Andrio, P. | Search bioRxiv

bioRxiv2022

Prediction Of The Impact Of Genetic Variability On Drug Sensitivity For Clinically Relevant EGFR Mutations

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.

biochemistry↗

bioRxiv2021

Enabling the execution of large scale workflows for molecular dynamics simulations

The usage of workflows has led to progress in many fields of science, where the need to process large amounts of data is coupled with difficulty in accessing and efficiently using High Performance Computing platforms. On the one hand, scientists are focused on their problem and concerned with how to process their data. On top of that, the applications typically have different parts and use different tools for each part, thus complicating the distribution and the reproducibility of the simulations. On the other hand, computer scientists concentrate on how to develop frameworks for the deployment of workflows on HPC or HTC resources; often providing separate solutions for the computational aspects and the data analytic ones. In this paper we present an approach to support biomolecular researchers in the development of complex workflows that i) allow them to compose pipelines of individual simulations built from different tools and interconnected by data dependencies, ii) run them seamlessly on different computational platforms, and iii) scale them up to the large number of cores provided by modern supercomputing infrastructures. Our approach is based on the orchestration of computational building blocks for Molecular Dynamics simulations through an efficient workflow management system that has already been adopted in many scientific fields to run applications on multitudes of computing backends. Results demonstrate the validity of the proposed solution through the execution of massively parallel runs in a supercomputer facility.

molecular biology↗