Background: The epidermal growth factor receptor (EGFR) is a member of the ErbB family that is involved in a number of processes responsible for cancer development and progression such as angiogenesis, apoptosis, cell...
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Background: The epidermal growth factor receptor (EGFR) is a member of the ErbB family that is involved in a number of processes responsible for cancer development and progression such as angiogenesis, apoptosis, cell proliferation and metastatic spread. Malfunction in activation of protein tyrosine kinases has been shown to result in uncontrolled cell growth. The EGFR TK domain has been identified as suitable target in cancer therapy and tyrosine kinase inhibitors such as erlotinib have been used for treatment of cancer. Mutations in the region of the EGFR gene encoding the tyrosine kinase (TK) domain causes altered responses to EGFR TK inhibitors (TKI). In this paper we perform molecular dynamics simulations and PCA analysis on wild-type and mutant (T854A) structures to gain insight into the structural changes observed in the target protein upon mutation. We also report two novel inhibitors identified by combined approach of QSAR model development. Results: The wild-type and mutant structure was observed to be stable for 26 ns and 24 ns respectively. In PCA analysis, the mutant structure proved to be more flexible than wild-type. We developed a 3D-QSAR model using 38 thiazolyl-pyrazoline compounds which was later used for prediction of inhibitory activity of natural compounds of ZINC library. The 3D-QSAR model was proved to be robust by the statistical parameters such as r(2) (0.9751), q(2)(0.9491) and pred_r(2)(0.9525). Conclusion: Analysis of molecular dynamics simulations results indicate stability loss and increased flexibility in the mutant structure. This flexibility results in structural changes which render the mutant protein drug resistant against erlotinib. We report two novel compounds having high predicted inhibitory activity to EGFR TK domain with both wild-type and mutant structure.
Background: Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitativestructureactivityrelationship (QSAR) modeling. QSAR...
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Background: Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitativestructureactivityrelationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results: This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions: AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of hig
Background: Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway pres...
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Background: Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general. Results: This paper describes a methodology for predicting novel/potent inhibitors against DHDPS. Here, quantitativestructureactivityrelationship (QSAR) models were trained and tested on experimentally verified 23 enzyme's inhibitors having inhibitory value (K-i) in the range of 0.005-22(mM). These inhibitors were docked at the active site of DHDPS (1YXD) using AutoDock software, which resulted in 11 energy-based descriptors. For QSAR modeling, Multiple Linear Regression (MLR) model was engendered using best four energy-based descriptors yielding correlation values R/q(2) of 0.82/0.67 and MAE of 2.43. Additionally, Support Vector Machine (SVM) based model was developed with three crucial descriptors selected using F-stepping remove-one approach, which enhanced the performance by attaining R/q(2) values of 0.93/0.80 and MAE of 1.89. To validate the performance of QSAR models, external cross-validation procedure was adopted which accomplished high training/testing correlation values (q(2)/r(2)) in the range of 0.78-0.83/0.93-0.95. Conclusions: Our results suggests that ligand-receptor binding interactions for DHDPS employing QSAR modeling seems to be a promising approach for prediction of antibacterial agents. To serve the experimentalist to develop novel/potent inhibitors, a webserver "KiDoQ" has been developed http://crdd.o
The article reports that EDETOX is a 3-year project, which was started in January 2001, that will generate new data on dermal absorption of chemicals. The consortium comprises 12 participants from seven European Union...
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The article reports that EDETOX is a 3-year project, which was started in January 2001, that will generate new data on dermal absorption of chemicals. The consortium comprises 12 participants from seven European Union (EU) member states. EDETOX member laboratories all participated in the percutaneous penetration sub-group of the Dermal Exposure Network coordinated by the University of Surrey. Collaborations were established that resulted in a successful application to the EU. The aims of the project are to produce new knowledge that will standardise in vitro systems for predicting percutaneous penetration and compare these with relevant in vivo studies. It will use the expertise gathered in the investigations with these systems to evaluate and develop predictive computational models of skin penetration and disposition of health-related chemicals.
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