In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.
Mutations in the kinase domain of the Epidermal Growth Factor Receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients under chemotherapy treatment based on kinase inhibitors use. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and to design new drugs effective against resistant variants. To this end, we have explored a variety of in silico methods, from sequence-based to ‘state-of-the-art’ atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears and what will be the impact in drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to produce alterations in drug activity and can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on non-equilibrium alchemical free energy calculations show predictive power. The integration of these ‘state-of-the-art’ methods in a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.
Mutations in the kinase domain of the epidermal growth
factor receptor
(EGFR) can be drivers of cancer and also trigger drug resistance in
patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug
sensitivity would help to optimize chemotherapy and design new drugs
that are effective against resistant variants before they emerge in
clinical trials. To this end, we explored a variety of in
silico methods, from sequence-based to “state-of-the-art”
atomistic simulations. We did not find any sequence signal that can
provide clues on when a drug-related mutation appears or the impact
of such mutations on drug activity. Low-level simulation methods provide
limited qualitative information on regions where mutations are likely
to cause alterations in drug activity, and they can predict around
70% of the impact of mutations on drug efficiency. High-level simulations
based on nonequilibrium alchemical free energy calculations show predictive
power. The integration of these “state-of-the-art” methods
into a workflow implementing an interface for parallel distribution
of the calculations allows its automatic and high-throughput use,
even for researchers with moderate experience in molecular simulations.
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