Most efforts to estimate the reproducibility of published findings have focused on specific areas of research, even though science is usually assessed and funded on a regional or national basis. Here we describe a project to assess the reproducibility of findings in biomedical science published by researchers based in Brazil. The Brazilian Reproducibility Initiative is a systematic, multi-center effort to repeat between 60 and 100 experiments: the project will focus on a set of common laboratory methods, repeating each experiment in three different laboratories. The results, due in 2021, will allow us to estimate the level of reproducibility of biomedical science in Brazil, and to investigate what the published literature can tell us about the reproducibility of research in a given area.
All data cited in the article is available at the project's site at the Open Science Framework (https://osf.io/6av7k/).
The project's funder (Instituto Serrapilheira) made suggestions on the study design, but had no role in data collection and interpretation, or in the decision to submit the work for publication. K. N. and A.P.W.S. are supported by post-doctoral scholarships within this project. C.F.D.C. is supported by a PhD scholarship from CNPq.
© 2019, Amaral et al.
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Nature has inspired the design of improved inhibitors for cancer-causing proteins.
Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.