Skip to main content

Prediction of anti-microtubular target proteins of tubulins and their interacting proteins using Gene Ontology tools



Tubulins are highly conserved globular proteins involved in stabilization of cellular cytoskeletal microtubules during cell cycle. Different isoforms of tubulins are differentially expressed in various cell types, and their protein–protein interactions (PPIs) analysis will help in identifying the anti-microtubular drug targets for cancer and neurological disorders. Numerous web-based PPIs analysis methods are recently being used, and in this paper, I used Gene Ontology (GO) tools, e.g., Stringbase, ProteomeHD, GeneMANIA, and ShinyGO, to identify anti-microtubular target proteins by selecting strongly interacting proteins of tubulins.


I used 6 different human tubulin isoforms (two from each of α-, β-, and γ-tubulin) and found several thousands of node-to-node protein interactions (highest 4956 in GeneMANIA) and selected top 10 strongly interacting node-to-node interactions with highest score, which included 7 tubulin family protein and 6 non-tubulin family proteins (total 13). Functional enrichment analysis indicated a significant role of these 13 proteins in nucleation, polymerization or depolymerization of microtubules, membrane tethering and docking, dorsal root ganglion development, mitotic cycle, and cytoskeletal organization. I found γ-tubulins (TUBG1, TUBGCP4, and TUBBGCP6) were known to contribute majorly for tubulin-associated functions followed by α-tubulin (TUBA1A) and β-tubulins (TUBB AND TUBB3). In PPI results, I found several non-tubular proteins interacting with tubulins, and six of them (HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41) were found closely associated with their functions.


Increasing number of regulatory proteins and subpopulation of tubulin proteins are being reported with poor understanding in their association with microtubule assembly and disassembly. The functional enrichment analysis of tubulin isoforms using recent GO tools resulted in identification of γ-tubulins playing a key role in microtubule functions and observed non-tubulin family of proteins HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41 strongly interacting functional proteins of tubulins. The present study yields a promising model system using GO tools to narrow down tubulin-associated proteins as a drug target in cancer, Alzheimer’s, neurological disorders, etc.


Tubulin isoforms play a major role in modulating microtubule structure, dynamics, and mechanics. Their polymerization into microtubules is indispensable for cell division, growth, intracellular trafficking, and cell signaling. It has three functional domains, namely guanine triphosphate (GTP) binding, drug binding, and motor microtubule-associated protein (MAP)-binding domains. Gene mutations of tubulins are associated with a large spectrum of diseases such as cancer, Alzheimer’s, abnormal neuronal migration, neuronal motor impairments, intellectual disability, and epilepsy [1,2,3,4]. In eukaryotes, tubulin superfamily includes six types: alpha, beta, gamma, delta, epsilon, and zeta [5, 6]. Human microtubules constitute mixed combinations of these isotypes encoded by different genes on distinct chromosomes. These isotypes differ from one another by divergent sequences at their carboxy-terminal (C-terminal) tail [7,8,9].

Studies in protein–protein interactions and cellular functions with tubulins help in identifying drug targets and in understanding disease pathologies associated with tubulins. In vitro studies using plant alkaloids demonstrated important ligand-binding domains in tubulin isoforms. The natural and synthetic agents of plant alkaloids such as colchicine, paclitaxel, and vinblastine were demonstrated to interact with tubulin as anti-microtubular drugs [10, 11]. The interaction of anti-microtubular drugs paclitaxel and vinblastine was earlier demonstrated as anticancer drugs in aggressive metastatic tumors. The functional roll of β-III tubulin in voltage-dependent anion channel (VDAC) was determined using brain synaptosomes [12]. Cell signaling studies of tubulin in antigen-mediated mast cells suggested new strategies in the treatment of allergies and inflammatory diseases. The overexpression of β-II tubulin was reported to promote cancer growth and metastasis and observed as a useful prognostic marker. The inhibition of proteins with anti-apoptotic pathway plays a major role in understanding the development of anti-microtubular drugs for anticancer drug discovery [13]. The overexpression of BCL2, an apoptosis regulatory protein, was earlier shown to sensitize tumor cells to programmed cell death induced by anti-microtubule drugs. Various isoforms of tubulin were identified as prognostic markers in solid tumors [14], for example, β-III tubulin has reportedly overexpressed in rectal cancer, gastric cancer, and gliomas.

Multiple isoforms of tubulin are reported to maintain the stability of microtubules named as microtubular associated proteins (MAPs) [15]. However, their combined functional role is incompletely understood. Based on the curated dataset using sequences from 611 proteins, an online computational tool, namely MAP analyzer, was designed as MAP predictor to identify microtubule-interacting proteins based on their sequence motifs. This tool contains four types of microtubule-related proteins: (1) proteins directly binding to tubulins, (2) proteins altering the microtubule organization and dynamics, (3) proteins colocalizing with microtubules, (4) and indirectly interacting microtubule proteins. This database can be used with protein IDs and protein names obtainable through UniProtKB, RefSeq, etc. Most of the MAPs are reported to interact with microtubule by binding with polymerized or depolymerized tubulin dimers for stabilizing the microtubules. Several MAPs are used as drug targets and MAP inhibitors to be used in clinical trials [16, 17]. Promising approach to find a suitable microtubule drug target depends on understanding tubulin molecular signaling pathways and their protein–protein interactions, which can be performed using GO tools. In the present study, I wanted to identify druggable target proteins of tubulin and their associated proteins by identifying strongly interacting proteins of α-, β-, and γ-tubulins using web-based GO tools.


Selection of tubulin isoforms for PPI analysis

In the present study, I used six tubulin isoforms (6TI), two from each of human α-, β-, and γ-tubulin, e.g., TUBA1A, TUBA1B, TUBB, TUBB3, TUBG1, AND TUBGCP2, respectively, for PPI analysis. Our working model involved selection of strongly interacting proteins of tubulin superfamily using open-source web-based GO tools (Fig. 1). In Stringbase cutoff confidence score of 0.44, GeneMANIA as per version 3.6.0 default setup and ProteomHD cutoff score of 0.9 were used for PPI search. Our selection process for identifying final set of strongly interacting protein of tubulin for functional analysis involved three steps. The first step involved use of all 6TI together in Stringbase, GeneMANIA, and ProteomeHD, second step involved use of each isoform separately, and final step involved use of all the pooled dataset in GeneMANIA.

Fig. 1
figure 1

Flow chart depicting the steps involved in identification of tubulin interacting proteins and functional enrichment methods

PPI analysis using String database

Stringbase version 11.5 is an open-source web-based database for PPI analysis available at [18, 19]. It depicts functional association between two proteins jointly contributing to a specific function. The interaction between two proteins does not need physical interaction; it would be sufficient if some part of overlapping occurred functionally in a pathway or functional map. By this definition, proteins which are found inhibitory in action can be functionally associated in the pathway. In this PPI search, the data were derived through various modes from curated databases, experimentally determined, gene neighborhood, gene fusions, co-occurrences, co-expressions, and protein homology. In PPI analysis, the edges are representation between two interacting nodal proteins as predicted edges derived based on experimental evidences and protein associations from 12 different biological data sources. The obtained data from PPI analysis were saved into Excel sheet by downloading tabular text output, and top-level interacting proteins were selected based on highest score values.

PPI analysis using ProteomeHD

ProteomeHD is an open-source functional annotation tool designed based on R script which builds a database of co-regulated proteins using unsupervised machine learning. This PPI analysis tool is available as an interactive and functionally annotated map at [20]. The results were obtained with dual cellular functions which are more informative than mRNA co-expression analysis. It provides functional insights that are difficult to obtain by other proteomics approaches. The results of proteins with co-regulation score cutoff value was set at 0.9, and interaction data was obtained in CSV format to identify top-level interacting proteins based on highest score values.

PPI analysis using GeneMANIA

GeneMANIA (version 3.6.0) is an open-source protein network search engine available freely at [21]. It finds new members in gene network and builds weighted functional interaction data for each gene based on their predictive value for the query list. It can often find additional genes from the network giving more weightage to physical interaction or predicted physical interactions and prioritize genes for functional assays. It builds network based on millions of interactions obtained from IRefIndex, GEO, BioGRID, and I2D as well as from organism-specific functional genomics data sets. The resulting functional and interaction data can be downloaded into Excel sheet through notepad for identifying top-level interacting genes based on highest score values.

Functional enrichment analysis

At first step, I used 6TI and selected set of strongly interacting proteins of tubulin for functional analysis in GeneMANIA. Wherein, functional categorization of tested gene set with specific FDR value (false discovery rate) with responsible number of genes involved in the network and from the genome were represented in table. The highest number of genes involved were shown first. Next, I used open-source web-based GO tool ShinyGO version 0.76 for high-level functional enrichment analysis at [22] which produces hierarchical clustering trees, networks summarizing overlapping terms/pathways, protein–protein interaction networks, gene characteristics plots, and enriched promoter motifs based on annotation from Ensembl. The number of folds of functional enrichment with number of genes involved with FDR values is graphically represented. Furthermore, the categorization of numbers and groups of genes involved in high-level functional enrichment is represented in tabular format downloadable directly from the database. The correlation of significant functional pathway enrichment is represented through hierarchical tree clustering, and results were downloaded in PNG format from database.


Selection of strongly interacting proteins of tubulins

To identify tubulin-interacting proteins, the PPI analysis was carried out as described in flow chart (Fig. 1). In the first step, I used all 6TI together for PPI analysis in Stringbase, ProteomeHD, and GeneMANIA. In Stringbase, I observed 11 edges in PPIs between the score of 1 and 0.97 and observed all PPIs within tubulin family of isoforms (Fig. 2), GeneMANIA indicated 1183 edges in PPIs between the score of 1 and 0.26 (Fig. 3), and ProteomeHD indicated 7813 PPIs between the score of 0.87 and 0.61 (Fig. 4). Overall. in top 10 PPIs at the first level of selection, I found TUBGCP4, TUBB2B, TUBG2, TUBGCP2, TUBGCP3, TUBB8, TUBA1C, and TUBGCP5 proteins as new tubulin family interacting proteins and UNC5C, PKM, CLIC1, GAPDH, RPL4, PCBP1, AND PRDX1 as non-tubulin family proteins (Table 1).

Fig. 2
figure 2

Six selected tubulin isoforms PPI in Stringbase tool. Network nodes details are as follows: number of nodes: 6; number of edges 11; avg. local clustering coefficient 0.839; PPI enrichment p-value 1.54e-14

Fig. 3
figure 3

Six selected tubulin isoforms PPI in GeneMANIA. Totally, 1183 edges were detected between these 6 isoforms. Showing 20 related genes, with 26 total genes, 1177 total links, and no attributes

Fig. 4
figure 4

Six selected tubulin isoforms PPI in ProteomeHD. Cross-validated training data were used to assess performance, yielding an area under the precision-recall curve (AUPRC) of 0.379. (A random classifier would yield an AUPRC of 0.006.) Total protein interactions resulted identification of 7813 proteins

Table 1 Using all 6 tubulin isoforms together in single search, top 10 PPI interactions in Stringbase, GeneMANIA, and ProteomeHD are shown with node-to-node interaction scores. As per ProteomeHD results format, single list column of interacting proteins is shown in last column

In the second level of PPI analysis, I used single tubulin isoform separately as key word using Stringbase, ProteomeHD, and GeneMANIA. In Stringbase, I identified 9 top-level interacting protein as functionally predicted partners with maximum number of 111 interacting proteins (Table 2). In ProteomeHD and GeneMANIA, top 10 interacting proteins were shown in Tables 3 and 4 with maximum number of 1193 and 852 PPIs, respectively. I pooled all top-level interacting proteins from the Table 1 and Tables 2, 3, and 4 resulting in identification of 224 tubulin and non-tubulin family of proteins altogether interacting directly with tubulin isoforms or indirectly associated with each isoform through co-regulation or co-expressions. For third level of PPI analysis, the pooled 224 proteins were used in GeneMANIA to identify more interacting protein, which resulted in identification of 4956 PPIs. Using this large dataset, I specifically selected top 10 proteins interacting with tubulin family based on weight of the edges (Table 5). In this third level of selection, I found TUBG1, TUBB3, TUBGCP4, TUBB, TUBA1B, TUBGCP6, AND TUBA1A as 7 top-level tubulin family of proteins strongly interacting with 6 other proteins such as HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41, and I used these 13 proteins for high-level functional enrichment analysis. All the 6TI selected for PPI interactions showed strong node–node interaction data within themselves in all the databases. TUBGCP2 (γ-tubulin family) exhibited maximum number of PPIs among tubulin family. Though several non-tubulin families of protein interactions were observed as in Tables 2, 3, and 4, I selected HTT, DPYSL2, SKI, UNC5C, NINL, and DDX4 for functional enrichment analysis as they were found to be repeated in PPIs.

Table 2 Stringbase PPI search was used with single tubulin isoform separately to identify functional predicted partners with highest interaction score. The total number of protein interactions varied between 71 and 111 node-to-node interactions
Table 3 ProteomeHD PPI search was used with single tubulin isoform separately to identify functional predicted partners with highest interaction score. The total number of protein interactions varied between 157 and 1193 node-to-node interactions
Table 4 GeneMANIA search was used with single tubulin isoform separately to identify functional predicted partners with highest interaction score. The total number of protein interactions varied between 400 and 852 node-to-node interactions
Table 5 The pooled dataset of 224 genes from all the PPI databases was used in GeneMANIA to identify top ten strongly interacting proteins as shown in above table. The interactive values between two genes gene 1 and gene 2 are shown as weight. GeneMANIA search resulted in 4956 interacting nodal edges, and 13 proteins were identified from the table. The maximum interactions of γ-tubulin family (TUBG1, TUBGCP4, and TUBGCP6) were observed

Functional enrichment analysis of strongly interacting proteins of tubulins

In first step of functional enrichment analysis, I determined the functional characteristics of 6TI together in GeneMANIA and observed a maximum of 15 genes in network associated with important functions in protein binding and localization of nucleic acids and chromosomes (Table 6). The 13 strongly interacting protein of tubulins exhibited similar functions as with 6TI, with maximum of 11 genes from the network and exhibited additional functions as microtubule binding, polymerization or depolymerization of microtubules, membrane tethering, membrane docking, cell cycle association functions, and enzyme activities (Table 7). Furthermore, high-level functional enrichment analysis using ShinyGO suggested microtubule and cell cycle functions with more than 100-fold functional enrichment in dorsal root ganglion development and microtubule nucleation (Fig. 5).

Table 6 Functional enrichment analysis of six tubulin isoforms together in GeneMANIA. The functional significance of tubulins is listed starting with lowest FDR (false discovery rate) value
Table 7 Functional enrichment analysis of top 13 strongly interacting proteins of tubulins in GeneMANIA. The functional significance of tubulins is listed starting with lowest FDR (false discovery rate) value
Fig. 5
figure 5

High-level functional enrichment analysis of top 13 strongly interacting proteins of tubulin in ShinyGO ontology

To identify responsible genes contributing to specific function, I categorized the top 13 strongly interacting proteins of tubulin using ShinyGO. Out of 13 genes, maximum of 7 genes were shown to involve in cell cycle process, cellular component, and cellular localization. Additional functions were described as in Table 8. The results in Tables 5 and Table 8 indicated a maximum number repeated interactions of γ-tubulins family of proteins (TUBG1, TUBGCP4, & TUBBGCP6), which were known to contribute majorly for tubulin-associated functions compared with α-tubulin (TUBA1A) and β-tubulins (TUBB AND TUBB3). HTT, DPYSL2, SKI, UNC5C, NINL, and DDX were found closely associated with many of the tubulin-associated functions. I next evaluated correlation among significant pathways using tree dendrogram (Fig. 6), in which many shared genes were clustered together and observed highest significant values with microtubule organization, mitotic cell cycle, and cytoskeleton organization.

Table 8 Groupings of high-level functional enrichment of top 13 strongly interacting tubulin proteins using ShinyGo. The maximum interactions of γ-tubulin family (TUBG1, TUBGCP4, and TUBGCP6) were found more
Fig. 6
figure 6

A hierarchical clustering tree summarizing the correlation among significant pathways of top 10 tubulin interacting genes in ShinyGO. Pathways with many shared genes were clustered together. Bigger dots indicate more significant P-values


Increasing number of regulatory proteins and subpopulation of tubulin proteins are being reported with poor understanding in their association with microtubule assembly and disassembly. In different cell types, tubulin isotypes are expressed at different levels with various cellular functions by multiple genes [1, 2, 7, 23]. In this paper, the selection of 6TI was based on the manual curation, giving importance to all three tubulin classes (α-, β-, & γ-tubulins). Extensive literature search indicated high diversity of tubulin isotypes in various animal species, and in the present study, I selected two isotypes from each major class of α-, β-, and - γ - tubulins belonging to humans. All the 6TI previously exhibited important molecular functions in MT dynamics, and their functional abnormalities were reported in human disorders such as neuronal abnormalities, impaired motor behaviors, and cancers [24,25,26,27,28,29].

In our PPI analysis using 6TI, I shortlisted top level and strongly interacting proteins of tubulins out of several thousands of PPIs observed in GO databases. As described in methodology, these PPI data were obtained from curated GO databases. The majority of the results of tubulin PPIs were based on experimental findings from molecular interactions studies of tubulin isotypes in relation to MT dynamics through various signaling pathways, enzyme activity, gene fusions, mRNA co-expressions, co-regulations, or physical interactions [18,19,20,21,22].

In our initial PPI analysis, GeneMANIA identified 20 additional tubulin isotypes and a non-tubular protein UNC5C exhibiting strong interaction with TUBB3. Earlier studies reported that UNC5C (netrin receptor) directly interacted with TUBB3 in axon outgrowth of primary neurons and mediated microtubule polymerization which could be inhibited by its ligand netrin-1 in cosedimentation assay [30]. In initial round of PPI search, I found UNC5C (netrin receptor) interaction with TUBB3 as strong PPI interactions, which is a non-tubulin family of protein involved in neuronal development and also associated with Alzheimer’s disease [31].

Mutations in TUBB3 were previously reported in facial palsy and peripheral neuropathy [32] and also reported in the development of gallbladder cancers through Akt/mTOR signal pathway [27]. Other than 6TI, repeated PPIs of γ-tubulin subtypes (TUBGCP2, TUBGCP3, TUBGCP4, and TUBGCP5) were observed, and such involvement of γ-tubulins subtypes was previously reported to play essential role in GCP formation during assembly of microtubules. GCP abnormalities were earlier reported in cancer progression, retinopathy, and neuronal abnormalities [23, 33, 34]. Similarly, other additional tubulin isotypes TUBB2B, TUBB8, and TUBA1C, which I observed in first level of our selection steps, were previously reported to involve in nucleation of MT, cancer progression, oocyte maturation, and neuronal abnormalities [23, 35, 36].

Similarly, initial PPI analysis using 6TI together in Stringbase and GeneMANIA indicated strong PPIs of TUBGCP2 with other γ-tubulin subtypes (TUBG1, TUBGCP2, &TUBGCP4) and in the second round of PPI analysis of TUBGCP2 in all databases indicated its strong interactions with other γ-tubulin subtypes (TUBG1 AND TUBG2) as well as with several other GCP subtypes (TUBGCP3, TUBGCP4, TUBGCP5, and TUBGCP6) possibly indicating their combined functional role of these GCP subtypes in the formation of γ-tubulin ring complex (γ-TuRC). In third round of PPI analysis in GeneMANIA, I found several γ-tubulin subtypes interacting with non-tubulin family of proteins. The involvement of γ-tubulin in the formation of γ-TuRC was previously reported [3, 5, 37], and in the present study, I identified the involvement of additional γ-tubulin subtypes in the formation of γ-TuRC. In our three selection steps of PPI analysis using different databases, I found the interaction of TUBGCP2 with more number of γ-tubulin subtypes (TUBGCP3, TUBGCP4, TUBGCP5, and TUBGCP6). The possible role of γ-tubulin subtypes was earlier reported to regulate the nucleation of α-/β-tubulin heterodimers in microtubule assembly [37, 38]. In in vitro cellular assay, γ-tubulin was reported to form a complex with proliferating cell nuclear antigen (PCNA), and a significant correlation in expression of γ-tubulin (TUBG1) with PCNA was reported in tumor cells [39]. The association of non-tubulin family of proteins (MAPs) such as ChK2, C53, ATR, p53, BRCA1, and Rad51 with γ-tubulin isotypes was previously shown to regulate cell cycle and microtubule nucleation, which together act as a signal transduction hub in their functional network [40,41,42,43].

I found additional new tubulin family of isoforms and several other non-tubular family of proteins. TUBGCP2 (γ-tubulin) exhibited highest number of PPIs in selection steps, and other γ-tubulin family of proteins TUBG1, TUBGCP4, and TUBGCP6 was found in the final set of GeneMANIA as well as in high-level functional enrichment analysis in ShinyGO, possibly suggesting a significant functional role of γ-tubulins in microtubular processes as well as in the formation of γ-tubulin ring complex formation as described in literatures [5, 33, 34].

In initial round of selection process with 6TI together, I found TUBA1B as another top 7 tubulin family of protein interacting strongly with non-tubulin family of proteins; TUBA1B was also found strongly interacting with β-tubulin isotypes (TUBB and TUBB3) in Stringbase and GeneMANIA databases. TUBA1B was earlier found essential in the processes of cell cycle, spliceosome, and DNA replication [44, 45]. In the second round in different databases, TUBA1B strongly interacted with more number of β-tubulin isotypes compared to other isotypes possibly indicating its combined functional involvement with a few additional β-tubulin isotypes in MT dynamics.

The association of tubulin isotypes with non-tubulin proteins was largely dependent on various signaling pathways and posttranslational modifications of tubulins such as acetylation, phosphorylation, tyrosination, polyglutamylation, and methylation [27, 37, 46, 47]. The acetylation and phosphorylation of β-tubulin in reducing microtubule assembly were previously reported, and enzymes like minikinase/DYRK1a or cyclin-dependent kinase Cdk1 were involved in such processes [48]. Tyrosination was shown to regulate the recruitment of Clip-170 (a microtubule plus-end-tracking protein) to microtubule tips, and in addition, it was reported to recruit the motor protein dynein through its regulator dynactin which interacted directly with the α-tubulin tail [49,50,51]. SRC kinase-mediated tyrosine phosphorylation of TUBB3 was demonstrated to regulate mitotic spindle dynamics in prostate cancers [52]. In cancer stem cells, GLUT1, GRP78, VDAC, and Ephrins interacted with β-tubulin isotypes (e.g., βIVb) in maintaining cancer stem cell niches [53].

The uncontrollable growth in MT was earlier demonstrated with mutant TUBA1A, which exhibited weaker interaction with MAP protein such as XMAP215 [54]. In neural stem cell differentiation, the physical interaction of β3-tubulin (TUBB3) with dihydropyrimidinase-like 2 DPYSL2 (a family of collapsin response mediator) and Numb (neuronal protein) was demonstrated using GeneMANIA [55]. Disruption of α-tubulin 4a polyglutamylation prevents aggregation of hyper-phosphorylated tau (MAP protein) and microglia activation in mice [56].

In initial non-tubular protein interactions, I found PKM2, CLIC1, GAPDH, RPL4, PCB1, and PRDX1 in top six PPIs. Tumor-specific pyruvate kinase (PKM2) and chloride intracellular channel protein 1 (CLIC1) were earlier shown to involve in the process of cytokinesis and cell cycle progression, respectively [57, 58]. GAPDH was found as a known gene commonly used as housekeeping gene [59]. RPL4, PCB1, and PRDX1 were reported to involve in protein synthesis, maintenance of nucleolar structure, and antioxidant activities [60,61,62]. Based on our PPI results, I predicted the strong involvement of these non-tubular proteins functioning in association with our selected 6TI in MT dynamics.

Using ShinyGO, I was able to group the functional association of non-tubular proteins like HTT, DPYSL2, SKI, UNC5C, NINL, and DDX41 with tubulin isoforms. In GeneMANIA functional annotation studies, we found the involvement of all tubulin interacting proteins in nucleotide-binding, structural constituent of cytoskeleton, microtubule nucleation, microtubule polymerization or depolymerization, etc. I identified 13 strongly interacting proteins of tubulin based on highest node-to-node interaction score or weight of the interacting edges in PPI interactions. In order to refine and identify strongly interacting tubulin proteins, I repeated tubulin PPI search two times with all three web-based tools and finally with GeneMANIA for functional enrichment analysis. The high-level gene functional enrichment analysis using ShinyGO strongly suggested the significant functional involvement of 13 selected proteins in cell cycle process, microtubule assembly or disassembly, cellular component, and cellular localization. Based on grouping of top-level functional protein interactions, I found isoforms of γ-tubulins playing a major functional role which may be targeted as possible drug target. I found HTT (Huntington disease-causing gene) and UNC5C (netrin receptor) as top-level non-tubulin family of 6TI protein interactors; both were previously reported to involve in neurodevelopment by interacting with β-tubulin and tau proteins [30, 31, 63]. DPYSL2 (dihydropyrimidinase-like 2) and SKI (sphingosine kinase) were found essential in the assembly and stabilization of MT in various cell types and in cancers [55, 64]. NINL was earlier shown to control γ-tubules in stimulating MT nucleation [65], and DDX41 was shown to regulate RNA secondary structures [66].

In our combined output results from three different GO database, I found several additional tubulin isotypes and six additional non-tubular proteins HTT, DPYSL2, SKI, UNC5C, NINL, AND DDX4 repeatedly in PPIs. They were selected for functional enrichment analysis in GeneMANIA and ShinyGO, since these two databases were able to significantly group the functional genes in user-friendly downloadable format. Taking advantage of these GO database, I was able to shortlist the top-level tubulin functional partners based on their aggregated functional roles, significance of FDR values, and significant P-values, correlating significant pathways and number of folds in functional enrichments. In our studies, I was able to narrow down the top-level tubulin protein interactors from several thousands of PPIs results observed in databases. Our approach of identifying top-level tubulin family of proteins and their associated non-tubulin family of functional partners yielded several insights in prioritizing tubulins as drug targets.

Tubulin has gained attention as a fundamental target in anticancer therapeutic approaches since they play an essential role in cell division [27, 34, 44, 47, 48], and several microtubule-targeting agents (MTA) have been employed as anticancer drugs [67, 68]. In cancer cells, the interaction of MT with motor proteins like dynein, kinesin, and myosin was reported as crucial protein mediators in cell proliferation and invasion [69]. Therefore, the identification of MT signaling pathways through PPI studies may offer a source of novel anticancer treatments through identification of essential genes having more number of PPIs in network as HUB genes with positive feedback function [70, 71]. A few studies constructed a PPI network using cBioPortal, STRING, and KEGG pathway analysis using 50 frequently altered MAP genes and highlighted their potential application in cancer treatment and prognosis [72, 73].


This research identified the top-level tubulin family of proteins and its functional partners based on their multiple functional involvements. To improve the data coverage, I used three different user-friendly web-based GO databases and refined the selection process of tubulins by repeating PPI analysis three times. Gene Ontology and protein network-based interactive analysis are increasingly gaining importance in elucidating the functions of novel and druggable genes. Considering the complexity in various GO data resources, developing a simplified computational, machine learning, and genetic algorithms approaches will be highly beneficial for understanding disease gene relationship and therapeutic purposes.

Availability of data and materials

All data analyzed during this study are included in this article.



Protein-protein interactions


Gene Ontology


Microtubule-associated proteins


Guanine triphosphate


Tubulin in voltage-dependent anion channel


Six tubulin isoforms


Area under the precision recall curve


False discovery rate


  1. Binarová P, Tuszynski J (2019) Tubulin: structure, functions and roles in disease. Cells 8(10):1294.

    Article  Google Scholar 

  2. Fourel G, Boscheron C (2020) Tubulin mutations in neurodevelopmental disorders as a tool to decipher microtubule function. FEBS Lett 594(21):3409–3438.

    Article  Google Scholar 

  3. Nsamba ET, Gupta ML (2022) Tubulin isotypes - functional insights from model organisms. J Cell Sci 135(9):jcs259539.

    Article  Google Scholar 

  4. Hussey SP, Fritz-Laylin LK (2019) “The missing link”: the tubulin mutation database connects over 1500 missense mutations with phenotypes across eukaryotes. Cytoskeleton (Hoboken, N.J.) 76(2):175–176.

    Article  Google Scholar 

  5. Vemu A, Atherton J, Spector JO, Moores CA, Roll-Mecak A (2017) Tubulin isoform composition tunes microtubule dynamics. Mol Biol Cell 28(25):3564–3572.

    Article  Google Scholar 

  6. Chaaban S, Brouhard GJ (2017) A microtubule bestiary: structural diversity in tubulin polymers. Mol Biol Cell 28(22):2924–2931.

    Article  Google Scholar 

  7. Verdier-Pinard P, Pasquier E, Xiao H, Burd B, Villard C, Lafitte D, Miller LM, Angeletti RH, Horwitz SB, Braguer D (2009) Tubulin proteomics: towards breaking the code. Anal Biochem 384:197–206

    Article  Google Scholar 

  8. Sullivan KF, Cleveland DW (1986) Identification of conserved isotype-defining variable region sequences for 4 vertebrate β-tubulin polypeptide classes. Proc Natl Acad Sci USA 83:4327–4331. [CrossRef] [PubMed]

    Article  Google Scholar 

  9. Luduena RF (1993) Are tubulin isotypes functionally significant. Mol Biol Cell 4:445–457

    Article  Google Scholar 

  10. Wang J, Miller DD, Li W (2022) Molecular interactions at the colchicine binding site in tubulin: an X-ray crystallography perspective. Drug Discov Today 27(3):759–776.

    Article  Google Scholar 

  11. Florian S, Mitchison TJ (2016) Anti-microtubule drugs. Methods Mol Biol (Clifton, N.J.) 1413:403–421.

    Article  Google Scholar 

  12. Puurand M, Tepp K, Timohhina N, Aid J, Shevchuk I, Chekulayev V, Kaambre T (2019) Tubulin βII and βIII isoforms as the regulators of VDAC channel permeability in health and disease. Cells 8(3):239.

    Article  Google Scholar 

  13. Shi J, Mitchison TJ (2017) Cell death response to anti-mitotic drug treatment in cell culture, mouse tumor model and the clinic. Endocr Relat Cancer 24(9):T83–T96.

    Article  Google Scholar 

  14. Shankaraiah N, Nekkanti S, Brahma UR, Praveen Kumar N, Deshpande N, Prasanna D, Senwar KR, Jaya Lakshmi U (2017) Synthesis of different heterocycles-linked chalcone conjugates as cytotoxic agents and tubulin polymerization inhibitors. Bioorg Med Chem 25(17):4805–4816.

    Article  Google Scholar 

  15. Zhou Y, Yang S, Mao T, Zhang Z (2015) MAPanalyzer: a novel online tool for analyzing microtubule-associated proteins. Database 2015:bav108.

    Article  Google Scholar 

  16. Hong Y, Zhu YY, He Q, Gu SX (2021) Indole derivatives as tubulin polymerization inhibitors for the development of promising anticancer agents. Bioorg Med Chem 55:116597. Advance online publication

    Article  Google Scholar 

  17. Emens LA, Molinero L, Loi S, Rugo HS, Schneeweiss A, Diéras V, Iwata H, Barrios CH, Nechaeva M, Nguyen-Duc A, Chui SY, Husain A, Winer EP, Adams S, Schmid P (2021) Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer: biomarker evaluation of the IMpassion130 study. J Natl Cancer Inst 113(8):1005–1016.

    Article  Google Scholar 

  18. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ, von Mering C (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49(D1):D605–D612.

    Article  Google Scholar 

  19. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607–D613.

    Article  Google Scholar 

  20. Kustatscher Georg, Grabowski Piotr, Schrader Tina A, Passmore Josiah B, Schrader Michael, Rappsilber Juri (2019) Co-regulation map of the human proteome enables identification of protein functions. Nat Biotechnol 37:1361–1371

    Article  Google Scholar 

  21. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38 Suppl:W214-20

    Article  Google Scholar 

  22. Ge SX, Jung D, Yao R (2020) ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics (Oxford, England) 36(8):2628–2629.

    Article  Google Scholar 

  23. Dekker J, Diderich KEM, Schot R et al (2021) A novel family illustrating the mild phenotypic spectrum of TUBB2B variants. Eur J Paediatr Neurol 35:35–39

    Article  Google Scholar 

  24. Buscaglia G, Northington KR, Aiken J, Hoff KJ, Bates EA (2022) Bridging the gap: the importance of TUBA1A α-tubulin in forming midline commissures. Front Cell Dev Biol 9:789438. PMID: 35127710; PMCID: PMC8807549

    Article  Google Scholar 

  25. Snelleksz M, Dean B (2021) Lower levels of tubulin alpha 1b in the frontal pole in schizophrenia supports a role for changed cytoskeletal dynamics in the aetiology of the disorder. Psychiatry Res 303:114096

    Article  Google Scholar 

  26. Watanabe K, Nakashima M, Kumada S, Mashimo H, Enokizono M, Yamada K, Kato M, Saitsu H (2021) Identification of two novel de novo TUBB variants in cases with brain malformations: case reports and literature review. J Hum Genet 66(12):1193–1197

    Article  Google Scholar 

  27. Liu Z, Li S, Dong J, Miao Y (2021) TUBB3 promotes growth and invasion of gallbladder cancer cells by Akt/mTOR signal pathway. J Environ Pathol Toxicol Oncol 40(2):23–33

    Article  Google Scholar 

  28. Shen R, Zhang Z, Zhuang Y, Yang X, Duan L (2021) A novel TUBG1 mutation with neurodevelopmental disorder caused by malformations of cortical development. Biomed Res Int 26(2021):6644274

    Google Scholar 

  29. Mitani T, Punetha J, Akalin I et al (2019) Bi-allelic pathogenic variants in TUBGCP2 cause microcephaly and lissencephaly spectrum disorders. Am J Hum Genet 105(5):1005–1015

    Article  Google Scholar 

  30. Shao Q, Yang T, Huang H, Alarmanazi F, Liu G (2017) Uncoupling of UNC5C with polymerized TUBB3 in microtubules mediates Netrin-1 repulsion. J Neurosci 37(23):5620–5633

    Article  Google Scholar 

  31. Li Q, Wang BL, Sun FR, Li JQ, Cao XP, Tan L (2018) The role of UNC5C in Alzheimer’s disease. Ann Transl Med 6(10):178

    Article  Google Scholar 

  32. Whitman MC, Barry BJ, Robson CD et al (2021) TUBB3 Arg262His causes a recognizable syndrome including CFEOM3, facial palsy, joint contractures, and early-onset peripheral neuropathy. Hum Genet 140(12):1709–1731

    Article  Google Scholar 

  33. Scheidecker S, Etard C, Haren L et al (2015) Mutations in TUBGCP4 alter microtubule organization via the γ-tubulin ring complex in autosomal-recessive microcephaly with chorioretinopathy. Am J Hum Genet 96(4):666–674

    Article  Google Scholar 

  34. Wang Y, Wang H, Li C, Zhang J et al (2022) CircTUBGCP3 contributes to the malignant progression of rectal cancer. Dig Dis Sci 67(7):2957–2970

    Article  Google Scholar 

  35. Yao Z, Zeng J, Zhu H et al (2022) Mutation analysis of the TUBB8 gene in primary infertile women with oocyte maturation arrest. J Ovarian Res 15(1):38

    Article  Google Scholar 

  36. Gui S, Chen P, Liu Y et al (2021) TUBA1C expression promotes proliferation by regulating the cell cycle and indicates poor prognosis in glioma. Biochem Biophys Res Commun 5(577):130–138

    Article  Google Scholar 

  37. Alvarado-Kristensson M (2018) γ-Tubulin as a signal-transducing molecule and meshwork with therapeutic potential. Sig Transduct Target Ther 3:24

    Article  Google Scholar 

  38. Böhler A, Vermeulen BJA, Würtz M et al (2021) The gamma-tubulin ring complex: deciphering the molecular organization and assembly mechanism of a major vertebrate microtubule nucleator. Bioessays 43(8):e2100114. Epub 2021 Jun 23. PMID: 34160844

    Article  Google Scholar 

  39. Corvaisier M, Zhou J, Malycheva D, Cornella N, Chioureas D, Gustafsson NMS, Rosselló CA, Ayora S, Li T, Ekström-Holka K, Jirström K, Lindström L, Alvarado-Kristensson M (2021) The γ-tubulin meshwork assists in the recruitment of PCNA to chromatin in mammalian cells. Commun Biol 4(1):767

    Article  Google Scholar 

  40. Lesca C, Germanier M, Raynaud-Messina B, Pichereaux C, Etievant C, Emond S, Burlet-Schiltz O, Monsarrat B, Wright M, Defais M (2005) DNA damage induce gamma-tubulin-RAD51 nuclear complexes in mammalian cells. Oncogene 24(33):5165–5172.

    Article  Google Scholar 

  41. Lindström L, Li T, Malycheva D et al (2018) The GTPase domain of gamma-tubulin is required for normal mitochondrial function and spatial organization. Commun Biol 1:37

    Article  Google Scholar 

  42. Hubert T, Vandekerckhove J, Gettemans J (2011) Cdk1 and BRCA1 target γ-tubulin to microtubule domains. Biochem Biophys Res Commun 414(1):240–245.

    Article  Google Scholar 

  43. Morris VB, Brammall J, Noble J, Reddel R (2000) p53 localizes to the centrosomes and spindles of mitotic cells in the embryonic chick epiblast, human cell lines, and a human primary culture: an immunofluorescence study. Exp Cell Res 256(1):122–130

    Article  Google Scholar 

  44. Hu X, Zhu H, Chen B et al (2022) Tubulin Alpha 1b Is Associated with the immune cell infiltration and the response of HCC patients to immunotherapy. Diagnostics (Basel) 12(4):858.

    Article  Google Scholar 

  45. Lu C, Zhang J, He S, Wan C et al (2013) Increased α-tubulin1b expression indicates poor prognosis and resistance to chemotherapy in hepatocellular carcinoma. Dig Dis Sci 58(9):2713–2720.

    Article  Google Scholar 

  46. Etienne-Manneville S (2010) From signaling pathways to microtubule dynamics: the key players. Curr Opin Cell Biol 22(1):104–111.

    Article  Google Scholar 

  47. Jiang X, Shao W, Chai Y, Huang J, Mohamed MAA, Ökten Z, Li W, Zhu Z, Ou G (2022) DYF-5/MAK-dependent phosphorylation promotes ciliary tubulin unloading. Proc Natl Acad Sci U S A 119(34):e2207134119

    Article  Google Scholar 

  48. Ori-McKenney KM, McKenney RJ, Huang HH, Li T, Meltzer S, Jan LY, Vale RD, Wiita AP, Jan YN (2016) Phosphorylation of β-tubulin by the down syndrome kinase, minibrain/DYRK1a, regulates microtubule dynamics and dendrite morphogenesis. Neuron 90(3):551–563.

    Article  Google Scholar 

  49. Trisciuoglio D, Degrassi F (2021) The tubulin code and tubulin-modifying enzymes in autophagy and cancer. Cancers (Basel) 14(1):6.

    Article  Google Scholar 

  50. Nirschl JJ, Magiera MM, Lazarus JE, Janke C, Holzbaur EL (2016) α-Tubulin tyrosination and CLIP-170 phosphorylation regulate the initiation of dynein-driven transport in neurons. Cell Rep 14(11):2637–2652.

    Article  Google Scholar 

  51. McKenney RJ, Huynh W, Vale RD, Sirajuddin M (2016) Tyrosination of α-tubulin controls the initiation of processive dynein-dynactin motility. EMBO J 35(11):1175–1185.

    Article  Google Scholar 

  52. Alfano A, Xu J, Yang X, Deshmukh D, Qiu Y (2022) SRC kinase-mediated tyrosine phosphorylation of TUBB3 regulates its stability and mitotic spindle dynamics in prostate cancer cells. Pharmaceutics 14(5):932

    Article  Google Scholar 

  53. Maliekal TT, Dharmapal D, Sengupta S (2022) Tubulin isotypes: emerging roles in defining cancer stem cell niche. Front Immunol 13:876278.

    Article  Google Scholar 

  54. Hoff KJ, Aiken JE, Gutierrez MA, Franco SJ, Moore JK (2022) TUBA1A tubulinopathy mutants disrupt neuron morphogenesis and override XMAP215/Stu2 regulation of microtubule dynamics. Elife 5(11):e76189.

    Article  Google Scholar 

  55. Xiong LL, Qiu DL, Xiu GH, Al-Hawwas M, Jiang Y, Wang YC, Hu Y, Chen L, Xia QJ, Wang TH (2020) DPYSL2 is a novel regulator for neural stem cell differentiation in rats: revealed by Panax notoginseng saponin administration. Stem Cell Res Ther 11(1):155.

    Article  Google Scholar 

  56. Jiménez JS (2022) Macromolecular structures and proteins interacting with the microtubule associated Tau protein. Neuroscience S0306–4522(22):00263–00269.

    Article  Google Scholar 

  57. Jiang Y, Li X, Yang W, Hawke DH, Zheng Y, Xia Y, Aldape K, Wei C, Guo F, Chen Y, Lu Z (2014) PKM2 regulates chromosome segregation and mitosis progression of tumor cells. Mol Cell 53(1):75–87

    Article  Google Scholar 

  58. Uretmen Kagiali ZC, Saner N, Akdag M, Sanal E, Degirmenci BS, Mollaoglu G, Ozlu N (2019) CLIC4 and CLIC1 bridge plasma membrane and cortical actin network for a successful cytokinesis. Life Sci Alliance 3(2):e201900558

    Article  Google Scholar 

  59. Ferguson RE, Carroll HP, Harris A, Maher ER, Selby PJ, Banks RE (2005) Housekeeping proteins: a preliminary study illustrating some limitations as useful references in protein expression studies. Proteomics 5(2):566–571

    Article  Google Scholar 

  60. Okuwaki M, Saito S, Hirawake-Mogi H, Nagata K (2021) The interaction between nucleophosmin/NPM1 and the large ribosomal subunit precursors contribute to maintaining the nucleolar structure. Biochim Biophys Acta Mol Cell Res 1868(1):118879.

    Article  Google Scholar 

  61. Zhang X, Gao X, Coots RA, Conn CS, Liu B, Qian SB (2015) Translational control of the cytosolic stress response by mitochondrial ribosomal protein L18. Nat Struct Mol Biol 22(5):404–410.

    Article  Google Scholar 

  62. Espinoza-Lewis RA, Yang Q, Liu J, Huang ZP, Hu X, Chen D, Wang DZ (2017) Poly(C)-binding protein 1 (Pcbp1) regulates skeletal muscle differentiation by modulating microRNA processing in myoblasts. J Biol Chem 292(23):9540–9550

    Article  Google Scholar 

  63. Taran AS, Shuvalova LD, Lagarkova MA, Alieva IB (2020) Huntington’s disease-an outlook on the interplay of the HTT protein, microtubules and actin cytoskeletal components. Cells 9(6):1514.

    Article  Google Scholar 

  64. Hengst JA, Hegde S, Paulson RF, Yun JK (2020) Development of SKI-349, a dual-targeted inhibitor of sphingosine kinase and microtubule polymerization. Bioorg Med Chem Lett 30(20):127453.

    Article  Google Scholar 

  65. Casenghi M, Meraldi P, Weinhart U, Duncan PI, Körner R, Nigg EA (2003) Polo-like kinase 1 regulates Nlp, a centrosome protein involved in microtubule nucleation. Dev Cell 5(1):113–125.

    Article  Google Scholar 

  66. Zhang L, Li X (2021) DEAD-box RNA helicases in cell cycle control and clinical therapy. Cells 10:1540

    Article  Google Scholar 

  67. Cao YN, Zheng LL, Wang D, Liang XX, Gao F, Zhou XL (2018) Recent advances in microtubule-stabilizing agents. Eur J Med Chem 1(143):806–828.

    Article  Google Scholar 

  68. Borys F, Joachimiak E, Krawczyk H, Fabczak H (2020) Intrinsic and extrinsic factors affecting microtubule dynamics in normal and cancer cells. Molecules 25(16):3705.

    Article  Google Scholar 

  69. Khwaja S, Kumar K, Das R, Negi AS (2021) Microtubule associated proteins as targets for anticancer drug development. Bioorg Chem 116:105320.

    Article  Google Scholar 

  70. Seo CH, Kim JR, Kim MS, Cho KH (2009) Hub genes with positive feedbacks function as master switches in developmental gene regulatory networks. Bioinformatics 25(15):1898–1904

    Article  Google Scholar 

  71. Mabonga L, Kappo AP (2019) Protein-protein interaction modulators: advances, successes and remaining challenges. Biophys Rev 11(4):559–581.

    Article  Google Scholar 

  72. Gutiérrez-Escobar AJ, Méndez-Callejas G (2017) Interactome analysis of microtubule-targeting agents reveals cytotoxicity bases in normal cells. Genomics Proteomics Bioinformatics 15(6):352–360.

    Article  Google Scholar 

  73. Jiang L, Zhu X, Yang H, Chen T, Lv K (2020) Bioinformatics analysis discovers microtubular tubulin beta 6 class V (TUBB6) as a potential therapeutic target in glioblastoma. Front Genet 11:566579.

    Article  Google Scholar 

Download references


The author thanks the encouragement and support received from the management Bharath Institute of Higher Education and Research, Chennai, India. The author thanks Prof. Dr. A. Kumaravel, BIHER, Chennai, India, for critical reading and making corrections to the manuscript.


This research did not receive any specific grant from funding agencies in the public or commercial sectors.

Author information

Authors and Affiliations



The author participated in the conception and design of the study. The author solely contributed to data analysis and manuscript preparation. The author read and approved the final manuscript.

Corresponding author

Correspondence to Polani B. Ramesh Babu.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The author declares no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramesh Babu, P.B. Prediction of anti-microtubular target proteins of tubulins and their interacting proteins using Gene Ontology tools. J Genet Eng Biotechnol 21, 78 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: