Learning single-cell perturbation responses using neural optimal transport.

Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-ß and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.

3DReact: Geometric Deep Learning for Chemical Reactions.

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

COSIFER: a Python package for the consensus inference of molecular interaction networks.

SUMMARY: The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. AVAILABILITY AND IMPLEMENTATION: COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Reproducible image-based profiling with Pycytominer.

Technological advances in high-throughput microscopy have facilitated the acquisition of cell images at a rapid pace, and data pipelines can now extract and process thousands of image-based features from microscopy images. These features represent valuable single-cell phenotypes that contain information about cell state and biological processes. The use of these features for biological discovery is known as image-based or morphological profiling. However, these raw features need processing before use and image-based profiling lacks scalable and reproducible open-source software. Inconsistent processing across studies makes it difficult to compare datasets and processing steps, further delaying the development of optimal pipelines, methods, and analyses. To address these issues, we present Pycytominer, an open-source software package with a vibrant community that establishes an image-based profiling standard. Pycytominer has a simple, user-friendly Application Programming Interface (API) that implements image-based profiling functions for processing high-dimensional morphological features extracted from microscopy images of cells. Establishing Pycytominer as a standard image-based profiling toolkit ensures consistent data processing pipelines with data provenance, therefore minimizing potential inconsistencies and enabling researchers to confidently derive accurate conclusions and discover novel insights from their data, thus driving progress in our field.

Backbone circularization of Bacillus subtilis family 11 xylanase increases its thermostability and its resistance against aggregation.

The activity of proteins is dictated by their three-dimensional structure, the native state, and is influenced by their ability to remain in or return to the folded native state under physiological conditions. Backbone circularization is thought to increase protein stability by decreasing the conformational entropy in the unfolded state. A positive effect of circularization on stability has been shown for several proteins. Here, we report the development of a cloning standard that facilitates implementing the SICLOPPS technology to circularize proteins of interest using split inteins. To exemplify the usage of the cloning standard we constructed two circularization vectors based on the Npu DnaE and gp41-1 split inteins, respectively. We use these vectors to overexpress in Escherichia coli circular forms of the Bacillus subtilis enzyme family 11 xylanase that differ in the identity and number of additional amino acids used for circularization (exteins). We found that the variant circularized with only one additional serine has increased thermostability of 7 °C compared to native xylanase. The variant circularized with six additional amino acids has only a mild increase in thermostability compared to the corresponding exteins-bearing linear xylanase, but is less stable than native xylanase. However, this circular xylanase retains more than 50% of its activity after heat shock at elevated temperatures, while native xylanase and the corresponding exteins-bearing linear xylanase are largely inactivated. We correlate this residual activity to the fewer protein aggregates found in the test tubes of circular xylanase after heat shock, suggesting that circularization protects the protein from aggregation under these conditions. Taken together, these data indicate that backbone circularization has a positive effect on xylanase and can lead to increased thermostability, provided the appropriate exteins are selected. We believe that our cloning standard and circularization vectors will facilitate testing the effects of circularization on other proteins.