Original Article MMP9 in pan-cancer and computational study to screen for MMP9 inhibitors
Xianjie Ai1, Xinyu Wang2, Taotao Ren1, Zhong Li1, Bo Wu1, Ming Li1
1Lower Extremity Division, Orthopedic Trauma Department, Honghui Hospital, Xi’an Jiaotong University, Youyi East Road No. 555, Beilin District, Xi’an, Shaanxi, China; 2Department of Orthopaedic Trauma, Center of Orthopaedics and Traumatology, The First Hospital of Jilin University, Street Xinmin 71, Changchun, Jilin, China
Received May 21, 2024; Accepted September 26, 2024; Epub November 15, 2024; Published November 30, 2024
Abstract: Purpose: The stromal cell protein metalloproteinase 9 (MMP9), associated with extracellular matrix degra- dation and remodeling, promotes tumor invasion and metastasis and regulates cell adhesion molecule and cytokine activity. This study evaluated MMP9 in pan-cancer and screened for compounds and drug candidates that can inhib- it it. Methods: MMP9 expression in pan-cancer tissues was evaluated in a pan-cancer dataset from the University of California Santa Cruz database, along with the correlation between MMP9 and the tumor microenvironment (TME), RNA modification genes, and tumor mutation burden. MMP9 crystal structures were downloaded, and a ligand- based pharmacophore model was constructed. A machine learning model was constructed for further screening. The identified compounds were pooled into Discovery Studio 4.5 for absorption, distribution, metabolism, and excre- tion (ADME) and toxicity prediction. Molecular docking was used to demonstrate the binding affinity and mechanism between the compounds and MMP9, and the stability of the ligand-receptor complex was assessed. Results: The expression levels of MMP9 differed between tumor tissues. Prognostic analysis showed that high MMP9 expression indicates poor survival and tumor progression in glioma (GMBLGG), pan-kidney (KIPAN; KICH+KIRC+KIRP), uveal melanoma (UVM), low-grade glioma (LGG), adrenocortical carcinoma (ACC), and liver hepatocellular carcinoma (LIHC). MMP9 expression in GMBLGG, KIPAN, UVM, LGG, ACC, and LIHC was positively correlated with the TME. The ligand-based pharmacophore model and the machine learning model identified 49 small molecules. ADME and tox- icity prediction identified CEMBL82047 and CEMBL381163 as potential MMP9 inhibitors, showing robust binding affinity with MMP9. The resulting complexes are stable in the natural environment. Conclusion: CHEMBL82047 and CHEMBL381163 are ideal compounds for inhibiting MMP9. The findings of this study will contribute to the design and improvement of MMP9-targeting drugs.
Keywords: MMP9, pan-cancer, ligand-based pharmacophore model, machine learning model, virtual screening, molecular dynamics simulation
Introduction
Tumors are formed when normal cells prolifer- ate and differentiate abnormally under the action of various initiating and promoting fac- tors. Tumors, especially malignant ones, de- stroy normal tissues and organs and can cause gradual organ dysfunction until failure or death due to compression, consumption, or destruc- tion [1]. Malignant cancer is one of the leading causes of death worldwide [2], with an extreme- ly low cure rate in developed and developing countries [3, 4]. Although great progress has been made in cancer therapy, many patients
still have poor prognoses and low survival rates. Thus, novel therapeutic methods and drugs are urgently needed.
Matrix metalloproteinase-9 (MMP9), a member of the zinc-dependent endopeptidase family, is a gelatinase involved in a variety of biological processes (e.g., proteolytic extracellular matrix (ECM) degradation, cell-ECM and cell-cell inter- actions, and cell surface cleavage activities). In addition, it degrades and regulates ECM pro- teins and releases bioactive proteins, including cytokines, chemokines, and growth factors [5, 6]. MMP9 degrades type IV collagen and dis-
MMP9 in cancer & computational screening of inhibitors
rupts basement membranes associated with tumor invasion and metastasis. The expression level of MMP9 mRNA is significantly higher in nasopharyngeal carcinoma tissues than in nasopharyngeal tissues, and MMP9 overex- pression accelerates tumor growth by inducing angiogenesis and enhanced local cell invasion and metastasis by degrading the ECM [7]. In esophageal cancer, MMP9 overexpression is significantly correlated with the depth of tumor infiltration, lymphatic infiltration, lymph node metastasis, and the degree of pathological dif- ferentiation [7]. The ECM is a key component of the local tumor microenvironment (TME) and undergoes extensive remodeling during breast cancer evolution. MMP9 is reported as a key player in ECM remodeling during cancer initia- tion and progression through a variety of mech- anisms [8].
Currently, several chemotherapeutic agents target MMP9. MMP9-IN-1, a highly selective MMP9 inhibitor with oral efficacy [9, 10], selec- tively inhibits MMP9 to control the develop- ment, progression, invasion, and metastasis of nasopharyngeal carcinoma, but it also affects the function of the human respiratory system and reduces the activity of other proteases and cytokines because of its strong and effec- tive inhibitory effect [7, 11]. JNJ0966 is anoth- er highly selective MMP9 inhibitor that blocks the conversion of MMP9 zymogen to a catalyti- cally active enzyme [12]. However, it is currently only used in scientific research. Other MMP9 inhibitors exist but with extensive effect tar- gets, which means they have more side effects. Therefore, novel MMP9-targeting drugs are needed.
This study combined a pharmacophore model and a machine learning model to screen for novel MMP9 inhibitors. Pharmacophores are combinations of characterized three-dimen- sional structural elements [13, 14] and have been used to design and screen new drugs on the basis of specific ligand structures [15, 16]. Machine learning is used to predict or classify drugs using data analysis [17] and is helpful in many fields, such as clinical data processing [18, 19]. We explored the role of MMP9 in pan-cancer and assessed the relevance of MMP9 in the tumor immune microenvironment and mRNA modifications. We then constructed a pharmacophore model and a machine learn-
ing model to screen for inhibitors of MMP9, fol- lowed by absorption, distribution, metabolism, excretion (ADME) and toxicity analysis, protein- ligand docking, and molecular dynamics (MD) simulation. This research provides a novel investigation strategy and a group of therapeu- tic candidates for MMP9, which might serve as a strong foundation for further agonist research.
Methods
Analysis of the expression level of MMP9 in pan-cancer datasets
The unified and standardized pan-cancer data- set TCGA (The Cancer Genome Atlas) Pan- Cancer (PANCAN, N = 10535, G = 60499) was downloaded from the University of California Santa Cruz (UCSC) database (https://xen- abrowser.net/). The expression data of the ENSG00000100985 (MMP9) gene was ex- tracted from each sample, and the samples from normal solid tissue, primary blood-derived cancer - peripheral blood, and primary tumors were further screened, followed by log2 (x+0.001) transformation of each expression value. Cancers with fewer than three samples were excluded. The difference in expression between normal and tumor samples in each tumor was calculated using R software (ver- sion 3.6.4), and significance analysis was per- formed using unpaired Wilcoxon rank sum and signed rank tests. Finally, a plot showing the differences in MMP9 expression between can- cers was created.
Identification of the correlation between MMP9 expression levels and survival in pan- cancer
Several metrics (overall survival [OS] and pro- gression-free survival [PFS]) were selected from TCGA samples to investigate the associa- tion between MMP9 expression and patient outcomes. A high-quality prognostic dataset (TCGA) was obtained from a previously pub- lished TCGA prognosis study published in Cell; cancers with fewer than 10 samples and sam- ples with a follow-up time of less than 30 days were excluded. The R software package “sur- vival” was used to obtain a forest map for Cox to analyze the relationship between MMP9 gene expression and survival in each tumor. The patients with each tumor type in the TCGA dataset were divided into two groups according
MMP9 in cancer & computational screening of inhibitors
to the best cut-off value of MMP9 to compare the prognostic differences. The prognostic dif- ferences between the two groups were further analyzed using the “survfit” function of the R software package “survival”, and the log-rank test was used to evaluate significant prognostic differences between the samples of different groups.
Association between MMP9 expression and the TME in pan-cancer
The gene expression profiles of each tumor were extracted separately, and the expression profiles were mapped to “Gene Symbol”. The R software package “ESTIMATE” was used to calculate the stromal, immune, and ESTIMATE scores of each patient with each tumor type according to gene expression. The corr.test function of the R software package “psych” was used to calculate the Pearson’s correlation coefficient between genes and immune inva- sion and immune cell invasion scores in each tumor to determine whether immune invasion scores were significantly correlated.
Correlation between MMP9 expression and mRNA-modifying genes in pan-cancer
The expression data of the marker genes of the MMP9 gene and three types of RNA modifica- tion genes (m1A, m5C, and m6A) in each sam- ple were extracted. Primary blood-derived can- cer - peripheral blood samples and primary tumor samples were screened, and the Pear- son correlation coefficients between MMP9 and the marker genes of the five types of immune pathways were calculated by filtering all normal samples and transforming each expression value. These data were used to esti- mate the role of RNA modifications in cancer using the gene expression dataset and further summarize their therapeutic potential for abnormal deposition in cancer.
Association between MMP9 expression and tumor mutation burden in pan-cancer
Simple nucleotide variation data were down- loaded from the database and processed. A simple nucleotide variation dataset was used to plot the mutational landscape of MMP9 in four tumor types. Tumor mutation burden (TMB) scores were calculated using mutation data of four tumor samples from TCGA, and patients
were divided into low-TMB and high-TMB groups according to the TMB score quartile. Dif- ferentially expressed genes (DEGs) were identi- fied in the low- and high-TMB groups.
Construction and verification of pharmacody- namic mass models
Pharmacophore models are useful for screen- ing ideal compounds, and two types of pharma- cophore models are known: structure-based pharmacological models derived directly from the X-ray structure of protein - ligand complex- es and ligand-based pharmacological models derived from the structure of known active compounds. The crystal structures of human MMP9 receptors with different ligands (pro- tein data bank [PDB] IDs: 2OW0, 2OW1, 4H3X, and 4WZV) were analyzed using LigandScout v4.3, which provides automated construction of three-dimensional pharmacophores. Ligand- Scout identifies 3D chemical features; ligand options containing hydrogen bond donors (HBDs) and acceptors (HBAs) are shown as concentrated vectors, along with negative and positive ignitable spheres. Moreover, lipophilic regions are indicated by spheres. In addition, to expand selectivity, the LigandScout indicator incorporates spatial data about regions into each promising inhibitor. Pharmacophore sig- natures were entered into the web server Pharmit (http://pharmit.csb.pitt.edu/) to search for and identify small molecules that bind to the target molecule (MMP9 receptor) on the basis of structural and chemical similarities between small molecules. By combining the code from the PDB, 1,752,844 possible small molecules are obtained. Then, the deep learn- ing model was built by DeepScreening (http:// deepscreening.xielab.net/) for further screen- ing, and the performance of the model was evaluated using test loss, accuracy, recall, pre- cision, the F1 (F1-score), and Matthew’s corre- lation coefficient (MCC).
ADME and toxicity prediction
The ADME module of Discovery Studio 4.5 was used to calculate the ADME of selected com- pounds, along with their water solubility, blood- brain barrier permeability, cytochrome P-450 2D6 (CYP2D6) inhibition, hepatotoxicity, human enteric absorption, and plasma protein binding levels. The topcat module of Discovery Studio
MMP9
Pan-cancer Analysis
Virtual Screening
Gene Expression
1752844molecules
Pharmacophore Model
Prognotic Analysis
230 molecules
Immune Infiltrarion Analusis
Deep Learning Model
49 molecules
mRNA Modification Analusis
ADMET
SNP Analysis
2 molecules
Protein-molecule Docking and MD
4.5 was used to calculate the potential com- pounds’ toxicity and other properties, such as the National Toxicology Program rodent carci- nogenicity, the Ames mutagenicity, the devel- opmental toxicity potential, the median oral lethal dose (LD50), and the chronic oral mini- mum observed adverse reaction level (LOAEL) in rats. These pharmacological properties were considered when selecting appropriate drug candidates for MMP9.
Protein molecule docking
Molecular docking was assessed using the Glide module of the Schrödinger kit to collect the active conformation of small molecules interacting with the MMP9 receptor. Top-level compounds from the pharmacophore screen- ing were prepared in Maestro using the LigPrep module to obtain the starting struc- ture for docking. Ligand-acceptor interactions included hydrogen bond interactions, van der Waals interactions, IT-IT stacked interactions, and ionic interactions. The molecular docking results were analyzed according to the binding energy (kcal/mol) between small molecules and amino residues and the number of binding interactions.
Molecular dynamics simulation
The best binding conformations of the ligand- MMP9 complexes among the potential com- pounds predicted by the molecule docking pro- gram were submitted to the MD simulation using Discovery Studio 4.5. The ligand-acce-
ptor complex was placed into an orthogonal box and sol- vated with an explicit perio- dic boundary-solvated water model. To simulate the physi- ological environment, sodium chloride was added to a sys- tem with an ionic strength of 0.145. The system was then subjected to a CHARMM force field for analogy-based ligand parameterization. For this sys- tem, the following simulation protocols were applied: 1000 minimization steps for the fastest descent and conjugate gradient; 5ps equilibrium sim- ulation at 300 K (slow drive 2ps from 50 K initial tempera- ture) and atmospheric pressure; 25ps-MD sim- ulation (production mode) at NPT (atmospheric pressure and temperature). The Particle Grid Ewald (PME) algorithm was used to calculate remote electrostatic, and the Linear Constraint Solver (LINCS) algorithm was used to fix all bonds involving hydrogen. With the initial com- plexity setting as a reference, the trajectories of the root mean square deviation (RMSD), poten- tial energy, and structural features were deter- mined by the Discovery Studio 4.5 analysis tra- jectory protocol.
Results
MMP9 expression in pan-cancer
The complete data analysis process is depict- ed in Figure 1. We analyzed the expression data of 26 cancer types and found that MMP9 was highly expressed in the vast majority of tumor samples. The expression differed signifi- cantly between most tumors, including glio- blastoma multiforme (GBM), cervical squa- mous cell carcinoma and endocervical ad- enocarcinoma (CESC), lung adenocarcinoma (LUAD), colon adenocarcinoma (COAD), co- lon adenocarcinoma/rectum adenocarcinoma esophageal carcinoma (COADREAD), breast invasive carcinoma (BRCA), esophageal carci- noma (ESCA), stomach and esophageal carci- noma (STES), kidney renal papillary cell carci- noma (KIRP), kidney pancreas carcinoma (KIPAN), stomach adenocarcinoma (STAD), prostate adenocarcinoma (PRAD), uterine cor- pus endometrial carcinoma (UCEC), head and
A
25
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20
Expression
15
10
5
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0
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Norma
-5
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-15
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GBM(T=153,N=5)
GBMLGG(T=662,N=5)
LGG(T=509,N=5)
CESC(T=304,N=3)
LUAD(T=513,N=109)
COAD(T=288,N=41)
COADREAD(T=380,N=51)
BRCA(T=1092,N=113)
ESCA(T=181,N=13)
STES(T=595,N=49)
KIRP(T=288,N=129)
KIPAN(T=884,N=129)
STAD(T=414,N=36)
PRAD(T=495,N=52)
UCEC(T=180,N=23)
HNSC(T=518,N=44)
KIRC(T=530,N=129)
LUSC(T=498,N=109)
LIHC(T=369,N=50)
THCA(T=504,N=59)
READ(T=92,N=10)
PAAD(T=178,N=4)
PCPG(T=177,N=3)
BLCA(T=407,N=19)
KICH(T=66,N=129)
CHOL(T=36,N=9)
B
GBMLGG.L.H
C
1.0
p=1.2e-51
Survival probability
HR=6.26,95C1%(4.79,8.18)
0.8
4,000-
3,000-
TCGA-GBMLGG(N=656)
4,000
.5
ImmuneScore
r=0.50
ImmuneScore
3,000-
TCGA-LGG(N=504)
2,000
1,000-
p=1.1e,43
2,000
r=0.35
0.3
1,000-
p=2.3e-16
0-
0
0.0 - Number at risk
-1,000
-1,000
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-2,000-
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464
155
71
18
3
4
0
1,605
3,210
4,815
6,420
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0
5
-5
0
5
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MMP9 Expression
MMP9 Expression
KIPAN -L .H
1.0
p=2.9e-5
Survival probability
HR=1.76,95C1%(1.35,2.31)
0.8
4,000-
TCGA-KIPAN(N=878)
4,000=
ImmuneScore
3,000-
r=0.47
ImmuneScore
3,000
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2,000-
r=0.32
0.5
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1,000-
p=2.0e-48:
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p=1.1e-13:
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175
160
46
28
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-5
0
5
-5
0
5
0
1,481
2,962
4,443
5,924
MMP9 Expression
MMP9 Expression
Overall survival
UVM-L .H
1.0
p=6.7e-5
Survival probability
HR=4.92,95C1%(2.08,11.66)
0.8
4,000-
TCGA-UVM(N=79)
4,000=
ImmuneScore
3,000-
r=0.59
ImmuneScore
3,000
TCGA-ACC(N=77)
2,000
r=0.29
0.5
2,000
1,000-
p=9.1e-9
1,000-
p=0.01
0.3
0
0
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-2,000-
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54
37
13
2
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11
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0
5
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20
-5
0
5
0
650
1,300
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2,600
MMP9 Expression
MMP9 Expression
Overall survival
neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), lung squa- mous cell carcinoma (LUSC), liver hepatocellu- lar carcinoma (LIHC), rectum adenocarcinoma (READ), pheochromocytoma and paraganglio- ma (PCPG), bladder urothelial carcinoma (BLCA), kidney chromophobe carcinoma (KICH), and cholangiocarcinoma (CHOL) (P < 0.05). MMP9 was highly expressed in brain low-grade
glioma (LGG), cervical squamous cell carcino- ma and endocervical adenocarcinoma (CESC), and pancreatic adenocarcinoma (PAAD); how- ever, because of the small sample size of the control group (normal), no significant differenc- es were detected. Furthermore, the expression of MMP9 in thyroid carcinoma (THCA) did not differ significantly from that of normal samples (Figure 2A).
MMP9 in cancer & computational screening of inhibitors
Pan-cancer prognostic analysis of MMP9
To further explore the association between MMP9 and the prognosis of pan-cancer, we performed prognostic analysis on 39 cancer types. The OS results (Supplementary Figure 1A) showed that for glioma (GMBLGG), KIPAN, uveal melanoma (UVM), LGG, adrenocortical carcinoma (ACC), liver hepatocellular carcino- ma (LIHC), BLCA, and testicular germ cell tumors (TGCTs), higher MMP9 expression was associated with a lower survival rate (P < 0.05). For skin cutaneous melanoma (SKCM) and SKCM-M, higher MMP9 expression was as- sociated with a higher survival rate, suggesting that MMP9 is a beneficial factor for these two tumor types (P < 0.05). For the other 28 tu- mors, expression was not significantly associ- ated with survival (P > 0.05). We also plotted the survival curves of GMBLGG, KIPAN, UVM, LGG, ACC, KIRC, LIHC, BLCA, and TGCT (Figure 2B, Supplementary Figure 1B, 1C). In addi- tion, we analyzed the PFS of pan-cancer (Supplementary Figure 1D) and found that for GMBLGG, KIPAN, KIRC, UVM, LGG, ACC, THCA, GBM, and KICH, higher MMP9 expression was associated with faster tumor progression (P < 0.05); for lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) and ovarian serous cystadenocarcinoma (OV), higher MMP9 ex- pression was associated with slower tumor progression, suggesting that MMP9 is a sup- pressor of tumor development in these can- cers (P < 0.05). For the other 28 tumors, MMP9 expression was not significantly associated with tumor progression (P > 0.05). In summary, higher MMP9 expression was associated with a lower survival rate and tumor progression in GMBLGG, KIPAN, UVM, LGG, ACC, and LIHC.
Correlation between MMP9 expression, the TME, and immune infiltration
The TME is composed of various components, such as immune cells, non-immune stromal cells, and ECM proteins, including innate immune cells, adaptive immune cells, extracel- lular immune factors, and cell surface mole- cules. TME, also known as the tumor immune microenvironment (TIME), has unique internal interactions and plays an important role in tumor biology [20, 21]. To further explore the correlation between MMP9 and tumor immune infiltration, we performed immune analysis on six tumors with MMP9 expression. We found
that MMP9 expression in GMBLGG, KIPAN, UVM, LGG, ACC, and LIHC was positively corre- lated with the immune score, ESTIMATE score, and stromal score (Figure 2C, Supplementary Figure 2).
In addition, we analyzed the correlation of MMP9 expression with immune cells in each tumor (Figure 3A). We found that macrophag- es were significantly associated with MMP9 expression. Specifically, M0 macrophages were significantly positively correlated with MMP9 expression in all six tumors; classically activated M1 macrophages were positively cor- related with MMP9 expression in GMBLGG, KIPAN, UVM, LGG, and ACC; alternative activat- ed M2 macrophages were positively correlated with MMP9 expression in GMBLGG and LGG. High macrophage expression leads to the release of more cytokines (such as epidermal growth factor (EGF), which promotes the metastasis and invasion of cancer cells [22, 23]; this may explain the high correlation between MMP9 expression and metastasis. Monocytes were negatively correlated with MMP9 expression in five tumors but not in ACC, suggesting that the ability to recognize and kill tumor cells was inhibited [24]. Activated natural killer cells were negatively correlated with MMP9 expression in GMBLGG, KIPAN, KIRC, and ACC, indicating that their ability to kill tumor cells decreases when tumors express more MMP9. Furthermore, MMP9 expression was positively correlated with regulatory T cells (Tregs) in GMBLGG, KIPAN, UVM, LGG, and KIRC, which could suppress the immune sys- tem [25].
Correlation of MMP9 expression with RNA modification genes
Chemical RNA modifications play an important role in fundamental cellular processes, such as cell differentiation, protein production, cell signaling, and the maintenance of circadian rhythms [26, 27], and these modifications can be critical in tumor suppression or tumor-pro- moting effects. We found that GBMLGG was positively correlated with most of the genes in m1A modification, with significant differences between tumor types; the gene ALKBH3 was positively associated with MMP9 expression in four tumors - GBMLGG, KIPAN, ACC, and LGG - with statistically significant differences be- tween tumors (Figure 3B). ALKBH3 can pro-
MMP9 in cancer & computational screening of inhibitors
A
Correlation coefficient
TCGA-LGG(N=504) *
0.5
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*
*
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TCGA-KIRC(N=528) *
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*
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TCGA-GBMLGG(N=656)
*
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*
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TCGA-UVM(N=79)
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*
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TCGA-ACC(N=77) *
2.0
*
*
*
*
Plasma_cells
T_cells_CD8
T_cells_CD4_naive
T_cells_CD4_memory_resting
T_cells_follicular_helper
T_cells_CD4_memory_activated
T_cells_regulatory_(Tregs)
T_cells_gamma_delta
NK_cells_resting
NK_cells_activated
Monocytes
Macrophages_MO
Macrophages_M1
Macrophages_M2
Dendritic_cells_resting
Dendritic_cells_activated
Mast_cells_resting
Mast_cells_activated
Eosinophils
Neutrophils
pValue
1.0
0.0
B
TRMT61A
Type
D
Type
TRMT61A
· Writer
*
Writer
*
Reader
TRMT6
Reader
*
Eraser
*
*
TRMT6
· Eraser
*
TRMT10C
Correlation coefficient
Correlation coefficient
*
*
1.0
TRMT10C
1.0
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TRMT61B
.
*
0.0
TRMT61B
0.0
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*
-1.
YTHDC1
-1.
YTHDF3
*
YTHDF1
1.0
YTHDF3
1.0
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pValue
0.5
YTHDF1
pValue
YTHDF2
0.5
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ALKBH1
0.0
*
0.0
*
ALKBH3
ALKBH1
*
*
*
*
*
GBMLGG(N=662)
UVM(N=79)
ACC(N=77)
LGG(N=509)
KIPAN(N=884)
KIRC(N=530)
ALKBH3
NSUN3
NSUN4
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Type
TRDMT1
TRMT61A
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*
*
*
*
*
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· Eraser
NSUN5
TRMT6
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Correlation coefficient
DNMT3A
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UVM(N=79)
ACC(N=77)
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KIPAN(N=884)
KIRC(N=530)
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*
NSUN3
*
NSUN4
*
*
TRDMT1
*
GBMLGG(N=662)
UVM(N=79)
ACC(N=77)
LGG(N=509)
KIPAN(N=884)
KIRC(N=530)
Figure 3. A. Pan-cancer cohort (GBMLGG, KICH, KIRC, KIRP, KIPAN and UVM). Analysis of the relationship between MMP9 expression and immune cell infiltration. B-D. Correlation between m1A, m5C and m6A mRNA modification genes and the expression of MMP9 in Pan-cancer cohort (GBMLGG, KICH, KIRC, KIRP, KIPAN and UVM).
mote the proliferation, migration, and invasion of cancer cells [28]. In m6A modification (Figure 3C), MMP9 expression was positively correlat- ed with most genes in GBMLGG, with signifi-
cant differences between tumor types. TR- MT61A was positively correlated with MMP9 expression in four tumors - GBMLGG, KIPAN, ACC, and LGG - with statistically significant dif-
MMP9 in cancer & computational screening of inhibitors
ferences between tumors (Figure 3C). A58 in m1tRNA is composed of the RNA-binding com- ponent TRMT6 and the catalytic component TRMT61A, which is crucial for maintaining m1tRNA stability, affects translation initiation, and has profound effects on various biological processes [29]. In m5C modification (Figure 3D), MMP9 expression was positively correlat- ed with most genes in GBMLGG, with sig- nificant differences between tumor types. DNMT3B was positively correlated with MMP9 in four tumors - GBMLGG, KIPAN, ACC, and LGG - with significant differences between tumors (Figure 3D). DNMT3B is involved in de novo DNA methylation in embryonic stem cells and early embryos. It is overexpressed in several human tumors and is an indicator of early tumor recurrence and poor prognosis in hepa- tocellular carcinoma [30].
Correlation of MMP9 expression with TMB
We further performed single nucleotide poly- morphism (SNP) analysis by dividing patients into two groups: a high MMP9 expression gro- up and a low MMP9 expression group. In LGG (Supplementary Figure 3A), the genes IDH1, TP53, and ATRX had high mutation frequencies (> 20%), and EGFR, MYH13, EPPK1, MYO15A, SI, KIAA1109, CDH17, SLCO1B1, SYNE2, CFAP47, SSPO, and ZFHX4 also had higher mutation rates and more mutation types in the high MMP9 expression group. In KIRC (Supplementary Figure 3B), the genes VHL and PBRM1 had high mutation frequencies (> 20%), and the mutation types were mostly mis- sense mutations, frameshift deletion muta- tions, nonsense mutations, splice site muta- tions, and in-frame insertions. THSD7B, ADGRV1, XPO7, LAMC2, and UBR4 also had higher mutation rates and mutation types in the MMP9 high expression group. TP53, CTNNB1, and MUC16 showed high mutation frequencies (> 20%) in ACC (Supplementary Figure 3C) as well as higher mutation rates in the high MMP9 expression group. DST, FAT4, ASXL3, CNTNAP5, and NF1 also had higher mutation rates and more mutation types in high MMP9 expression group. In UVM (Supplementary Figure 3D), the genes GNAQ, GNA11, BAP1, and SF3B1 had high mutation frequencies (> 20%), whereas BAP1 had a high- er mutation frequency in the high MMP9 expression group. Finally, SF3B1 and EIF1AX
showed higher mutation rates in patients with high MMP9 expression.
Construction and validation of the pharmaco- phore model
To further screen for novel inhibitors of MMP9, we constructed a ligand-based pharmaco- phore model. We first considered evaluating the major residues obtained by analyzing the crystal structures (PDB IDs: 2OW0, 2OW1, 4H3X, and 4WZV) to obtain the major residues of the MMP9 receptor (Figure 4A-D), identify- ing small active molecules and target proteins and the physicochemical interaction patterns between them and then mapping them to 3D array features (e.g., hydrogen bonds, lipophilic contacts, and ionic or aromatic interactions).
As shown in Figure 4A, the crystal structure 2OWO exhibited two hydrophobic interactions, binding with the residues TYR423, LEU397, LEU418, VAL398, and ZN444. Two hydrogen bond acceptors were found with ALA189, GLN402, HOH503, HOH608, and LEU188. In addition, a positively ionized region was also detected. The crystal structure 2OW1 (Figure 4B) exhibited two hydrophobic interactions, binding with the residues VAL398, LEU418, TYR423, LEU397, and ZN444. Five hydrogen bond acceptors were found with LEU188, HOH593, HOH557, ALA189, and GLN402, and three hydrogen bond donors were also ob- served, along with a positively ionized region. The crystal structure 4H3X (Figure 4C) exhibit- ed two hydrophobic interactions, binding with the residues LEU243, TYR248, VAL223, and ZN301. Two hydrogen bond acceptors were found with LEU188 and ALA189, and three hydrogen bond donors with ALA189, HIS226, and HOH415 were also observed, along with a positively ionized region. The crystal structure 4WZV (Figure 4D) exhibited two hydropho- bic interactions, binding with the residues TYR245, MET247, ZN302, VAL223, and TYR248. Four hydrogen bond acceptors were found with ALA191, HOH401, LEU188, and ALA189, and ALA189, HIS230, and GLU227 hydrogen bond donors were also observed, along with a positively ionized region. As shown in Supplementary Figure 4A-D, these com- pounds exerted the largest effect with the amino acid residue H401.
MMP9 in cancer & computational screening of inhibitors
A
ALA189A
GLN402A
HOH503A
ZN444A
HOH608A
HN
VAL398A
LEU418A
NH
NH
LEU397A
TYR423A
LEU188A
B
VAL398A
LEU188OH593A
HOH SEZA 89A
HIS401A
GLN402A
OH
NH
LEU418A
HOH551A
F
NH
NH
F
3
TYR423A
LEU397A
ZN444A
C
TYR248A
HO
LEU243A
NH
VAL223A
LEU188A
ZN301A
ALA189A
o =S
HIS226A
HOH415A
D
ALA191A
GLU227A
HIS230A
o
HO
NH
NH
TYR248A
HOH818B
VAL223A
TYR2458
LEU188A
ZN302A
MET2478
ALA189A
E
Accuracy
Precision
AUC
Accuracy
Precision
AUC
1.00
1.00
1.00
0.80
0.80
0.80
0.60
0.60
0.60
0.40
0.40
0.40
0.20
0.20
0.20
0.00
15
20
25
Epoch
0.00
0
5
10
30
0
5
Epoch
0.00
10
15
20
25
30
0
5
10
15
20
25
Epoch
30
Virtual screening
We performed a prospective virtual screening (VS) of a database of compounds of natural ori- gin and synthetic drugs, in which we used fitted values as pharmacology-based screening crite-
ria. After removing duplicates, we screened 230 small molecules with the same pharmaco- phore from 1,752,844 small molecules. Then, we built a deep learning model with MMP9 and 3479 small molecules and validated it. The accuracy, precision, and area under the curve
MMP9 in cancer & computational screening of inhibitors
(AUC) of the model gradually stabilized with increases in the Epoch, and finally stabilized at around 0.9 (Figure 4E). Recall and F1 also gradually stabilized around 0.9, and loss and MCC gradually stabilized around 0.45 and 0.7, respectively (Supplementary Figure 4E-H). After screened by the machine learning model, 49 small molecules (score = 1) from the 230 small molecules were identified.
ADME and toxicity prediction
Pharmacokinetics is an important analytical method for detecting effective compounds in the process of drug discovery, and the analysis of its properties plays a key role in drug design (Supplementary Table 1). Water solubility pre- dictions (defined in water at 25℃) indicated that 33 compounds were soluble in water. In addition, 21 compounds showed good human intestinal absorption levels. Furthermore, 40 compounds were highly bound to plasma pro- teins, whereas the rest were not. CYP2D6 is an important enzyme involved in drug metabo- lism, and all 49 compounds were predicted to be non-inhibitors of cytochrome P450 2D6 (CYP2D6). Regarding hepatotoxicity, seven compounds were predicted to be nontoxic. CHEMBL82047 and CHEMBL381163 have good water solubility, intestinal absorption, and protein binding and can act as non-inhibitors of CYP2D6 without hepatotoxicity (Supplementary Table 2). We conducted a comprehensive in- vestigation of the safety of these small mole- cules; the results showed that two small mole- cules, CEMBL82047 and CEMBL381163, are non-mutagenic and predicted to have less Ames mutagenic, rodent carcinogenic, and developmental toxicity potential than other compounds.
Protein molecular docking
To further study the binding properties of small molecules to proteins, we carried out molecular docking experiments (Figures 5, 6A, 6B, Supplementary Figure 5A-D). As shown in Table 1, CEMBL82047 and CEMBL381163 have higher binding affinity to the protein compared with the drugs JNJ0966 and MMP9- IN-1. Supplementary Figure 5E, 5F shows the TT-dependent interactions and hydrogen bonds determined by the structural calculations. The results of the structural calculation studies showed that CEMBL82047 forms four pairs of
hydrogen bonds with the MMP9 residue accep- tor, and the complex itself forms four pairs of TT-related interactions with the MMP9 residue acceptor. CHEMBL381163 forms four pairs of hydrogen bonds and seven pairs of n-related interactions with the MMP9 residue acceptor (Tables 2 and 3).
Molecular dynamics simulation
Molecular dynamics simulation is a method for simulating the physical motion trajectories and states of atoms and molecules based on Newtonian mechanics. We build a molecular dynamics simulation module to evaluate the stability of small molecule-protein complexes under natural environment conditions. Figure 6C, 6D shows the potential energy and RMSD plots for each complex. The trajectories of each complex reached equilibrium, and the potential energy and RMSD of complex- es CEMBL82047-MMP9 and CEMBL381163- MMP9 reached a steady state over time. This indicates that the complexes can exist stably in the natural environment.
Discussion
Tumors are among the leading causes of death worldwide [2], and MMP9 is a reported cancer biomarker [6] that promotes tumor invasion and metastasis, greatly contributing to the occurrence and development of tumors [5, 6]. Although great progress has been made in the design and development of drugs targeting MMP9, these drugs have many shortcomings. This study systematically assessed the expres- sion pattern and prognostic value of MMP9 in pan-cancer and screened for specific MMP9- targeting drugs.
We found that the expression level of MMP9 differed significantly between tumor samples and normal samples in most of the 26 cancers investigated. Higher MMP9 expression was associated with poorer survival and tumor pro- gression in GMBLGG, KIPAN, UVM, LGG, ACC, and LIHC. These findings are consistent with those of previous reports; for example, elevat- ed MMP9 expression in breast cancer has been identified as a predictor of shortened patient survival [31-33]; it also acts as a prog- nostic biomarker for thyroid cancer [34]. To fur- ther confirm the correlation between MMP9 expression and tumors, we performed immune
A
B
C
D
ARG A:424
A:597
LEU
ALA
A:187
A:417
90°
PRO
A:415
H H
H
2
TYR
A:423MET
6
H
LEU A:418
GLY :186
A-456
A:422
A:426
TYR
-189A:1
GLU
A:420
PRO
A:416
H
A:421
VAI
HIS
A:398
A:401
HIS
A:190
HIS
A:411
HIS
A:405
Interactions
van der Waals
Pi-Pi Stacked
Conventional Hydrogen Bond
Alkyl
Carbon Hydrogen Bond
Pi-Alkyl
E
F
GLY
A:186
90°
PHE
HIS
A:190
ALA
HIS
:189
A:405
MET
A:422
A:110
ARG
LEU
A:397
GLI
A:424
2.
A:402
HIS
A:401
Å
4.411
H
LEU
A:418
H
PRO
A:421
TYR
LEU
A:423
A:187
A:188
VAL
A:398
Interactions
van der Waals
Pi-Sulfur
Conventional Hydrogen Bond
Alkyl
Carbon Hydrogen Bond
Pi-Alkyl
Pi-Signa
infiltration analysis on six abovementioned tumors with high MMP9 expression in TCGA. We found that MMP9 expression in patients with tumors was significantly correlated with the stromal score, immune score, and ESTIMA- TE score. We also examined the relationship between MMP9 expression and the infiltration of 22 immune cell subtypes, and our findings showed that the level of immune cell infiltration
was significantly correlated with MMP9 expres- sion in most cancer types. This also demon- strates that immune escape occurs in patients with tumors with high MMP9 expression; more- over, it illustrates the mechanism of MMP9 in tumors. For example, macrophages were sig- nificantly positively associated with all six tumors, and high macrophage expression pro- motes cancer initiation and malignant progres-
A
B
LEU-188
LEU-188
ALA-189
ALA-189
GLN-402
GLN-402
HIS-411
C
D
Potential energy(Kcal/mol)
-47000
CHEMBL82047
60
CHEMBL82047
CHEMBL381163
CHEMBL381163
40
-48000
RMSD
20
-49000
0
0
20
40
60
80
100
120
20
40
60
80
100
120
Time(Ps)
Time(Ps)
| COCKER potential energy | |
|---|---|
| CEMBL82047 | -12.164 |
| CEMBL381163 | -11.623 |
| JNJ0966 | -6.629 |
| MMP9IN1 | -8.618 |
sion. During tumorigenesis, macrophages cre- ate a mutagenic and growth-promoting inflam- matory environment; as tumors progress to malignant tumors, macrophages stimulate angiogenesis, enhance tumor cell migration and invasion, and suppress antitumor immuni- ty [34]. Monocytes were negatively associated with six tumors, and their ability to generate antitumor effectors and activate antigen-pre- senting cells was suppressed [24]. NK cells were also significantly inhibited in these six tumors, and their ability to directly kill tumor cells and release soluble factors affecting
innate and adaptive immune responses was significantly inhibited. In the TME, Tregs can be induced and differentiated by traditional T cells; they have strong immunosuppressive functions, inhibit anti-tumor immunity, and pro- mote the occurrence and development of tumors, which also explains why Treg levels are positively correlated with these tumor types [35]. Activated CD4 memory T cells can sup- press anticancer immunity, thereby hindering protective immune surveillance of tumors and hindering effective antitumor immune respons- es of tumor hosts, promoting tumor develop- ment and progression. This finding is consis- tent with the results of a previous study, in which activated CD4 memory T cell expression was positively correlated with tumors [36].
Exploring the mutational landscape of MMP9 in different cancers further, we found that UVM, KIRC, ACC, and LGG - four types of tumors with high MMP9 expression - had much higher mutation numbers and more mutation types
MMP9 in cancer & computational screening of inhibitors
| Receptor | Compound | Donor Atom | Receptor Atom | Distances (Å) |
|---|---|---|---|---|
| 2OW1 | CEMBL82047 | LEU188:H | UNK900:O2 | 1.84939 |
| ALA189:H | UNK900:O2 | 2.5637 | ||
| GLN402:HE22 | UNK900:O5 | 2.04371 | ||
| UNK900:H29 | ALA189:O | 2.21685 | ||
| CEMBL381163 | LEU188:H | UNK900:O3 | 2.73564 | |
| GLN402:HE22 | UNK900:O7 | 1.84541 | ||
| HIS411:HD1 | UNK900:O4 | 2.63353 | ||
| UNK900:H22 | ALA189:O | 2.04562 | ||
| JNJ0966 | LEU188:H | UNK900:N3 | 2.7175 | |
| UNK900:H1 | MET422:O | 2.01943 | ||
| MMP9IN1 | GLN227:HE21 | UNK900:N2 | 2.6066 | |
| UNK900:H11 | TYR245:O | 1.90625 |
| Receptor | Compound | Donor Atom | Receptor Atom | Distances (Å) |
|---|---|---|---|---|
| 2OW1 | CEMBL82047 | HIS401 | UNK900 | 4.2886 |
| UNK900:C15 | LEU187 | 4.82839 | ||
| UNK900 | LEU188 | 5.32252 | ||
| UNK900 | VAL398 | 4.8719 | ||
| CEMBL381163 | UNK900:H4 | HIS401 | 2.77631 | |
| UNK900:C1 | LEU397 | 4.16409 | ||
| UNK900:C14 | LEU187 | 4.4138 | ||
| PHE110 | UNK900:C15 | 4.14676 | ||
| HIS411 | UNK900 | 5.04727 | ||
| TYR423 | UNK900:C1 | 4.81758 | ||
| UNK900 | LEU188 | 4.80899 | ||
| JNJ0966 | HIS401 | UNK900 | 4.00463 | |
| UNK900 | TYR423 | 5.5835 | ||
| ALA189 | UNK900:C9 | 3.89591 | ||
| UNK900:C9 | LEU188 | 4.59029 | ||
| UNK900:C9 | VAL398 | 4.42465 | ||
| UNK900 | LEU188 | 4.21123 | ||
| UNK900 | VAL398 | 4.71999 | ||
| UNK900 | LEU397 | 5.13481 | ||
| UNK900 | LEU418 | 5.36355 | ||
| MMP9IN1 | UNK900 | LEU243 | 5.32463 |
than normal tissues. This also verified that MMP9 promotes tumorigenesis and develop- ment. TMB reflects the number of cancer muta- tions, and a higher TMB generally indicates better outcomes. Mutations are processed as neoantigens and presented to T cells by major histocompatibility complex (MHC) proteins, and a higher TMB results in more neoantigens, increasing the chances of T-cell recognition and improving immunotherapy efficacy [37].
Although MMP9 is highly expressed in most tumors and closely related to tumor metasta- sis, only a few drugs specifically target MMP9, and they have many limitations. JNJ0966 is a specific inhibitor of MMP9; it is reportedly involved in the progression and development of various diseases, and it can regulate a series of physiological response processes in the body by regulating the expression of MMP9. However, as mentioned above, JNJ0966 is currently only
MMP9 in cancer & computational screening of inhibitors
used in scientific research [12]. Similarly, MMP9-IN-1, as a specific MMP9-targetingdrug, has not been put into clinical use on a large scale because of several defects, such as respiratory system inhibition [7, 11]. Although the mechanism of action of MMP9 in tumor progression is relatively clear, the application of existing drugs is not satisfactory. Therefore, it is necessary to use various cell biology experi- ments and other methods to screen for and develop new drugs targeting MMP9.
We virtually screened 1,752,844 small-mole- cule compounds in a natural source com- pound and synthetic drug database. By con- structing a pharmacophore model, we screen- ed 230 small-molecule compounds with the same pharmacophore and then constructed a pharmacophore model. We used a machine learning model to further screen 49 small mol- ecule compounds with high binding affinity to MMP9 and pooled them for further study.
The ADME and toxicity prediction results indi- cated that CEMBL82047 and CEMBL381163 had good water solubility, absorption levels, and plasma protein binding properties, with no hepatotoxicity or toxicity and low Ames muta- genicity, rodent carcinogenicity, and develop- mental toxicity, indicating their potential as ideal compounds. Then, we further performed docking analysis and the results showed that CEMBL82047 and CEMBL381163 had higher binding affinity to MMP9 than JNJ0966 and MMP9-IN-1. Because these two compounds form more chemical bonds with MMP9 than JNJ0966 and MMP9-IN-1, they have a higher interaction force and more stable binding with MMP9, which may enhance their inhibition of MMP9, thereby improving the tumor-killing effect. Finally, we conducted a molecular dynamics simulation, and the results showed that the potential energy and RMSD of these complexes reached a steady state over time, indicating that the two complexes remain sta- ble in natural environments.
In conclusion, MMP9 is highly expressed in most cancers. Higher MMP9 expression in GMBLGG, KIPAN, UVM, LGG, ACC, and LIHC is associated with poorer survival and tumor pro- gression. In GMBLGG, KIPAN, UVM, LGG, ACC, and LIHC, higher MMP9 expression is associ- ated with increased infiltration of immune
cells, such as macrophages and regulatory T cells, and more RNA modifications. In UVM, LGG, ACC, and LIHC, higher MMP9 expression indicates that the tumor has a higher TMB. A total of 49 candidate inhibitors against MMP9 were screened with a ligand-based pharmaco- phore model and a machine learning model. CHEMBL82047 and CHEMBL381163 have good water solubility, absorption levels, and plasma protein binding properties. They also have low Ames mutagenicity, rodent carcinoge- nicity, and developmental toxicity potential, with no hepatotoxicity or toxicity. These mole- cules have a high binding affinity to proteins and are stable in the natural environment. Therefore, CEMBL82047 and CEMBL381163 show potential as MMP9-inhibiting drugs.
Acknowledgements
This work was supported by Natural Science Foundation of Shaanxi province (2022JQ-299), and Research Program of Xi’an Heath Com- mission (2021yb26).
Disclosure of conflict of interest
None.
Address correspondence to: Drs. Ming Li and Bo Wu, Lower Extremity Division, Orthopedic Trauma Department, Honghui Hospital, Xi’an Jiaotong University, Youyi East Road No. 555, Beilin District, Xi’an, Shaanxi, China. E-mail: limingguke123@163. com (ML); bocai527@163.com (BW)
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MMP9 in cancer & computational screening of inhibitors
| CancerCode | pvalue | Hazard Ratio(95%CI) | |
|---|---|---|---|
| TCGA-GBMLGG(N=619) | 1.4e-41 | 1.28(1.23,1.33) | |
| TCGA-KIPAN(N=855) | 4.5e-6 | 1.15(1.08,1.22) | |
| TCGA-UVM(N=74) | 7.4e-6 | 1 1 - - I | 1.63(1.32,2.02) |
| TCGA-LGG(N=474) | 2.8e-5 | 1 O | 1.14(1.07,1.22) |
| TCGA-ACC(N=77) | 4.0e-4 | I- -1 | 1.38(1.15,1.66) |
| TCGA-KIRC(N=515) | 4.7e-4 | 1.14(1.06,1.22) | |
| TCGA-LIHC(N=341) | 5.9e-3 | 1.11(1.03,1.20) | |
| TCGA-BLCA(N=398) | 7.5e-3 | 1.08(1.02,1.15) | |
| TCGA-TGCT(N=128) | 0.03 | 2.98(1.04,8.51) | |
| TCGA-PAAD(N=172) | 0.07 | I | 1.11(0.99,1.24) |
| TCGA-GBM(N=144) | 0.11 | I | 1.08(0.98,1.18) |
| TCGA-KICH(N=64) | 0.13 | --- I | 1.37(0.92,2.06) |
| TCGA-LUAD(N=490) | 0.27 | 1.05(0.96,1.14) | |
| TCGA-SARC(N=254) | 0.30 | 1.04(0.97,1.11) | |
| TCGA-THCA(N=501) | 0.32 | F | 1.14(0.88,1.47) |
| TCGA-UCS(N=55) | 0.49 | - 4 | 1.06(0.90,1.24) |
| TCGA-HNSC(N=509) | 0.52 | 1.03(0.95,1.11) | |
| TCGA-LUSC(N=468) | 0.56 | 1.03(0.94,1.12) | |
| TCGA-MESO(N=84) | 0.58 | 1.03(0.93,1.14) | |
| TCGA-READ(N=90) | 0.60 | 1 - - -- I | 1.09(0.79,1.51) |
| TCGA-KIRP(N=276) | 0.64 | + 1 | 1.03(0.91,1.16) |
| TCGA-SKCM-P(N=97) | 0.72 | I - -1 | 1.04(0.85,1.27) |
| TCGA-COADREAD(N=368) | 0.74 | 1 | 1.02(0.91,1.14) |
| TCGA-COAD(N=278) | 0.81 | + | 1.02(0.90,1.15) |
| TCGA-LAML(N=144) | 0.86 | 1.01(0.95,1.07) | |
| TCGA-SKCM(N=444) | 0.02 | 0.94(0.89,0.99) | |
| TCGA-SKCM-M(N=347) | 0.04 | 0.94(0.89,1.00) | |
| TCGA-OV(N=407) | 0.07 | 0.95(0.89,1.01) | |
| TCGA-DLBC(N=44) | 0.18 | - - - | 0.80(0.57,1.12) |
| TCGA-UCEC(N=166) | 0.23 | 1- -1 | 0.90(0.77,1.07) |
| TCGA-CHOL(N=33) | 0.40 | I - | 0.91(0.74,1.13) |
| TCGA-BRCA(N=1044) | 0.41 | 0.97(0.89,1.05) | |
| TCGA-STES(N=547) | 0.42 | 0.97(0.90,1.05) | |
| TCGA-STAD(N=372) | 0.52 | 0.97(0.88,1.07) | |
| TCGA-THYM(N=117) | 0.64 | 1- r - - - I | 0.89(0.55,1.45) |
| TCGA-PRAD(N=492) | 0.68 | -- - - | 0.92(0.63,1.34) |
| TCGA-PCPG(N=170) | 0.87 | 0.97(0.68,1.39) | |
| TCGA-ESCA(N=175) | 0.93 | + 1 | 0.99(0.88,1.13) |
| TCGA-CESC(N=273) | 0.99 | + ʻ | 1.00(0.88,1.14) |
A
B
LGG .L .H
Survival probability
1.0
p=2.9e-4
HR=1.96,95Cl%(1.35,2.83)
0.8
2.0.5
0.3
0.0- Number at risk
L 353
H121
56
15
2
17
1
5
2
0
1,605
3,210
Overall survival
4,815
6,420
ACC -L -H
Survival probability
1.0
p=1.0e-3
L
HR=3.61,95Cl%(1.60,8.14)
0.8
D0.5
0.3
0.0- Number at risk
L
41
H 36
25
11
4
1
16
3
0
1,168
2,336
Overall survival
3,504
4,672
KIRC .L .H
Survival probability
1.0
p=1.8e-3
HR=1.61,95Cl%(1.19,2.18)
0.8
0.5
0.3
0.0- Number at risk
L
344
186
66
16
1
-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
H 171
91
24
8
1
Log2(Hazard Ratio(95%CI))
0
1,134
2,268
Overall survival
3,402
4,536
C
LIHC -L -H
D
Survival probability
1.0
p=2.4e-4
HR=1.96,95Cl%(1.36,2.83)
0.8
20.5
0.3
0.0- Number at risk
L
81
59
25
6
1 H
H
60
48
15
0
918
1,836
Overall survival
2,754
3,672
BLCA .L .H
Survival probability
1.0
p=7.1e-3
HR=1.56,95Cl%(1.13,2.16)
0.8
0.5
0.3
0.0- Number at risk
L
150
33
10
1
H
248
1
45
12
5
0
1,262
2,524
Overall survival
3,786
5,048
TGCT-L .H
1.0
Survival probability
0.8
p=2.8e-3
HR=2.98,95C1%(1.04,8.51)
2.0.5
0.3
0.0
Number at risk
L
96
40
19
8
-0.8-0.6-0.4-0.20.0 0.2 0.4 0.6 0.8
32
12
8
1
Log2(Hazard Ratio(95%CI))
0
1,859
3,718
Overall survival
5,577
7,436
| CancerCode | pvalue | Hazard Ratio(95%CI) | |||
|---|---|---|---|---|---|
| TCGA-GBMLGG(N=616) | 5.7e-32 | 1.22(1.18,1.27) | |||
| TCGA-KIPAN(N=845) | 2.4e-8 | . + 1 | 1.18(1.12,1.25) | ||
| TCGA-KIRC(N=508) | 3.6e-7 | . - -l | 1.21(1.12,1.30) | ||
| TCGA-UVM(N=73) | 3.5e-4 | . I --- I | 1.41(1.17,1.70) | ||
| TCGA-LGG(N=472) | 2.4e-3 | F 1 | 1.09(1.03,1.15) | ||
| TCGA-ACC(N=76) | 6.1e-3 | F -- + | 1.25(1.06,1.47) | ||
| TCGA-THCA(N=499) | 6.7e-3 | · - - - -- 1 | 1.23(1.06,1.43) | ||
| TCGA-GBM(N=143) | 0.02 | -1 | 1.13(1.02,1.24) | ||
| TCGA-KICH(N=64) | 0.03 | 1- - ----- | 1 1.43(1.04,1.97) | ||
| TCGA-SARC(N=250) | 0.07 | 1 | 1.05(0.99,1.12) | ||
| TCGA-BLCA(N=397) | 0.07 | + 1 | 1.05(1.00,1.12) | ||
| TCGA-PCPG(N=168) | 0.10 | --- 1 | 1.18(0.97,1.44) | ||
| TCGA-PAAD(N=171) | 0.12 | H- - | 1.09(0.98,1.21) | ||
| TCGA-UCS(N=55) | 0.28 | -- 1 | 1.09(0.93,1.27) | ||
| TCGA-LUSC(N=467) | 0.29 | 1.06(0.95,1.17) | |||
| TCGA-BRCA(N=1043) | 0.35 | 17 -1 | 1.04(0.96,1.14) | ||
| TCGA-KIRP(N=273) | 0.55 | + | 1.03(0.93,1.15) | ||
| TCGA-PRAD(N=492) | 0.56 | - - 1 | 1.04(0.92,1.17) | ||
| TCGA-READ(N=88) | 0.57 | |- | - - - " . ------ | | 1.09(0.80,1.49) | |
| TCGA-LUAD(N=486) | 0.60 | I- -I | 1.02(0.95,1.10) | ||
| TCGA-ESCA(N=173) | 0.61 | 4 | 1.03(0.92,1.16) | ||
| TCGA-MESO(N=82) | 0.61 | -1 | 1.03(0.91,1.17) | ||
| TCGA-TGCT(N=126) | 0.62 | 1.05(0.86,1.29) | |||
| TCGA-LIHC(N=340) | 0.72 | F 4 | 1.01(0.95,1.08) | ||
| TCGA-DLBC(N=43) | 7.0e-4 - - - | - - ---- | 1 . | 0.69(0.54,0.88) | |
| TCGA-OV(N=407) | 0.02 | G + | 0.94(0.89,0.99) | ||
| TCGA-STAD(N=375) | 0.10 | 0.92(0.83,1.01) | |||
| TCGA-SKCM(N=434) | 0.11 | 1 1 | 0.96(0.92,1.01) | ||
| TCGA-CESC(N=273) | 0.17 | I- | - | 0.92(0.81,1.04) | |
| TCGA-UCEC(N=166) | 0.18 | -rl | 0.91(0.80,1.04) | ||
| TCGA-CHOL(N=33) | 0.20 | 0.88(0.72,1.07) | |||
| TCGA-STES(N=548) | 0.21 | I- 4 | 0.95(0.88,1.03) | ||
| TCGA-SKCM-M(N=338) | 0.23 | 1 I | 0.97(0.92,1.02) | ||
| TCGA-HNSC(N=508) | 0.31 | 1- el | 0.96(0.89,1.04) | ||
| TCGA-SKCM-P(N=96) | 0.33 | |- - | - --- 1 | 0.91(0.76,1.10) | |
| TCGA-COAD(N=275) | 0.39 | 0.95(0.85,1.07) | |||
| TCGA-COADREAD(N=363) | 0.66 | ト ー | 0.98(0.88,1.09) | ||
| TCGA-THYM(N=117) | 0.69 | . ----- 1 | 0.94(0.71,1.25) | ||
Supplementary Figure 1. (A) MMP9 expression correlates with overall survival time (OS). Forest plots showing the correlations between OS and MMP9 expression across 39 types of cancers. (B, C) Survival curves of MMP9 ex- pression in LGG, ACC, KRIC, LIHC, BLCA and TGCT. L represents low expression of MMP9 group, H represents high expression of MMP9 group. (D) Forest plots showing the correlations between Progression-free survival time (PFS) and MMP9 expression across 39 types of cancers.
MMP9 in cancer & computational screening of inhibitors
A
ESTIMATEScore
6,000
ESTIMATEScore
6,000
4,000
TCGA-GBMLGG(N=656)
4,000
TCGA-LGG(N=504)
ESTIMATEScore
6,000
r=0.38
4,000
TCGA-KIPAN(N=878)
r=0.55
r=0.49
2,000
p=8.0e-54
2,000
p=1.6e-18
2,000
p=1.4e-53
0
0
0
-2,000
-2,00€
-2,000
-4,000
-4,000
-4,000
-5
MMP9 Expression
0
5
MMP9 Expression
-5
0
5
-5
MMP9 Expression
0
5
ESTIMATEScore
6,000
6,000
TCGA-UVM(N=79)
ESTIMATEScore
4,000
TCGA-KIRC(N=528)
ESTIMATEScore
6,000
TCGA-ACC(N=77)
r=0.41
4,000
r=0.59
4,000
r=0.31
2,000
p=3.0e-23
2,000
p=1.3e-8
2,000
p=5.7e-3
0
0
0
-2,000
-2,000
-2,000
-4,000
-4,000
-4,000
MMP9 Expression
-5
0
5
MMP9 Expression
-5
0
5
MMP9 Expression
5
0
5
B
StromalScore
2,000
TCGA-GBMLGG(N=656)
StromalScore
2,000
TCGA-LGG(N=504)
1,000
r=0.59
r=0.39
StromalScore
2,000
TCGA-KIPAN(N=878)
0
p=1.4e-62
1,000
0
p=1.7e-19
1,000
r=0.46
0
p=1.4e-46
-1,00€
-1,00€
-1,000
-2,000
-2,000
-2,000
MMP9 Expression
-5
0
5
-5
0
5
MMP9 Expression
5
MMP9 Expression
0
5
StromalScore
2,000
TCGA-KIRC(N=528)
StromalScore
2,000
TCGA-UVM(N=79)
2,000
TCGA-ACC(N=77)
1,000
r=0.44
0
p=2.8e-26
1,000
r=0.48
StromalScore
1,000
0
p=6.5e-6
r=0.32
p=4.8e-3
0
-1,000
-1,000
-1,000
-2,000
-2,000
-2,000
-5
0
5
-5
MMP9 Expression
0
MMP9 Expression
5
-5
MMP9 Expression
0
5
MMP9 in cancer & computational screening of inhibitors
A
MutCount
309
LGG
SampleGroup
0-
IDH1(p<0.001)
85.9%
TP53(p<0.05)
ATRX(p=0.52)
51.1%
EGFR(p<0.05)
36.6%
7.0%
MYH13(p<0.05)
EPPK1(p<0.01)
2.0%
MYO15A(p<0.05)
2.0%
2.0%
SI(p<0.05)
KIAA1109(p<0.05)
2.0%
CDH17(p<0.05)
1.6%
SLCO1B1(p<0.05)
1.4%
SYNE2(p<0.05)
1.4%
CFAP47(p<0.05)
1.4%
SSPO(p<0.05)
1.4%
ZFHX4(p<0.05)
1.4%
1.4%
B
MutCount
15=
KIRC
SampleGroup
VHL(p=0.45)
PBRM1(p=0.46)
60.4%
50.9%
TTN(p=0.35)
SETD2(p=0.21)
HI
21.5%
BAP1(p=0.07)
15.3%
MUC16(p=0.71
12.7%
MTOR(p=0.71)
8.0%
KDM5C(p=1.00)
8.0%
7.3%
PTEN(p<0.001)
SSPO(p<0.05)
4.4%
THSD7B(p<0.05)
3.6%
ADGRV1(p<0.05)
3.3%
XPO7(p<0.05)
3.3%
LAMC2(p<0.05)
2.2%
UBR4(p<0.05)
2.2%
2.2%
C
10ª
ACC
MutCount
SampleGroup
0
TP53(p=0.20)
CTNNB1(p=0.32)
27.1%
MUC16(p=0.18)
25.0%
22.9%
TTN(p=0.97)
PKHD1(p=0.41)
18.8%
HMCN1(p=0.10)
14.6%
MEN1(p=0.19)
14.6%
MUC4(p=0.65)
12.5%
PRKAR1A(p=1.00)
12.5%
ANK2(p=0.98)
12.5%
DST(p=0.06)
10.4%
FAT4(p=0.06)
10.4%
ASXL3(p=0.34)
10.4%
CNTNAP5(p=0.98)
10.4%
NF1(p=0.34)
10.4%
10.4%
D
UVM
MutCount
6
SampleGroup
0
GNAQ(p=0.22)
GNA11(p=0.92)
49.4%
44.3%
BAP1(p=0.07)
SF3B1(p<0.05)
27.8%
EIF1AX(p<0.05)
22.8%
COL14A1(p=0.98)
12.7%
CYSLTR2(p=0.98)
3.8%
MYOF(p=1.00)
3.8%
PKHD1L1(p=1.00)
3.8%
3 8% 3.8%
SRSF2(p=0.98)
3.8%
MACF1(p=0.23)
3 8%
ADAMTSL1(p=1.00)
3.8%
2.5%
APC(p=0.49)
ARHGEF17(p=1.00)
2.5%
ARID1B(p=1.00)
2.5%
2.5%
SampleGroup:
. Missense_Mutation
.Frame_Shift_Ins
Nonstop_Mutation
LowExp
. Frame_Shift_Del
.In_Frame_Del
.In_Frame_Ins
. HighExp
. Splice_Site
. Translation_Start_Site
. Nonsense_Mutation
MMP9 in cancer & computational screening of inhibitors
A
P193 G404F403 A400A399 308
B
A40
P194 P193
G404
C
F403
G2334232 A231 P193 F19
P1947
L407
A406
F396
G408
A191
L234
D235
F110
G229
L407
L395
L409
A400
G106
F228
G408
$394
D410
A399
Q108
A191
L409
Y393
D410
405
402
W210
S412
H411
405
F396
S237
H230
A225
H41
H401
L418
402
₩148
H236
E227
A224
5412
L418
4397
E208
W148
420
H40
L395
H226
A242
L243
F221
W148
E416
P421
$394
¥420
190
D205
V398
M244
245
223
L220
E416
A417
P421
6MR501
F204
A417
Y393
89
H203
M422
7MR501
397
$238
P246
10B306
222
S219
M419
W210
M419
M422
L188
A202
V239
M247
A189
Y218
V414
V414
Y423
89
E208
P240
Y423
W210
187
A191
L188
P415
N444
44 E111
$186
P415
R424
L188
Y248
Y179
ZN444
L187
D205
R249
ZN301
L187
E208
R424
D177
F425
G186
F204
F250
G188
D205
F425
H175
T426
H203
T251
F204
P430
F181
P255
H203
L431
D185
P430
H190
D435
ZN445
2D182
K184
L431
D435
D185
F181
L256
F181
H190
CA447 F110
G112”
D260
CA44@D182 K184
CA303 GOL309 D182 D185-180
D
A231 P194 P193 G229F228 A225
E
F
L232
G233
A224
V223
Loss
Recall
L234
F221
Loss
Recall
8237
L220
0.67
1.00
W148
H238
D235 H230
27
$219
0.60
E241
L243
H228
Y218
0.80
A242
¥245
+222
W210
0.50
M244
P246
E40301
91
E208
0.40
0.60
$238
D205
V239
M247
190
F204
0.30
P240
Y248
89
0.40
H203
R249
ZN302
L188
PG0303,
186
L187
A202
0.20
F250
D201
0.10
0.20
T251
F192
P255
¥179
L256
D177
0.00
D260
0
5
10
15
20
25
Epoch
0.00
30
0
5
10
15
20
25
30
Epoch
ZN303
H175
CA305 D182
D185F18
G
MCC
H
F1
MCC
F1
1.00
1.00
0.80
0.80
0.60
0.60
0.40
0.40
0.20
0.20
0.00
5
10
15
25
Epoch
0.00
0
20
30
Epoch
0
5
10
15
20
25
30
| Supplementary Table 1. Adsorption, distribution, metabolism, and excretion properties of compounds | ||||||
|---|---|---|---|---|---|---|
| Solubility Level | BBB level | CYP2D6 | Hepatotoxicity | Absorption Level | PPB Level | |
| CHEMBL344828 PubChem-10764489 | 3 | 3 | 0 | 1 | 0 | 0 |
| CHEMBL2425940 PubChem-73293197 | 3 | 4 | 0 | 1 | 3 | 1 |
| CHEMBL139884 PubChem-10502046 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL381554 PubChem-44409390 | 3 | 4 | 0 | 1 | 0 | 1 |
| CHEMBL2425944 PubChem-73293200 | 4 | 4 | 0 | 1 | 3 | 0 |
| CHEMBL82047 PubChem-10738924 | 3 | 3 | 0 | 0 | 0 | 1 |
| CHEMBL196647 PubChem-44402021 | 4 | 4 | 0 | 1 | 3 | 1 |
| CHEMBL381163 PubChem-44409365 | 3 | 4 | 0 | 0 | 1 | 1 |
| CHEMBL206481 PubChem-44409389 | 3 | 4 | 0 | 1 | 2 | 0 |
| CHEMBL207776 PubChem-21304710 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL138643 PubChem-23523890 | 2 | 4 | 0 | 1 | 3 | 1 |
| CHEMBL382227 PubChem-44411830 | 2 | 4 | 0 | 1 | 1 | 1 |
| CHEMBL419503 PubChem-44325156 | 4 | 4 | 0 | 1 | 3 | 0 |
| CHEMBL252711 PubChem-44445823 | 4 | 4 | 0 | 1 | 3 | 0 |
| CHEMBL433171 PubChem-21130561 | 2 | 4 | 0 | 0 | 0 | 1 |
MMP9 in cancer & computational screening of inhibitors
| CHEMBL1801052 PubChem-9847113 | 2 | 4 | 0 | 1 | 1 | 1 |
| CHEMBL234529 PubChem-25181080 | 3 | 4 | 0 | 0 | 3 | 1 |
| CHEMBL126004 PubChem-10389610 | 3 | 4 | 0 | 1 | 0 | 1 |
| CHEMBL236167 PubChem-23655323 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL429800 PubChem-23656291 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL358812 PubChem-10549612 | 4 | 4 | 0 | 1 | 2 | 0 |
| CHEMBL1801395 PubChem-22707860 | 2 | 4 | 0 | 1 | 1 | 1 |
| CHEMBL1916211 PubChem-57403331 | 2 | 2 | 0 | 1 | 0 | 1 |
| CHEMBL1770697 PubChem-20620715 | 2 | 4 | 0 | 1 | 2 | 1 |
| CHEMBL47728 PubChem-44291532 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL303082 PubChem-44306344 | 2 | 4 | 0 | 1 | 3 | 1 |
| CHEMBL71227 PubChem-44309863 | 2 | 4 | 0 | 1 | 1 | 1 |
| CHEMBL1770712 PubChem-20620688 | 3 | 4 | 0 | 1 | 0 | 1 |
| CHEMBL164980 PubChem-11070343 | 3 | 4 | 0 | 1 | 1 | 0 |
| CHEMBL44045 PubChem-44289352 | 3 | 3 | 0 | 0 | 0 | 1 |
| CHEMBL362797 PubChem-22644895 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL561625 PubChem-45269631 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL35606 | 3 | 4 | 0 | 1 | 0 | 1 |
| CHEMBL2425935 PubChem-73292710 | 3 | 4 | 0 | 1 | 1 | 0 |
| CHEMBL2204827 PubChem-71459505 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL369302 PubChem-22644965 | 3 | 4 | 0 | 1 | 0 | 1 |
| CHEMBL1771223 PubChem-54587429 | 2 | 4 | 0 | 1 | 3 | 1 |
| CHEMBL92778 PubChem-9913479 | 2 | 4 | 0 | 1 | 1 | 1 |
| CHEMBL292671 PubChem-44299758 | 3 | 4 | 0 | 1 | 3 | 1 |
| CHEMBL1771216 PubChem-20620240 | 3 | 4 | 0 | 0 | 3 | 1 |
| CHEMBL1801431 PubChem-10280852 PubChem-46939559 | 2 | 4 | 0 | 1 | 2 | 1 |
| CHEMBL381505 PubChem-44409164 | 3 | 4 | 0 | 1 | 2 | 1 |
| CHEMBL1771222 PubChem-54580544 | 2 | 4 | 0 | 1 | 2 | 1 |
| CHEMBL1771215 PubChem-10483139 | 2 | 4 | 0 | 1 | 3 | 1 |
| CHEMBL1771221 PubChem-54583511 | 2 | 4 | 0 | 1 | 2 | 1 |
| CHEMBL42771 PubChem-44289604 | 3 | 3 | 0 | 1 | 0 | 1 |
| CHEMBL1801398 PubChem-46938727 | 2 | 4 | 0 | 1 | 0 | 1 |
| CHEMBL44250 PubChem-10572544 | 3 | 3 | 0 | 0 | 0 | 1 |
| CHEMBL338007 PubChem-10789711 | 4 | 4 | 0 | 1 | 1 | 0 |
Aqueous-solubility level: 0 (extremely low); 1 (very low, but possible); 2 (low); 3 (good). Blood brain barrier level: 0 (Very high penetrant); 1 (High); 2 (Medium); 3 (Low); 4 (Undefined). Cytochrome P450 2D6 level: 0 (Non-inhibitor); 1 (Inhibitor). Hepatotoxicity: 0 (Nontoxic); 1 (Toxic). Human- intestinal absorption level: 0 (good); 1 (moderate); 2 (poor); 3 (very poor). Plasma protein binding: 0 (Absorbent weak); 1 (Absorbent strong).
| Mouse NTP | Rat NTP | AMES | DTP | |||
|---|---|---|---|---|---|---|
| Female | Male | Female | Male | |||
| CHEMBL344828 PubChem-10764489 | 0 | 0.68 | 0.809 | 0.383 | 1 | 0.978 |
| CHEMBL2425940 PubChem-73293197 | 0 | 0 | 1 | 1 | 1 | 0 |
| CHEMBL139884 PubChem-10502046 | 0 | 0.016 | 0 | 0.701 | 0.396 | 0.999 |
| CHEMBL381554 PubChem-44409390 | 0 | 0 | 0 | 0 | 0.844 | 1 |
| CHEMBL2425944 PubChem-73293200 | 0 | 0.109 | 1 | 1 | 1 | 0.001 |
| CHEMBL82047 PubChem-10738924 | 0 | 0.964 | 0.439 | 1 | 0.99 | 0.11 |
| CHEMBL196647 PubChem-44402021 | 0.59 | 0 | 1 | 1 | 1 | 1 |
| CHEMBL381163 PubChem-44409365 | 0 | 0 | 0.062 | 0.023 | 0.964 | 1 |
| CHEMBL206481 PubChem-44409389 | 0 | 0 | 0 | 0 | 0.623 | 1 |
| CHEMBL207776 PubChem-21304710 | 0 | 0.003 | 0 | 0.898 | 0.498 | 0.946 |
MMP9 in cancer & computational screening of inhibitors
| CHEMBL138643 PubChem-23523890 | 0 | 1 | 1 | 1 | 1 | 1 |
|---|---|---|---|---|---|---|
| CHEMBL382227 PubChem-44411830 | 0.032 | 0.05 | 1 | 1 | 0 | 1 |
| CHEMBL419503 PubChem-44325156 | 0 | 0.004 | 1 | 1 | 0.995 | 1 |
| CHEMBL252711 PubChem-44445823 | 0 | 0 | 1 | 1 | 1 | 1 |
| CHEMBL433171 PubChem-21130561 | 0.898 | 0.005 | 0.953 | 1 | 1 | 1 |
| CHEMBL1801052 PubChem-9847113 | 0.076 | 0.352 | 1 | 1 | 0.424 | 1 |
| CHEMBL234529 PubChem-25181080 | 0.985 | 0.028 | 1 | 0 | 1 | 0.946 |
| CHEMBL126004 PubChem-10389610 | 0 | 0.891 | 1 | 1 | 0.137 | 0.002 |
| CHEMBL236167 PubChem-23655323 | 0.003 | 0.012 | 1 | 1 | 1 | 0.434 |
| CHEMBL429800 PubChem-23656291 | 0.003 | 0.012 | 1 | 1 | 1 | 0.434 |
| CHEMBL358812 PubChem-10549612 | 0 | 0.006 | 1 | 1 | 0.639 | 0.003 |
| CHEMBL1801395 PubChem-22707860 | 0 | 0.002 | 0.999 | 1 | 0.948 | 1 |
| CHEMBL1916211 PubChem-57403331 | 0 | 0 | 0 | 1 | 1 | 0.98 |
| CHEMBL1770697 PubChem-20620715 | 0.006 | 0.821 | 1 | 1 | 0 | 1 |
| CHEMBL47728 PubChem-44291532 | 0 | 0 | 0 | 0 | 0.777 | 0 |
| CHEMBL303082 PubChem-44306344 | 1 | 0.636 | 1 | 1 | 0.999 | 0.521 |
| CHEMBL71227 PubChem-44309863 | 0 | 0.001 | 0.993 | 1 | 0.983 | 1 |
| CHEMBL1770712 PubChem-20620688 | 0.018 | 0.998 | 1 | 1 | 0.92 | 1 |
| CHEMBL164980 PubChem-11070343 | 0 | 1 | 0 | 1 | 1 | 0.273 |
| CHEMBL44045 PubChem-44289352 | 0 | 0.752 | 0.999 | 0.999 | 0 | 1 |
| CHEMBL362797 PubChem-22644895 | 0 | 0.001 | 0 | 0 | 0.071 | 0.649 |
| CHEMBL561625 PubChem-45269631 | 0 | 0.009 | 0.001 | 0.984 | : 1 | 0 |
| CHEMBL35606 | 0 | 0.002 | 0 | 1 | 0.968 | 0 |
| CHEMBL2425935 PubChem-73292710 | ||||||
| CHEMBL2204827 PubChem-71459505 | 0.025 | 0.364 | 0.998 | 1 | 1 | 0.829 |
| CHEMBL369302 PubChem-22644965 | 1 | 0.63 | 1 | 1 | 1 | 0.002 |
| CHEMBL1771223 PubChem-54587429 | 0 | 0.227 | 1 | 1 | 0.374 | 0 |
| CHEMBL92778 PubChem-9913479 | 1 | 0.005 | 1 | 1 | 1 | 1 |
| CHEMBL292671 PubChem-44299758 | 0.987 | 1 | 0.903 | 1 | 1 | 0.002 |
| CHEMBL1771216 PubChem-20620240 | 0 | 0.997 | 1 | 1 | 0.689 | 0 |
| CHEMBL1801431 PubChem-10280852 PubChem-46939559 | 0 | 0.001 | 0.992 | 1 | 0.588 | 1 |
| CHEMBL381505 PubChem-44409164 | 0 | 0.17 | 0 | 0.712 | 0.024 | 0.998 |
| CHEMBL1771222 PubChem-54580544 | 0 | 0.776 | 1 | 1 | 1 | 0.813 |
| CHEMBL1771215 PubChem-10483139 | 0 | 0.24 | 1 | 1 | 0.334 | 0 |
| CHEMBL1771221 PubChem-54583511 | 0 | 0.187 | 1 | 1 | 1 | 0.996 |
| CHEMBL42771 PubChem-44289604 | 0 | 0.875 | 1 | 0.899 | 0.001 | 1 |
| CHEMBL1801398 PubChem-46938727 | 0 | 0.024 | 0.966 | 1 | 0 | 1 |
| CHEMBL44250 PubChem-10572544 | 0.001 | 0.611 | 1 | 1 | 0.002 | 1 |
| CHEMBL338007 PubChem-10789711 | 0 | 0.998 | 1 | 1 | 1 | 1 |
NTP < 0.3 (Non-Carcinogen); > 0.7 (Carcinogen). AMES < 0.3 (Non-Mutagen); > 0.7 (Mutagen). DTP < 0.3 (Nontoxic); >0.7 (Toxic).
A
B
PRO A:421
TYR
420
ARG
PRO
MET :42
A:424
A:415
HIS
:401
GLU
90°
A:416
LEN
H
A:187
ALA
A:417
2
GLY
A:186
THR
A:426
188
LEU
A:418
VAL
ALA
GLN
A:189
A:402
A:398
LEU
A:397
PRO
A:430
Interactions
van der Waals
Pi-Pi Stacked
Conventional Hydrogen Bond
Pi-Pi T-shaped
Carbon Hydrogen Bond
Alkyl
Pi-Sulfur
Pi-Alkyl
C
D
HIS
A:230
HIS
A:236
HIS
A:190
MET
A:247
PRO
A:240
ALA
TYR
90°
LEU
A:189
A:243
ARG
THR
A:249
A:251
2
LEU
H
A:187
ALA
H
A:242
GLY
PRO
A:186
A:246
LEU
4:226
TYR
A:248
A:188
VAL
LEU
GLU
A:223
A:ZZZ
A:241
Interactions
van der Waals
Halogen (Fluorine)
Conventional Hydrogen Bond
Pi-Sulfur
Carbon Hydrogen Bond
Pi-Alkyl
E
F
GUN-227
TYR 245
MET-422