The including protein-tyrosine phosphatase 1B (PTP1B), Cytochrome

The purpose of this proposal is to use integrated approach of GROMACS58, FOLD-X59-60 ,and Friend software with StSNP server61-62 to investigate the potency and selectivity of selected drugs in
diabetes and breast cancer integrative treatment. The potency depends on the
binding affinity and drug efficacy, which quantify how well a drug can bind a
receptor and initiate a response at the molecular, cellular, tissue or system
level; the selectivity refers to a drug’s ability to preferentially bind a
particular target and initiate a specific response. Both properties are related
to the structural and energetic specificity of drug binding to given receptors.
Molecular dynamics simulations will be used to compute drug potency and
selectivity to appropriate diabetes and breast cancer protein targets. The principal
goal is to provide a molecular level modeling method to assist for an optimised
treatment of individual patients. The approach is able to provide a rapid,
robust, and reliable prediction for drug potent and selectivity. The method
will be facilitated by developing a workflow for building, simulating and
analysing In silico models distributed over high performance computing and secure data
storage resources in Carnegie Mellon University-Qatar.

 

The study aims to:

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–      Acquire genetic information for one key drug target,
angiotensin-converting enzyme (ACE), in diabetes patients of Qatari nationals
suffering with breast cancer;

 

–      Investigate mutation effects on the drug potency and selectivity, in
which mutation status will be obtained from the sequence study;

 

–      Investigate drug potency and selectivity for other important molecular
targets associated with diabetes and breast cancer, including protein-tyrosine
phosphatase 1B (PTP1B), Cytochrome P450 2C9 (CYP2C9), p62/Sequestosome1
(SQSTM1), and peroxisome proliferator-activated receptors (PPARs);

 

–      Furnish understanding of the mechanisms of drug-protein interactions
involved in diabetes and breast cancer development and treatment, and link
these to patient specific molecular level simulation.

 

To achieve these aims, we will:

 

(1)  Perform variant calling (Next Generation Sequencing) NGS computational analyses
for patient specific nature of our proposal. In this study, we will collect genomic
data of 32 subjects from Qatar Biobank to perform variant calling analysis. Significant
mutant alleles (SNPs) will be identified which will be subjected to molecular modeling
and molecular dynamics study using GROMACS58, FOLD-X59-60, and Friend software with StSNP server61-62 to investigate the effect of drugs on the SNP mapped protein targets.
Comparative analyses between the wild-type and mutant protein-ligand targets
will be used to find out the potential drug for respective receptor targets by
ranking the drugs on binding affinity score.

 

(2) Use the established, effective and flexible
e-infrastructure on which molecular level modeling and simulation can run easily
on distributed computational resources at CMU-Q, to handle the large scale
computations needed for reproducible results.

 

The molecular approach we will use in this study has been
pharmaceutically and/or clinically adapted in a number of studies, optimized
over the years in the field of interface binding affinity calculation63. There could be two potential applications of our approach in the
improvement of drug potency and selectivity for (i) drug discovery within the
pharmaceutical industry, and (ii) clinical decision support when treating
patients64. For the application in the personalized medicine treatment, we
believe that with the results to be obtained from the project could place our
simulation model among the high Technology Readiness Levels (TRL). A proven
methodology this advance would bring more inter and intra-scientific
collaborations to the research community of Qatar, which in future would lead
to more advance computational studies. Moreover, our molecular approach of
using GROMACS58, FOLD-X59-60, and Friend with StSNP server61-62 integrated with python automated tool to predict the
binding affinity of drugs with their respective targets would serve as a novel
step in developing an e-infrastructure methodology specifically to be used by
the research community of Qatar in studying aggressive and progressive diseases
such as breast cancer and diabetes.