteins we need.

   The above descriptions show that each of the complexes contains
   virus–target host proteins and is more or less associated with the
   virus. Then we use the protein complexes and Node2Vec to calculate the
   similarity between the rest of the unknown proteins and known host
   proteins, then score them as potential target proteins. The score
   computes as follows.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><msub
   ><mi>e</mi><mi>c</mi></msub><mo>=</mo><msub><mi>w</mi><mi>c</mi></msub>
   <mrow></mrow><mo>∗</mo><mi>s</mi><mi>i</mi><msub><mi>m</mi><mi>c</mi></
   msub><mrow><mo
   stretchy="false">(</mo><mi>c</mi><mi>o</mi><mi>m</mi><mi>p</mi><mi>l</m
   i><mi>e</mi><mi>x</mi><mo stretchy="false">)</mo></mrow><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   7

   w
   [MATH: <msub><mrow></mrow><mi>c</mi></msub> :MATH]
   indicates the proportion of known host proteins in the complexes to
   which potential target protein C belongs. n
   [MATH: <msub><mrow></mrow><mi>c</mi></msub> :MATH]
   indicates the number of known proteins in the complexes. Let N
   indicates the total number of proteins in the complexes. w
   [MATH: <msub><mrow></mrow><mi>c</mi></msub> :MATH]
   in Eq. ([172]8) can be obtained as follows.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><msub><mi>w</mi><mi>c</mi></msub><mo>=</mo><m
   frac><msub><mi>n</mi><mi>c</mi></msub><mi>N</mi></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   8

   The second term in Eq. ([173]7) indicates the sum of the similarities
   between target protein C and the known proteins in its complexes. The
   formula is as follows.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>s</mi><mi>i</mi><msub><mi>m</mi><mi>c</mi
   ></msub><mrow><mo
   stretchy="false">(</mo><mi>c</mi><mi>o</mi><mi>m</mi><mi>p</mi><mi>l</m
   i><mi>e</mi><mi>x</mi><mo
   stretchy="false">)</mo></mrow><mo>=</mo><munderover><mo
   movablelimits="false">∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow>
   <mi>n</mi></munderover><mrow><mi>s</mi><mi>i</mi><mi>m</mi><mo
   stretchy="false">(</mo><mi>c</mi><mo>,</mo><msub><mi>P</mi><mi>i</mi></
   msub><mo stretchy="false">)</mo></mrow><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   9

   P
   [MATH: <msub><mrow></mrow><mi>i</mi></msub> :MATH]
   indicates each known virus–target protein in the complexes of candidate
   protein C, in which the complexes in the Equation select the complexes
   corresponding to the maximum value in Eq. ([174]8).

Methods

Statistical model

   We define the functional similarity between the two interacting
   proteins a and b as GO
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">similarity</mi></mrow></msub> :MATH]
   , and as follows.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>G</mi><msub><mi>O</mi><mrow><mi
   mathvariant="italic">similarity</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo>,</mo><mi>b</mi><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mrow><mo
   stretchy="false">|</mo><mi>G</mi><msub><mi>O</mi><mrow><mi
   mathvariant="italic">sum</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow><mo>∩</mo><mi>G</mi><msub><mi>O</mi><mrow
   ><mi mathvariant="italic">sum</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><mo
   stretchy="false">|</mo></mrow><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   10

   Based on the definition of protein functional similarity, the adjacency
   matrix A
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">ij</mi></mrow></msub> :MATH]
   of graph G can express as follows.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><msub><mi>A</mi><mrow><mi
   mathvariant="italic">ij</mi></mrow></msub><mo>=</mo><mfenced
   open="{"><msubsup><mrow></mrow><mrow><mn>0</mn><mo>,</mo><mspace
   width="1em"></mspace><mi>o</mi><mi>t</mi><mi>h</mi><mi>e</mi><mi>r</mi>
   <mi>w</mi><mi>i</mi><mi>s</mi><mi>e</mi></mrow><mrow><msub><mi>e</mi><m
   row><mi mathvariant="italic">ij</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>i</mi><mo>,</mo><mi>j</mi><mo>∈</mo><mi>E</m
   i><mo stretchy="false">)</mo></mrow></mrow></msubsup></mfenced><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   11

   e
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">ij</mi></mrow></msub> :MATH]
   equals GO
   [MATH: <msub><mrow></mrow><mrow><mi
   mathvariant="italic">similarity</mi></mrow></msub> :MATH]
   .

Dataset

   Based on the latest study of the interaction map of 2019-nCov
   virus–host protein [[175]30], we obtained 332 high reliability
   2019-nCov human PPI data. We mapped the proteins with the Uniprot ID
   through the Uniprot database and got 256 key host factors in our
   experiments. To reduce the impact of data redundancy on results, we
   used the human PPI data in HIPPIE, which integrates interactive data of
   multiple databases, including MINT, HPRD, and BioGrid. The HIPPIE is
   the most commonly used PPI database. Therefore, we download 65,536 PPI
   data from the HIPPIE database, involving 11,564 human proteins.

Weighting protein networks by GO annotations

   GO is an internationally standardized gene function classification
   system. It consists of a predefined set of GO terms, which can limit
   and describe the function of gene products. GO terms provide the
   logical structure and correlation of biological processes and classify
   biological process (BP), molecular function (MF), and cellular
   component (CC). GO annotations [[176]31] are responsible for describing
   GO terms function. We use G = (V, E) to represent the proteins network,
   V is the set of proteins, and E represents the set of protein–protein
   interactions. The specific steps are as follows: (i) We assume that
   protein a contains N GO annotation sets on BP.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>G</mi><msub><mi>O</mi><mrow><mi
   mathvariant="italic">BP</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mfenced close="}"
   open="{"><mrow><mi>g</mi><msub><mi>o</mi><mn>1</mn></msub><mo>,</mo><mi
   >g</mi><msub><mi>o</mi><mn>2</mn></msub><mo>,</mo><mo>⋯</mo><mo>,</mo><
   mi>g</mi><msub><mi>o</mi><mi>n</mi></msub></mrow></mfenced><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   12

   M GO annotation sets on MF.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>G</mi><msub><mi>O</mi><mrow><mi
   mathvariant="italic">MF</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mfenced close="}"
   open="{"><mrow><mi>g</mi><msub><mi>o</mi><mn>1</mn></msub><mo>,</mo><mi
   >g</mi><msub><mi>o</mi><mn>2</mn></msub><mo>,</mo><mo>⋯</mo><mo>,</mo><
   mi>g</mi><msub><mi>o</mi><mi>m</mi></msub></mrow></mfenced><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   13

   K GO annotation sets on CC.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>G</mi><msub><mi>O</mi><mrow><mi
   mathvariant="italic">CC</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mfenced close="}"
   open="{"><mrow><mi>g</mi><msub><mi>o</mi><mn>1</mn></msub><mo>,</mo><mi
   >g</mi><msub><mi>o</mi><mn>2</mn></msub><mo>,</mo><mo>⋯</mo><mo>,</mo><
   mi>g</mi><msub><mi>o</mi><mi>k</mi></msub></mrow></mfenced><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   14

   (ii) According to the tree structure of GO, we can calculate all
   parental annotation sets of protein a under different categories, then
   add to the original annotation set of GO(a), and remove the redundant
   data. The function annotation of protein a obtains as follows.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>G</mi><msub><mi>O</mi><mrow><mi
   mathvariant="italic">sum</mi></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow><mo>=</mo><mfrac><mrow><mi>G</mi><msub><m
   i>O</mi><mrow><mi>B</mi><msub><mi>P</mi><mrow><mi
   mathvariant="italic">sum</mi></mrow></msub></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow><mo>+</mo><mi>G</mi><msub><mi>O</mi><mrow
   ><mi>M</mi><msub><mi>F</mi><mrow><mi
   mathvariant="italic">sum</mi></mrow></msub></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow><mo>+</mo><mi>G</mi><msub><mi>O</mi><mrow
   ><mi>C</mi><msub><mi>C</mi><mrow><mi
   mathvariant="italic">sum</mi></mrow></msub></mrow></msub><mrow><mo
   stretchy="false">(</mo><mi>a</mi><mo
   stretchy="false">)</mo></mrow></mrow><mn>3</mn></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   15

Cross-validation method

   We adopt Cross-Validation to adjust the parameters reasonably under the
   condition of moderate source datasets and apply them to practical
   problems. Furthermore, we use the K-fold Cross-Validation method to
   evaluate the experimental results.

   Firstly, we divide the identified host protein complexes into a
   training set and validation set according to the ratio of 8:2. To know
   how many validation data are in top k, we conduct ten groups of control
   experiments to verify the results and use the ratio as the final target
   proteins classification standard. We consider deleting the candidate
   protein with zero scores in the final ranking process to reduce the
   data redundancy. Moreover, k ranges from 0 to 100 with a step of 10,
   The Cross-Validation shows that the prediction results can be divided
   into four categories, as shown in Table [177]3.

Table 3.

   Classification table of predict results
   Type of host protein Result (whether host protein) Type of evaluation
   Host protein         Positive                      TP
   Host protein         Negative                      FN
   Not host protein     Positive                      FP
   Not host protein     Negative                      TN
   [178]Open in a new tab

   In this section, the true-positive rate (TPR) and false-positive rate
   (FPR) values are obtained according to the four results in
   Table [179]3, as shown in Eqs. ([180]16) and ([181]17), TPR indicates
   the prediction coverage of our method.
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>T</mi><mi>P</mi><mi>R</mi><mo>=</mo><mfra
   c><mrow><mi
   mathvariant="italic">TP</mi></mrow><mi>P</mi></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   16
   [MATH: <mrow><mtable><mtr><mtd
   columnalign="right"><mrow><mi>F</mi><mi>P</mi><mi>R</mi><mo>=</mo><mfra
   c><mrow><mi
   mathvariant="italic">FP</mi></mrow><mi>N</mi></mfrac><mspace
   width="4pt"></mspace></mrow></mtd></mtr></mtable></mrow> :MATH]
   17

   N represents candidate proteins that are not in the validation data. P
   represents candidate proteins that are in the validation data. TP means
   the recognized correctly by CCA-SE. FP means the recognized quantity
   wrongly.

   Considering the unbalanced data distribution between known and unknown
   proteins, we use the values of receiver operating characteristic (ROC)
   and area under the receiver operating characteristic curve (AUC) as
   evaluation indexes, then plot the ROC curves under different thresholds
   of TPR and FPR. The abscissa represented the FPR while the ordinate
   represented the TPR.

Experiments

Selection of parameter k

   We analyze the influence of parameter k, then select the best k value.
   Let the value of k vary from 0 to 100. The ROC curve shows in
   Fig. [182]3. The prediction results divide into two categories, the
   first k% data considered as the predicted potential target proteins,
   and the second (100-k)% data not considered as the predicted potential
   target proteins. It can be seen from Fig. [183]3 that when k is 40,
   CCA-SE successfully predicts 22 known host proteins. According to the
   above host PPI network, 80% of the proteins are non-structural protein
   interactions encoded by the 2019-nCov. In addition, this experimental
   result also shows that the predicted protein scores are high when the
   value of k increases, indicating that the use of Eq. ([184]7) helps to
   improve the sorting performance of candidate proteins. We plot the AUC
   curve to show the advantages and disadvantages of each group of
   algorithms. As shown in Fig. [185]4, the AUC curve is relatively stable
   in the ten groups of control experiments, which is basically around
   0.81, indicating that the algorithm still shows good performance even
   when the amount of data is less than the actual biological network
   data.

Fig. 3.

   [186]Fig. 3
   [187]Open in a new tab

   The influence of parameter k

Fig. 4.

   [188]Fig. 4
   [189]Open in a new tab

   Comparison of AUC values in multiple control experiments

   In summary, in the subsequent prediction of potential virus–target
   proteins, we set the value of k as 40 to achieve the best experimental
   results.

Comparative experiment of data integration

   To make a horizontal comparison and evaluate the applicability of the
   integrated data [[190]32], we compare the results of adding complexes
   data based on CCA-SE with only containing public human protein
   complexes data set. We also use TPR and FPR as evaluation indicators.
   The following table shows that the TPR containing public human protein
   complexes is only 68.67%, which indicates that the integrated protein
   complexes data can improve accuracy and make the biological data more
   comprehensive. It also shows that the complexes recognized by CCA-SE
   are more biological (Table [191]4).

Table 4.

   Experimental comparison of whether the complex is integrated or not
   Comparision algorithm Number of recognition TPR (%) FPR (%)
   Unintegrated data     17                    68.67   38.08
   Integrated data       22                    72      40.31
   [192]Open in a new tab

Performing GO enrichment analysis on prediction results

   After we obtain biological data, to read the genetic information,
   differential genetic analysis is a necessary experiment between
   different samples. Therefore we need to annotate these genes because
   the number of these genes are maybe large and difficult to compare. A
   common method divides these genes or proteins into several categories,
   one category is equivalent to a GO term. This process is called
   enrichment analysis. Commonly used enrichment analysis methods include
   GO analysis and KEGG analysis.

   We integrate known virus–target host proteins with their directly
   interacted human proteins and apply CCA-SE to the whole network. Our
   predicted results are listed in “Additional file [193]1” and named “new
   targets”. In our results, eight proteins have been proved among the top
   ten, which are [194]Q13617, [195]Q15370, [196]P62877, [197]Q15369,
   [198]P09884, [199]Q14181, [200]P35658, and [201]P78406. It reported
   that 87% of them combined with a virus of non-structural proteins. At
   the same time, these non-structural proteins are cleaved by 3CLPro, and
   this protease is one of the organic substances necessary for the
   reproduction of 2019-nCov. On the other hand, researchers have not
   recognized similar restriction sites in the human body. It is a medical
   value that we target 3CLPro as a drug target. Based on the above
   analysis, CCA-SE can recognize virus–target host proteins. Table [202]5
   is part of the potential target proteins.

Table 5.

   Some of the potential virus–target host proteins
   Uniprot ID  Protein name                MF          CC          BP
   [203]O15264 Mitogen-activated protein   GO:0005515; GO:0005829;
   GO:0071347;
               Kinase 13 (MAPK13)          GO:0005524; GO:0005737; GO:0018105
                                           GO:0004674; GO:0005634; GO:0070301;
                                           GO:0004707;             GO:0007049;
                                                                   GO:0050729;
                                                                   GO:1903936;
                                                                   GO:0006970;
                                                                   GO:0051403;
   [204]Q92598 Heat shock protein family H GO:0005515; GO:0005654;
   GO:1900034;
               (Hsp110) member 1 (HSPH1)   GO:0005524; GO:0005829; GO:0051135;
                                           GO:0000774; GO:0005737; GO:0045345;
                                           GO:0043014; GO:0005634; GO:0051085;
                                                       GO:0070062; GO:0006986;
                                                       GO:0032991; GO:0050790;
                                                       GO:0071682; GO:0006898;
                                                       GO:0005874;
                                                       GO:0005576;
   [205]Q13177 p21 (RAC1) activated kinase GO:0005515; GO:0005829;
   GO:0071407;
               2(PAK2)                     GO:0042802; GO:0005737; GO:0050770;
                                           GO:0019901; GO:0005634; GO:0051497;
                                           GO:0005524; GO:0098978; GO:0018105;
                                           GO:0045296; GO:0005911; GO:0031295;
                                           GO:0004674; GO:0014069; GO:0040008;
                                           GO:0030296; GO:0048471; GO:0043066;
                                           GO:0031267; GO:0005886; GO:0006469;
                                           GO:0004672;             GO:0150105;
                                                                   GO:0034333;
                                                                   GO:0006468;
                                                                   GO:0046777;
                                                                   GO:0050852;
                                                                   GO:0031098;
   [206]Q15311 ralA binding protein        GO:0005515; GO:0005829; GO:0043547;
               1(RALBP1)                   GO:0042910; GO:0016020; GO:0007264;
                                           GO:0005096;             GO:1990961;
                                           GO:0042626;             GO:0051056;
                                           GO:0022857;             GO:0043087;
                                                                   GO:0006935;
                                                                   GO:0006897;
                                                                   GO:0055085;
   [207]P78545 E74 like ETS transcription  GO:0005515; GO:0005654;
   GO:0045892;
               Factor 3(ELF3)              GO:0000978; GO:0005829; GO:0045944;
                                           GO:0001228; GO:0005634; GO:0006366;
                                           GO:1990837; GO:0005794; GO:0045747;
                                           GO:0003700;             GO:0006357;
                                           GO:0000981;             GO:0030855;
                                                                   GO:0060056;
                                                                   GO:0006954;
                                                                   GO:0001824;
                                                                   GO:0030198;
                                                                   GO:0030154;
   [208]Open in a new tab

   To verify the accuracy of the CCA-SE in predicting candidate
   virus–target proteins, we only conduct gene enrichment analysis for the
   top 50 potential target proteins. Table [209]6 lists the enrichment
   analysis results of proteins that are high scores. These GO terms meet
   the p value of less than 0.05. The analysis results in Table [210]6
   showed that most predicted GO annotations of target proteins are
   related to biological processes such as protein binding, enzyme
   binding, transcription factor binding, transcription factor activity,
   protein kinase binding, apoptosis, and proliferation. For example,
   GO:0005515, which belongs to MF. Previous studies have found that the
   spike protein RBD encoded by 2019-nCov contains six amino acids,
   including L455, F486, Q493, S494, N501, and Y505. Meantime, RBD can
   integrate with the ACE2 protein of human lung epithelial cells, so we
   can infer that the host protein corresponding to this functional
   annotation has a huge correlation with these residues. Another example,
   GO:0016032, which plays an important role in virus affection, relates
   to the viral genome replication and the assembly of progeny virus
   particles. Moreover, Babak Khorsand [[211]18] listed the most central
   nodes in human interactions of 2019-nCov in his paper, which are
   [212]Q86VP6, [213]Q92905, [214]Q13573, and [215]P01106. Our results
   included [216]Q92905, [217]Q13573, and [218]P01106, and we both
   performed experiments in the same datasets. The prediction of
   2019-nCov-target potential host proteins shows a significant enrichment
   effect. Demonstrating the accuracy of our prediction based on a
   molecular network.

Table 6.

   Results of GO enrichment analysis
   GO ID      GO terminology                         p value
   GO:0016032 Viral process                          1.23e−4
   GO:0003677 DNA binding                            1.40e−4
   GO:0004842 Ubiquitin-protein transferase activity 1.32e−4
   GO:0005515 Protein binding                        1.33e−4
   GO:0031625 Ubiquitin protein ligase binding       1.09e−05
   [219]Open in a new tab

KEGG pathway analysis of prediction results

   Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database with
   functional information about each gene. The core of the KEGG database
   is KEGG PATHWAY and KEGG ORTHOLOGY. In KEGG PATHWAY, biological
   metabolic pathways are divided into six categories, namely Cellular
   Processes, Environmental Information Processing, Genetic Information
   Processing, Human Diseases, Metabolism, and Organismal Systems. Once we
   get the differential gene information, in order to learn their
   functions more clearly, gene enrichment analysis may be used to
   discover biological pathways that play a key role in biological
   processes, so that we can better understand the molecular mechanisms of
   biological processes. KEGG pathway analysis [[220]33] selects pathway
   databases and human-related pathways to analyze the predicted proteins.

   In Fig. [221]5, C1–C6 represents the result processed by KEGG pathway
   enrichment analysis. C1 means Annotation Cluster 1, C2 means Annotation
   Cluster 2, and so on. We sort the results in descending order of
   Enrichment Score, and “other” includes those genes that do not belong
   to any of the clusters. These genes have not shown their functional
   characteristics in our pathway analysis, we consider them less
   important factors in our potential virus–target experiments. According
   to Fig. [222]5, a total of 42 pathways (p-value 0.05) are obtained by
   screening proteins with high scores, mainly including the TNF signaling
   pathway, T cell receptor signaling pathway (TCR) pathway, and MAPK
   pathway related to cell cycle and inflammatory immune regulation,
   PI3K-Akt signaling pathway and HIF-1 signaling pathway related to
   pulmonary fibrosis regulation, renal cell carcinoma pathway related to
   viral diseases and human immunodeficiency virus 1 infection pathway.
   The above pathways demonstrate that although some predicted host
   proteins do not directly interact with virus-encoded proteins, they are
   closely related to the pathogenesis of the virus.

Fig. 5.

   [223]Fig. 5
   [224]Open in a new tab

   Enrichment function analysis of KEGG signaling pathway

   The existing studies have shown that the MAPK pathway is related to
   cell growth and mutations. TNF is mainly produced by T cells and NK
   cells, and both are closely related to inflammation. They can release
   signals to interact with specific receptors on the cell surface, making
   them conservative, so that MAPK-JNK, 5-lipoxygenase, and other
   signaling pathways are activated, making cytokines related to
   inflammation disorders, such as abnormal expression of gp130 and IL-1,
   and promoting human inflammatory response [[225]3, [226]4].

   The PI3K-Akt signaling pathway is closely related to the renal cell
   carcinoma pathway, as shown in Fig. [227]6. Tyrosine kinase receptors
   can activate phosphatidylinositol 3-kinases (PI3Ks) signaling pathways,
   which are related to cell proliferation and apoptosis. When PI3Ks is
   activated, it produces a messenger that binds to the signal protein
   PDK1 containing the PH domain. Through phosphorylation, the Akt
   signaling protein is activated to form PI3K-Akt. This protein can also
   phosphorylate and regulate the downstream factor mammalian target of
   rapamycin (mTOR) [[228]34], thereby activating the mTORC1 pathway,
   which participates in the expression of T cytokines in the immune
   system and promotes the enhancement of the immune system.

Fig. 6.

   [229]Fig. 6
   [230]Open in a new tab

   Renal cell carcinoma pathway

Conclusion

   In this paper, we proposed a protein complexes recognition method
   CCA-SE. The protein complexes obtained by CCA-SE were integrated with
   the human protein complexes to obtain a more reliable protein complexes
   dataset, then we defined a score function to get potential target
   proteins. The scoring function takes into account not only the
   relationship between the protein complexes and the virus-encoded
   proteins but also the protein itself to predict the virus–target human
   proteins. Moreover, we verified the effectiveness of CCA-SE on the
   biological network under different parameter settings. At the same
   time, the selected target proteins were imported into the DAVID v6.7
   database ([231]https://david.ncifcrf.gov/). We conducted GO function
   enrichment analysis and KEGG signal pathway enrichment analysis. The
   analysis explained the correlation between the predicted results
   obtained by the CCA-SE and the life process of virus infection and
   replication and proved the accuracy from the biological perspective.
   The experimental results showed that CCA-SE can effectively recognize
   human proteins targeted by the 2019-nCov and play a fundamental role in
   detecting virus drug targets.

Supplementary Information

   [232]12859_2022_4792_MOESM1_ESM.xls^ (23.5KB, xls)

   Additional file 1. The Additional file 1 contains “new targets”. “new
   targets” lists our predicted results and shows our method of predicting
   virus–target human proteins does achieve good results.

Acknowledgements