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Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations have become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, and each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simu...

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.

The decreasing cost of genome sequencing technology has lead to an explosion of information about which mutations are frequently observed in cancer, demonstrating an important role in cancer progression for kinase domain mutations. Many therapies have been developed that target mutations in kinase proteins that lead to constitutive activation. However, a growing body of evidence points to the serious dangers of many kinase ATP competitive inhibitors leading to paradoxical activation in non-constitutively active proteins. The large number of observed mutations and the critical need to only treat patients harboring activating mutations with targeted therapies raises the question of how to classify the thousands of mutations that have been observed. We start with an in depth look at the state of knowledge of the distribution and effects of kinase mutations. We then report on computational methods to understand and predict the effects of kinase domain mutations. Using molecular dynamics simulations of mutant kinases, we show that there is a switch-like network of labile hydrogen bonds that are often perturbed in activating mutations. This is paired with a description of a software platform that has been developed to streamline the execution and analysis of molecular dynamics simulations. We conclude by examining a machine learning method to demonstrate what kinds information derived from protein sequence alone have the most value in distinguishing activating and non-activating mutations.