Materials Cloud, a platform for open computational science

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts 1) archival and dissemination services for raw and curated data, together with their provenance graph, 2) modelling services and virtual machines, 3) tools for data analytics, and pre-/post-processing, and 4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of the FAIR principles that extends to computational workflows. Materials Cloud leverages the AiiDA framework to record the provenance of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow to retrace and reproduce any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain.Comment: 19 pages, 8 figure

The rovibrational spectrum of BeH, MgH and CaH at high temperatures in the X 2Σ+X\,{}^2\Sigma^+ state: a theoretical study

Accurate line lists for three molecules, BeH, MgH and CaH, in their ground electronic states are presented. These line lists are suitable for temperatures relevant to exoplanetary atmospheres and cool stars (up to 2000K). A combination of empirical and \textit{ab initio} methods is used. The rovibrational energy levels of BeH, MgH and CaH are computed using the programs Level and DPotFit in conjunction with `spectroscopic' potential energy curves (PECs). The PEC of BeH is taken from the literature, while the PECs of CaH and MgH are generated by fitting to the experimental transition energy levels. Both spin-rotation interactions (except for BeH, for which it is negligible) and non-adiabatic corrections are explicitly taken into account. Accurate line intensities are generated using newly computed \textit{ab initio} dipole moment curves for each molecule using high levels of theory. Full line lists of rotation-vibration transitions for $^9$BeH, $^{24}$MgH, $^{25}$MgH, $^{26}$MgH and $^{40}$CaH are made available in an electronic form as supplementary data to this article and at \url{www.exomol.com}.Comment: MNRAS (in press

Drug-perturbation-based stratification of blood cancer

As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non-BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.Peer reviewe

Complementary α-arrestin - Rsp5 ubiquitin ligase complexes control selective nutrient transporter endocytosis in response to amino acid availability

How cells adjust transport across their membranes is incompletely understood. Previously, we have shown that S.cerevisiae broadly re-configures the nutrient transporters at the plasma membrane in response to amino acid availability, through selective endocytosis of sugar- and amino acid transporters (AATs) (Müller et al., 2015). A genome-wide screen now revealed that Art2/Ecm21, a member of the α-arrestin family of Rsp5 ubiquitin ligase adaptors, is required for the simultaneous endocytosis of four AATs and induced during starvation by the general amino acid control pathway. Art2 uses a basic patch to recognize C-terminal acidic sorting motifs in these AATs and instructs Rsp5 to ubiquitinate proximal lysine residues. In response to amino acid excess, Rsp5 instead uses TORC1-activated Art1 to detect N-terminal acidic sorting motifs within the same AATs, which initiates exclusive substrate-induced endocytosis of individual AATs. Thus, amino acid availability activates complementary α-arrestin-Rsp5-complexes to control selective endocytosis for nutrient acquisition

Virtual computational chemistry teaching laboratories – hands-on at a distance

The COVID-19 pandemic disrupted chemistry teaching practices globally as many courses were forced online necessitating adaptation to the digital platform. The biggest impact was to the practical component of the chemistry curriculum – the so-called wet lab. Naively, it would be thought that computer-based teaching labs would have little problem in making the move. However, this is not the case as there are many unrecognised differences between delivering computer-based teaching in-person and virtually: software issues, technology and classroom management. Consequently, relatively few “hands-on” computational chemistry teaching laboratories are delivered online. In this paper we describe these issues in more detail and how they can be addressed, drawing on our experience in delivering a third-year computational chemistry course as well as remote hands-on workshops for the Virtual Winter School on Computational Chemistry and the European BIG-MAP project

Virtual Computational Chemistry Teaching Laboratories—Hands-On at a Distance

The COVID-19 pandemic disrupted chemistry teaching practices globally as many courses were forced online, necessitating adaptation to the digital platform. The biggest impact was to the practical component of the chemistry curriculum-the so-called wet lab. Naively, it would be thought that computer-based teaching laboratories would have little problem in making the move. However, this is not the case as there are many unrecognized differences between delivering computer-based teaching in-person and virtually: software issues, technology, and classroom management. Consequently, relatively few “hands-on” computational chemistry teaching laboratories are delivered online. In this paper, we describe these issues in more detail and how they can be addressed, drawing on our experience in delivering a thirdyear computational chemistry course as well as remote hands-on workshops for the Virtual Winter School on Computational Chemistry and the European BIG-MAP project

Materials Cloud, a platform for open computational science.

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and dissemination services for raw and curated data, together with their provenance graph, (2) modelling services and virtual machines, (3) tools for data analytics, and pre-/post-processing, and (4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow retracing and reproducing any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain