scholarly journals Proteomic and molecular dynamic investigations of PTM-induced structural fluctuations in breast and ovarian cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dmitry Tikhonov ◽  
Liudmila Kulikova ◽  
Arthur T. Kopylov ◽  
Vladimir Rudnev ◽  
Alexander Stepanov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Conformational Changes ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Proteomic Profiling ◽  
Dynamics Modeling ◽  
Protein Activity ◽  
Molecular Dynamics Modeling ◽  
Elimination Clearance

AbstractPost-translational processing leads to conformational changes in protein structure that modulate molecular functions and change the signature of metabolic transformations and immune responses. Some post-translational modifications (PTMs), such as phosphorylation and acetylation, are strongly related to oncogenic processes and malignancy. This study investigated a PTM pattern in patients with gender-specific ovarian or breast cancer. Proteomic profiling and analysis of cancer-specific PTM patterns were performed using high-resolution UPLC-MS/MS. Structural analysis, topology, and stability of PTMs associated with sex-specific cancers were analyzed using molecular dynamics modeling. We identified highly specific PTMs, of which 12 modified peptides from eight distinct proteins derived from patients with ovarian cancer and 6 peptides of three proteins favored patients from the group with breast cancer. We found that all defined PTMs were localized in the compact and stable structural motifs exposed outside the solvent environment. PTMs increase the solvent-accessible surface area of the modified moiety and its active environment. The observed conformational fluctuations are still inadequate to activate the structural degradation and enhance protein elimination/clearance; however, it is sufficient for the significant modulation of protein activity.

scholarly journals Structural Changes in Proteins With Post-translational Modifications in Female Oncopathologies

2021 ◽  
Author(s):  
Dmitry Tikhonov ◽  
Liudmila Kulikova ◽  
Arthur T. Kopylov ◽  
Vladimir Rudnev ◽  
Alexander Stepanov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Conformational Changes ◽  
Structural Changes ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Proteomic Profiling ◽  
Dynamics Modeling ◽  
Protein Activity ◽  
Post Translational Modifications ◽  
Elimination Clearance

Abstract Post-translational processing leads to conformational changes in protein structure that modulate molecular functions and change the signature of metabolic transformations and immune responses. Some post-translational modifications (PTMs), such as phosphorylation and acetylation, are strongly related to oncogenic processes and malignancy. This study investigated a PTM pattern in patients with gender-specific ovarian or breast cancer. Proteomic profiling and analysis of cancer-specific PTM patterns were performed using high-resolution UPLC-MS/MS. Structural analysis, topology, and stability of PTMs associated with sex-specific cancers were analyzed using molecular dynamics modeling. We identified highly specific PTMs, of which 12 modified peptides from eight distinct proteins derived from patients with ovarian cancer and 6 peptides of three proteins favored patients from the group with breast cancer. We found that all defined PTMs were localized in the compact and stable structural motifs exposed outside the solvent environment. PTMs increase the solvent-accessible surface area of the modified moiety and its active environment. The observed conformational changes are still inadequate to activate the structural degradation and enhance protein elimination/clearance; however, it is sufficient for the significant modulation of protein activity.

Activity Based Proteomic Profiling of Lysophosphatidic Acid Treated Cancer Cells

2008 ◽  
Vol 4 ◽  
Author(s):  
Aryeh B Hillman
Keyword(s):  
Ovarian Cancer ◽  
Lysophosphatidic Acid ◽  
Ovarian Cancer Cell Line ◽  
Acyl Chain ◽  
Cancer Cell Line ◽  
Protein Profiling ◽  
Proteomic Profiling ◽  
Protein Activity ◽  
Acyl Chain Length ◽  
New Strategy

Ovarian cancer is a debilitating disease lacking effective treatments. A key feature of the disease is elevated levels of the mitogenic lipid lysophosphatidic acid (LPA) found in the ascities fluid surrounding tumors. LPA evokes a wide array of pro-tumorgenic effects in cells and was recently shown to stimulate the expression of a cancer-associated protease, urokinase type plasminogen activator (uPA). To discern whether LPA treatment resulted in active uPA, I applied a novel proteomic technique, activity-based protein profiling (ABPP), that specifically monitors the amount of protein activity rather than abundance. I utilized ABPP to examine the effect of the bioactive lipid LPA on uPA in a human ovarian cancer cell line SKOV-3. To achieve this I first developed a new strategy for analysis of secreted proteins and then determined that treatment of SKOV-3 cells with LPA does indeed result in increases of active uPA. In addition to this finding, I also detected elevated uPA activity upon treatment of structurally distinct forms of LPA that vary in acyl chain length. This finding has not previously been reported and demonstrates the power of ABPP to identify changes in the functional state of low abundance enzyme activities.

scholarly journals Understanding Calcium-Dependent Conformational Changes in S100A1 Protein: A Combination of Molecular Dynamics and Gene Expression Study in Skeletal Muscle

Cells ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 181 ◽  
Author(s):  
Navaneet Chaturvedi ◽  
Khurshid Ahmad ◽  
Brijesh Singh Yadav ◽  
Eun Ju Lee ◽  
Subash Chandra Sonkar ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Surface Area ◽  
Conformational Changes ◽  
Protein A ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Myoblast Differentiation ◽  
Calcium Dependent ◽  
Accessible Surface ◽  
Solvent Accessible Surface

The S100A1 protein, involved in various physiological activities through the binding of calcium ions (Ca2+), participates in several protein-protein interaction (PPI) events after Ca2+-dependent activation. The present work investigates Ca2+-dependent conformational changes in the helix-EF hand-helix using the molecular dynamics (MD) simulation approach that facilitates the understanding of Ca2+-dependent structural and dynamic distinctions between the apo and holo forms of the protein. Furthermore, the process of ion binding by inserting Ca2+ into the bulk of the apo structure was simulated by molecular dynamics. Expectations of the simulation were demonstrated using cluster analysis and a variety of structural metrics, such as interhelical angle estimation, solvent accessible surface area, hydrogen bond analysis, and contact analysis. Ca2+ triggered a rise in the interhelical angles of S100A1 on the binding site and solvent accessible surface area. Significant configurational regulations were observed in the holo protein. The findings would contribute to understanding the molecular basis of the association of Ca2+ with the S100A1 protein, which may be an appropriate study to understand the Ca2+-mediated conformational changes in the protein target. In addition, we investigated the expression profile of S100A1 in myoblast differentiation and muscle regeneration. These data showed that S100A1 is expressed in skeletal muscles. However, the expression decreases with time during the process of myoblast differentiation.

Chemosensitivity testing in Ovarian Cancer and Breast Cancer

2004 ◽  
Vol 112 (S 1) ◽  
Author(s):  
S Albrecht ◽  
T Köppen ◽  
K Deutschmann ◽  
T Zimmermann ◽  
R Findeisen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Chemosensitivity Testing

Breast Cancer and Ovarian Cancer Genetics

2005 ◽  
Vol 15 (5) ◽  
pp. 533-545 ◽  
Author(s):  
Richard F. Edlich ◽  
Kathryne L. Winters ◽  
Kant Y. Lin
Keyword(s):  
Breast Cancer ◽  
Ovarian Cancer ◽  
Cancer Genetics

Application of the ESMACS Binding Free Energy Protocol to a Highly Varied Ligand Dataset: Lactate Dehydogenase A

2019 ◽  
Author(s):  
David Wright ◽  
Fouad Husseini ◽  
Shunzhou Wan ◽  
Christophe Meyer ◽  
Herman Van Vlijmen ◽  
...  
Keyword(s):  
Free Energy ◽  
Surface Area ◽  
Binding Free Energy ◽  
Normal Mode Analysis ◽  
Binding Mode ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Mode Analysis ◽  
Energy Calculation ◽  
Accessible Surface

<div>Here, we evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) for use in fragment based drug design scenarios. ESMACS is designed to generate reproducible binding affinity predictions from the widely used molecular mechanics Poisson-Boltzmann surface area (MMPBSA) approach. We study ligands designed to target two binding pockets in the lactate dehydogenase A target protein, which vary in size, charge and binding mode. When comparing to experimental results, we obtain excellent statistical rankings across this highly diverse set of ligands. In addition, we investigate three approaches to account for entropic contributions not captured by standard MMPBSA calculations: (1) normal mode analysis, (2) weighted solvent accessible surface area (WSAS) and (3) variational entropy. </div>

Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

2020 ◽  
Vol 4 (5) ◽  
pp. 805-812
Author(s):  
Riska Chairunisa ◽  
Adiwijaya ◽  
Widi Astuti
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Ovarian Cancer ◽  
Random Forest ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Discrete Wavelet ◽  
Prostate Tumors ◽  
Cancer Data ◽  
Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.  

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