Protein-Protein Docking and Structural Prediction of KMT2C Variant from Cervical Cancer Whole Exome Sequencing Data

 

Santosh Kumari Duppala¹, Smita C. Pawar², Ashish Vyas³, Sugunakar Vure⁴

¹Department of Microbiology, School of Bioengineering and Biosciences,

Jalandhar, Lovely Professional University, Punjab, India.

²Department of Genetics, Osmania University, Hyderabad, Telangana, India.

³Department of Microbiology, School of Bioengineering and Biosciences,

Jalandhar, Lovely Professional University, Punjab, India.

⁴MNR Foundation for Research and Innovation, MNR Medical College and Hospital,

MNR University, Fasalwadi Village, Sangareddy (District), Telangana, India-502294.

 

ABSTRACT:

Cervical cancer is one of the most frequent cancers among women and the fourth leading cancer for mortality worldwide, and it is caused by persistent infections of Human papillomavirus (HPV). Most of the death cases are reported in developing countries like Africa and Southeast Asia. As the incidence and mortality rates increase globally, women with advanced and recurrent cancers are showing less response towards chemoradiotherapy. Hence, molecular therapies and targets show promising results. In our study, we have performed whole exome sequencing of 10 samples in a cohort and after analyzing received top mutated genes/ variants and one of the top variants in this study we focused on KMT2C rs ID 138908625 exon 8 regions Chr 7:152265083 variation C>A, T, protein structure prediction, c score, and TM value evaluated for wild type, Query 1 and Query for top 5 models of KMT2C by I TASSER.  The predicted values of the models of KMT2C Query 2 show structural similarity and functional analog when compared to Query 1 with wild-type KMT2C. Further, protein-protein docking studies were performed using Cluspro 2.0 with the compounds of Arteminisin, Shikonin, Sitoinoside IX, Bucidarasin A, and Betulin with KMT2C. The Betulin shows better binding energy (-12.5 Kcal/mol) and followed by Bucidarasin (-12.3Kcal/mol) with KMT2C. The present study is the combination of insilico work with the whole exome sequencing variants, that can be used in the prognosis and diagnosis of cervical cancer. The docking studies predict the molecular binding affinities of the ligand and the protein fold conformations.

 

 

 

INTRODUCTION: 

Cervical cancer (CC) ranks fourth globally in cancer incidence and second among women in mortality¹. Every 8 seconds, a woman dies from CC worldwide, with India reporting a death every 8 minutes^2,3,4. HPV infections cause about 90% of CC cases, with other risk factors including genetics, smoking, and HIV^5,6,7.

 

HPV proteins E6 and E7 disrupt tumor suppressor genes, leading to uncontrolled cell growth. Early detection through pap smear tests is crucial^10,11,12. However, mortality rates are high, particularly in lower-middle-class and developing countries, underscoring the need for novel therapies^13,14,15. Adenocarcinoma of the cervix, accounting for 18 to 20 percent of cases, is more aggressive than squamous cell carcinoma¹⁶. Epigenetic dysregulation is significant in cancer development, prompting the search for new diagnostic and treatment approaches¹⁷. Current treatments include surgery, radiotherapy, and chemotherapy, but advanced cases require more effective interventions¹⁸. Next-generation sequencing (NGS) technologies are pivotal in targeted sequencing therapies. In our study, whole exome sequencing revealed novel mutations, including SNV variations (missense, synonymous, frameshift) contributing to cervical cancer. Among the top mutated genes is KMT2C, a tumor suppressor gene encoding a lysine-specific methyl transferase. KMT2 proteins (KMT2A-D) activate transcription by methylating histone 3 lysine 4 (H3K4), facilitating chromatin opening. The correlation between KMT2C and immune therapy is unclear, though studies suggest its association with immune cell infiltration. KMT2C expression varies across tissues, with mutations showing different patterns across cancers. KMT2C mutations are associated with enhanced response to immune checkpoint inhibitors, with higher TMB, PD-L1 expression, and MSI-H observed. Our study aimed to analyze KMT2C protein structure and variant binding affinities using I-TASSER, a predictive tool for structural changes and 3D model analysis. Docking studies with potential inhibitors for cervical cancer were also conducted.

 

A key challenge in cervical cancer research is determining the 3D protein structure of potential therapeutic targets, crucial for drug screening¹⁹. This understanding is vital for developing medications targeting specific proteins. However, traditional drug development methods are time-consuming, expensive, and ethically challenging²⁰. In silico investigations, like our study using I-TASSER and docking studies, are increasingly important for advancing cancer research and developing innovative medications. Through this approach, we identified new compounds for potential screening as novel treatments for KMT2C in cervical cancer.

 

Materials and Methods:

Cervical cancer tissue samples were collected from MNJ Government Cancer Hospital in Hyderabad and Homi Bhabha Hospital in Vishakhapatnam, India, confirmed through histological examination. Control samples were obtained from women undergoing hysterectomy. Ten tissue samples underwent whole exome sequencing, with KMT2C identified as one of the top mutated genes, showing a variation in exon 8 (Chr 7:152265083; rs ID 138908625).

 

I-TASSER:

I-TASSER predicts protein structure and function using the Iterative Threading ASSEmbly Refinement technique, excelling in predicting 3D conformational changes. We submitted wild type and mutation sequences of exon 8 region rs ID 13908625 protein sequences to I-TASSER. The wild-type sequence underwent mutations, with Arginine changing to Histidine in Query 1 and to Leucine in Query 2.

 

Original sequence of KMT2C exon 8 regions (wild type) submitted to I TASSER:

>protein
KEDANCAVCDSPGDLLDQFFCTTCGQHYHGMCLDIAVTPLKRAGWQCPECKVCQNC

 

Protein-Protein Interactions:

Using String software Protein-Protein Interactions of KMT2C gene was performed and their network interactions were illustrated.

 

KMT2C PDB structure:

The structure of KMT2C/ MLL3 from the protein data bank, retrieved the image for docking studies. KMT2C gene having 60 exons and spans more than 216 kb. It is headed in the 5-prime region by a 1.8-kb CpG island. KMT2C acts as a ligand and receptors from the literature of cervical cancer chosen compounds such as Artemisinim, Shikonin, Sitoinoside IX Bucidarasin A, Betulin.

 

Protein-Protein Docking:

Cluspro 2.0 is used for docking as a protein-protein study. The compounds used for docking were Artrmisinin, Shikonin, Sitoindoside IX, Bucidarasin A, and Betulin taken from the literature. These compounds are used in the treatment of several cancers and also in cervical cancer.

 

 

Figure 1: The 5F59 PDB structure of KMT2C

 

Figure 2: PDB structures of chemical compounds for Protein- Protein Docking

 

RESULTS:

Protein structures of KMT2C exon 8 show missense and frameshift mutations. I-TASSER provided results for the wild-type sequence and two mutated queries, each with multiple predicted models. While valuable, I-TASSER's predictions are subject to computational limitations.I-TASSER utilizes LOMETS to construct 3D models by aligning threads. Trustworthiness is assessed by template modeling (TM)-scores, indicating structural similarity²¹.

 

Predicted Secondary structures of wild type KMT2C, Query 1 and Query 2 KMT2C:

If the configuration score is more than 5, the secondary structure prediction is strong and confident. Here we got a higher score prediction of the secondary structure of three homology models.

 

 

Figure 3 A, B, C: Predicted secondary structures of KMT2C Models and their configuration score

 

 

Figure A: Predicted normalized B- factor of KMT2C (wild)

 

Figure B : Predicted normalized B- factor of KMT2C(Query 1)

 

 

Figure C: Predicted normalized B- factor of KMT2C (Query 2)

Figure 4 A, B, C: Predicted Normalized B factor graphs for KMT2C wild, Query 1 and Query 2

 

The below given Tables show 1: The PDB hits of KMT2C wild type, Query 1 and Query 2 sequences coverage, Table 2: The top ten PDB structures showing the close similarity of wild type KMT2C, Table 3: The GO ontology and consensus prediction of wild KMT2C with molecular function and GO score of 0.87, Table 4: Gene ontology template structure similarity with the query protein and template

 

Table 1: The PDB hits of KMT2C wild, Query 1 and Query 2 sequences coverage

 

Table 2: The 10 PDB structures that were showing close structural similarity

 

Table 3: GO Ontology consensus Predictions of Query

 

 

Table 4: Gene Ontology template structures with the query protein and template

 

Query -1 sequence of KMT2C  G> T (R to L) submitted to I TASSER, the below protein  sequence

>protein KEDANCAVCDSPGDLLDQFFCTTCGQHYHGMCLDIAVTPLKLAGWQCPECKVCQNC

 

Table 5: Top10 identified structural analogs in PDB

 

Table 6: Top 10 ontology templates and predicted PDB hits

 

Table 7:  GO Predictions and score of Query 1

Molecular function	GO:0008270	GO:0005515
GO score	0.86	0.86
Biological process	GO:0035556
GO Score	0.33
Cellular Component	None was Predicted

 

Table 5: Top 10 PDB hits and identified structural analogs of Query 1, Table 6: Top 10 Gene ontology templates and predicted PDB hits, Table 7: Gene Ontology prediction and molecular and biological function of GO score 0.86 and 0.33 of Query 1

 

Query 2 - KMT2C (Query 2) G to A (R to H)

>protein KEDANCAVCDSPGDLLDQFFCTTCGQHYHGMCLDIAVTPLKHAGWQCPECKVCQN

 

Table 8: Top 10 gene ontology and homology structures and c score and TM score and RMSD of Query 2

Rank	CscoreGO	TM-score	RMSDa	IDENa	Cov	PDB Hit
1	0.35	0.8425	1.36	0.25	1.00	2e6rA
2	0.34	0.7991	1.53	0.23	1.00	2kwjA
3	0.33	0.7312	2.20	0.28	0.95	2e6sA
4	0.32	0.7373	2.55	0.23	1.00	1wevA
5	0.31	0.7465	1.85	0.30	0.95	2puyB
6	0.31	0.7111	2.07	0.23	0.91	3o36A
7	0.30	0.7081	2.39	0.22	0.98	2yt5A
8	0.30	0.7204	1.96	0.24	0.95	1xwhA
9	0.30	0.6079	2.45	0.33	0.86	1f62A
10	0.29	0.8373	1.27	0.26	0.98	2ysmA

 

Table 9: Gene ontology and scores of molecular and biological scores of Query 2 are 0.86 and 0.32

 

Table 10: Top 5 model c scores, TM scores and RMSD and cluster density of 0.5228 values of wild type KMT2C

Name	C-score	Exp.TM-Score	Exp. RMSD	No. of decoys	Cluster density
KMT2C
Model1:	0.22	0.74+-0.11	2.4+-1.8	9865	0.5228
Model2:	-0.4		4916	0.2786
Model3:	-0.56		3892	0.2385
Model4:	-1.59		1610	0.0853
Model5:	-1.39		1716	0.1038

 

Table 11: Top 5 models of Query 1 KMT2C (R to H) showing cluster density of 0.4907, c score and TM score

Name	C-score	Exp.TM-Score	Exp. RMSD	No. of decoys	Cluster density
KMT2C
G>T
Model1:	0.15	0.73+-0.11	2.6+-1.9	9811	0.4907
Model2:	-0.62		4472	0.2272
Model3:	-0.54		4357	0.2455
Model4:	-0.03		6792	0.4085
Model5:	-1.74		1205	0.0738

 

Table 12: Top models of query 2 showing cluster density value 0.5267, TM value, c score and RMSD values

Name	C-score	Exp.TM-Score	Exp. RMSD	No. of decoys	Cluster density
KMT2C
Model1:	0.22	0.74+-0.11	2.4+-1.8	9992	0.5267
Model2:	-0.2	5719	0.344
Model3:	-1.35	2081	0.1091

 

Results of KMT2C wild type and mutated types:

These are the three KMT2C structures of wild type and mutation sequences and their Tm score binding affinities and C value scores results.It is calculated on the basis of template alignment and structure parameters. Expected Tm score and RMSD are measured based on their structure similarity between two structures it is used to evaluate the accuracy of the structural model with the native structure.  Decoys are low-temperature structures generated during monte Carlo simulations. Higher Cluster density is to find the higher simulations with good quality (Table 13).

 

Table 13: Wild type and variations of query 1 and query 2 predictions of the c score, Tm score and cluster density

Name	C-score	Exp.TM-Score	Exp. RMSD	No. of decoys	Cluster density
KMT2C
Model1:	0.22	0.74+-0.11	2.4+-1.8	9865	0.5228
Model2:	-0.4		4916	0.2786
Model3:	-0.56		3892	0.2385
Model4:	-1.59		1610	0.0853
Model5:	-1.39		1716	0.1038

 

Therefore, from our studies we observe KMT2C projects higher similarity with KMT2C (Query 2) followed by Query11

 

 

Figure A: Wild type predicted image among 5 models. The c-score value is 0.22 and showing cluster range 0.5228

 

 

Figure B: Query 1 shows the sight variation from wild type as the c score value of the best confidence level shows 0.15, Tm value 0.73+_ 0.11 and cluster density with 0.490

 

 

Figure C: Query 2 is the best predicted image among 5 models that we have received from I Tasser results. The c score 0.22, TM score 0.74+_0.11 and showing cluster density of 0.5267.

Figures 5 A, B, C: The predicted structures of KMT2C wild type, Query 1 and Query 2 showing their best predicted homology modelling

 

 

 

Protein – Protein Interactions of KMT2C

KMT2C interacts with the following lysine methyltransferase group genes and also other genes such as KMT2D, ASHL2, SETD1A, RBBP5, KDM6A, PAXP1, PAGR1, NCOA6, DPY30, WDR5. The network node represents post-translational modifications from the gene and all the nodes represent from a single gene (Figure 6).

 

KMT2C docking using Cluspro 2.0 software:

Protein- Protein Docking of KMT2C binding with the compounds that show activity on cervical cancer such as Artrmisinim, Shikonin, Sitoindoside IX, Bucidarasin A, Betulin. The binding energies of Betulin shows good activity with KMT2C with -12.5 Kcal/mol then further Bucidarasin A with -12.3 Kcal/Mol and followed by – 11.5 Kcal/mol in Sitoindoside IX (Table 14). The best docking structures of KMT2C with Betulin and Bucidarasin A are as shown in the (Figures:7 and 8)

 

 

Figure 6: Protein-Protein Interactions of KMT2C and their interacted network of genes

 

 

Figure 7: Protein-Protein (P-P) docking and binding affinity of KMT2C with Betulin is -12

 

Figure 8: P- P docking and binding affinity of KMT2C with Bucidarasin A shows -12,3

 

Table 14: Protein - Protein Docking of KMT2C with few compounds useful in the treatment in Cervical cancer

Compound name	Binding energy (Kcal/ mol)
Artemisinim	-9.9
Shikonin	-10.6
Sitoindoside IX	-11.5
Bucidarasin A	-12.3
Betulin	-12.5

 

DISCUSSION:

KMT2C/MLL3 is a complex involved in various cancers and developmental disorders, with mutations affecting genome integrity and tumorigenesis²². We analyzed the best predicted structures of the original KMT2C sequence and its two variations with the highest energy and confidence levels. SNPs play a crucial role in disease phenotypes, altering protein binding and secondary structure²³. Comparison, of wild-type and mutated sequences revealed differences in nucleotide base pairs and protein structures due to mutation. In the first query, a missense mutation changed Arginine to Histidine, impacting cellular processes and cancer metabolism. The mutation might contribute to cervical cancer development²⁴. I-TASSER software was used for structural analysis^25,26. In the second query, a frameshift mutation altered Arginine to Leucine, indicating potent mutations on chromosome 7. Predicted secondary structures showed good configurations, with cluster size being a more reliable metric than the C-score. The C-score, however, correlates with model quality, aiding in quantitative estimations of RMSD and TM-score. While lower-ranking models may occasionally have better C-scores, the first model is generally the most dependable choice [Figure. 6 and 7]. In our analysis of the top predicted models of wild-type KMT2C and its variants (Query 1 and Query 2), only the first models from (Tables 10,11,12), were considered, as the C-score of the first model is predominantly used in I-TASSER. The comparison with wild type revealed that Query 2 exhibited similar C-score and TM value as the wild type, indicating closer resemblance compared to Query 1. The protein models of KMT2C in Table 13 were assessed for wild type and Mutated Query sequences 1 and 2, revealing higher TM score and cluster density for Query 2. Additionally, gene ontology analysis showed similar coverage values ranging from 0 to 1 across wild type, Query 2, and Query 1. I-TASSER, an in-silico software, generates thousands of PDB IDs but outputs only the top ten GO PDB hits. Protein-protein interaction network analysis illustrated the involvement of KMT2C in DNA damage, epigenetic modifications^27,28,29 and apoptosis, with specific interactions highlighted for various proteins. Docking of KMT2C with compounds revealed stronger binding energy with Betulin (-12.5 Kcal/mol) followed by Bucidarisin A (-12.3 Kcal/mol), suggesting potential therapeutic implications for cervical cancer^30,31,32,.

 

CONCLUSION:

KMT2C as a potent novel mutation showing missense and frameshift mutations with high tumor mutation burden in Cervical cancer. In this study I TASSER given the prediction structures and c scores, TM values and cluster density, GO analogs of PDB with accuracy and coverage seen for similarity in the models. All the compounds in the study having antitumor properties and used in the drug management for few biomarkers. The docking of KMT2C with Betulin shows more binding affinity and energy followed by Bucidarasin A. These compounds would be useful in the further treatment in cervical cancer patients. The study insights will be helpful to understand in the molecular mechanisms of cancer with regarding to KMT2C and the compounds would build a future prospective to work for prognosis and clinical management and will be useful in the drug therapeutic management.

 

LIMITATIONS OF THE I TASSER AND COMPUTATIONAL MODELING:

Accuracy varies, especially for proteins with novel folds or lacking homologous templates. It's more effective for smaller proteins (<300 amino acids); larger ones pose challenges. It predicts a single static structure, missing common conformational changes due to interactions. Experimental validation is resource-intensive and may not always be feasible.

 

FUTURE PERSPECTIVES:

Further, experimental validation of the the marker KMT2C with the compounds taken in the study would become another level of understanding and combination of in silico and experimental would provide novel therapeutic targets.

 

ACKNOWLEDGEMENTS:

We are grateful to the Department of Genetics and Biotechnology, Osmania University, Hyderabad and the Department of Biotechnology, School of Bioengineering and Biosciences, Lovely Professional University, Jalandhar Punjab for their support and encouragement.

 

AUTHORS CONTRIBUTION:

Santosh Kumari Duppala is the first author and wrote the whole manuscript, analysis, tables and images. Smita C. Pawar, Ashish Vyas and Sugunakar Vure for idea, conceptualization and done proof reading.

 

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Accepted on 30.04.2024           © RJPT All right reserved

Research J. Pharm. and Tech 2024; 17(5):2301-2308.