The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.

Paracrine factors can induce cardiac regeneration and repair post myocardial infarction by stimulating proliferation of cardiac cells and inducing the anti-fibrotic, antiapoptotic, and immunomodulatory effects of angiogenesis. Here, we screened a human secretome library, consisting of 923 growth factors, cytokines, and proteins with unknown function, in a phenotypic screen with human cardiac progenitor cells. The primary readout in the screen was proliferation measured by nuclear count. From this screen, we identified FGF1, FGF4, FGF9, FGF16, FGF18, and seven additional proteins that induce proliferation of cardiac progenitor cells. FGF9 and FGF16 belong to the same FGF subfamily, share high sequence identity, and are described to have similar receptor preferences. Interestingly, FGF16 was shown to be specific for proliferation of cardiac progenitor cells, whereas FGF9 also proliferated human cardiac fibroblasts. Biosensor analysis of receptor preferences and quantification of receptor abundances suggested that FGF16 and FGF9 bind to different FGF receptors on the cardiac progenitor cells and cardiac fibroblasts. FGF16 also proliferated naïve cardiac progenitor cells isolated from mouse heart and human cardiomyocytes derived from induced pluripotent cells. Taken together, the data suggest that FGF16 could be a suitable paracrine factor to induce cardiac regeneration and repair.

Secreted proteins and their cognate plasma membrane receptors regulate human physiology by transducing signals from the extracellular environment into cells resulting in different cellular phenotypes. Systematic use of secretome proteins in assays enables discovery of novel biology and signaling pathways. Several secretome-based phenotypic screening platforms have been described in the literature and shown to facilitate target identification in drug discovery. In this review, we summarize the current status of secretome-based screening. This includes annotation, production, quality control, and sample management of secretome libraries, as well as how secretome libraries have been applied to discover novel target biology using different disease-relevant cell-based assays. A workflow for secretome-based screening is shared based on the AstraZeneca experience. The secretome library offers several advantages compared with other libraries used for target discovery: (1) screening using a secretome library directly identifies the active protein and, in many cases, its cognate receptor, enabling a rapid understanding of the disease pathway and subsequent formation of target hypotheses for drug discovery; (2) the secretome library covers significant areas of biological signaling space, although the size of this library is small; (3) secretome proteins can be added directly to cells without additional manipulation. These factors make the secretome library ideal for testing in physiologically relevant cell types, and therefore it represents an attractive approach to phenotypic target discovery.

Production and secretion of pro-metastatic proteins is a feature of many tumor cells. The FAM3C interleukin-like epithelial-to-mesenchymal-transition (EMT) inducer (ILEI) has been shown to be strongly up-regulated in several cancers and to be essential for tumor formation and metastasis in epithelial cells, correlating with a significant decrease in overall survival in colon and breast cancer patients. ILEI has been seen to interact with the γ-secretase presenilin 1 subunit (PS1). However, not much is known about the mechanism-of-action or the detailed ILEI structure. We present here the crystal structures of FAM3C ILEI and show that it exists as monomers but also as covalent dimers. The observed ILEI β-β-α fold confirmed previous indications that the FAM3C proteins do not form classical four-helix-bundle structures as was initially predicted. This provides the first experimental evidence that the interleukin-like EMT inducers are not evolutionarily related to the interleukins. However, more surprisingly, the ILEI dimer structure was found to feature a trans-linked domain swap, converting an intramolecular disulfide to intermolecular. Interestingly, dimeric but not monomeric ILEI was subsequently found to cause a dose-dependent increase in EpRas cell invasiveness comparable with TGF-β, indicating that the dimer might be the active ILEI species. This is in line with a parallel study showing that covalent oligomerization of ILEI is essential for EMT and tumor progression in vivo. The structures and the activity data give some first insight into the relationship between dimerization and ILEI function as well as indicate an intriguing link between ILEI, the PS1-protease, TGF-β, and the TGF-β receptor 1.

Regulatory T cells (Tregs) are the key cells regulating peripheral autoreactive T lymphocytes. Tregs exert their function by suppressing effector T cells. Tregs have been shown to play essential roles in the control of a variety of physiological and pathological immune responses. However, Tregs are unstable and can lose the expression of FOXP3 and suppressive functions as a consequence of outer stimuli. Available literature suggests that secreted proteins regulate Treg functional states, such as differentiation, proliferation and suppressive function. Identification of secreted proteins that affect Treg cell function are highly interesting for both therapeutic and diagnostic purposes in either hyperactive or immunosuppressed populations. Here, we report a phenotypic screening of a human secretome library in human Treg cells utilising a high throughput flow cytometry technology. Screening a library of 575 secreted proteins allowed us to identify proteins stabilising or destabilising the Treg phenotype as suggested by changes in expression of Treg marker proteins FOXP3 and/or CTLA4. Four proteins including GDF-7, IL-10, PAP and IFNα-7 were identified as positive regulators that increased FOXP3 and/or CTLA4 expression. PAP is a phosphatase. A catalytic-dead version of the protein did not induce an increase in FOXP3 expression. Ten interferon proteins were identified as negative regulators that reduced the expression of both CTLA4 and FOXP3, without affecting cell viability. A transcriptomics analysis supported the differential effect on Tregs of IFNα-7 versus other IFNα proteins, indicating differences in JAK/STAT signaling. A conformational model experiment confirmed a tenfold reduction in IFNAR-mediated ISG transcription for IFNα-7 compared to IFNα-10. This further strengthened the theory of a shift in downstream messaging upon external stimulation. As a summary, we have identified four positive regulators of FOXP3 and/or CTLA4 expression. Further exploration of these Treg modulators and their method of action has the potential to aid the discovery of novel therapies for both autoimmune and infectious diseases as well as for cancer.